academic achievement literature review

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Research Article

Academic achievement prediction in higher education through interpretable modeling

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Foreign Languages, Wuhan Business University, Wuhan, Hubei, People’s Republic of China

Roles Investigation, Software, Writing – review & editing

* E-mail: [email protected]

• Sixuan Wang, 

• Published: September 5, 2024
• https://doi.org/10.1371/journal.pone.0309838
• Reader Comments

Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance.

Citation: Wang S, Luo B (2024) Academic achievement prediction in higher education through interpretable modeling. PLoS ONE 19(9): e0309838. https://doi.org/10.1371/journal.pone.0309838

Editor: Shahid Akbar, Abdul Wali Khan University Mardan, PAKISTAN

Received: May 30, 2024; Accepted: August 20, 2024; Published: September 5, 2024

Copyright: © 2024 Wang, Luo. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: Fund recepient:Sixuan Wang Funder name: Hubei Provincial Department of Education Grant No: 2022GB087 Project name: A Study on the Curriculum Connection between College Japanese and High School Japanese from the Perspective of Core Literacy. https://jyt.hubei.gov.cn/ The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Context and motivation.

Academic achievement is of paramount importance in educational contexts, serving as a key indicator of both learning ability and the effectiveness of school administration and teaching standards [ 1 ]. The prediction of academic achievement is a continuously evolving topic in educational management. The integration of predictive models in education empowers educators to make well-informed choices, offer specific support, and enhance teaching strategies, thereby improving student learning outcomes [ 2 ].

Previous research on achievement prediction primarily utilized statistical analysis methods to process data and forecast outcomes, with data mainly derived from educational management systems, student identification cards, or surveys [ 3 ]. ML techniques, known for their ability to tackle complex, nonlinear problems without presuppositions, are adept at identifying connections between various parameters [ 4 ]. The state-of-the-art ML techniques for prediction [ 5 ] include K-Nearest Neighbors (KNN), Decision Trees, Random Forests (RF), Support Vector Machines (SVM), Neural Networks, and Naive Bayes. Recent scholarly efforts, both domestically and internationally, have been geared towards increasing the precision of student achievement predictions through technological innovations in algorithms [ 6 – 8 ].

Despite these developments, challenges remain in the domain of achievement prediction. A primary issue is the limited alignment between the outcomes produced by ML algorithms and the foundational principles of education and instruction, leading to hesitancy among educators in relying on these models. Additionally, there is a gap in thorough data analysis, examination of relationships, and investigation into variables that impact student academic performance patterns.

Contribution of the study

In addressing these challenges, our study delivers distinctive contributions to the field of interpretable machine learning within the context of higher education. We delineate these contributions as follows:

• Theoretical contribution: this study introduces ML models coupled with game theory-based SHAP analysis which aims to develop and validate the XGB-SHAP model, a novel approach for interpreting machine learning-based predictions of student achievement, and explore its efficacy across various teaching modalities.
• Practical contributions: It evaluates the significance of different indicators and their positive or negative impacts on prediction outcomes, thus shedding light on the educational implications of achievement prediction models. The findings of this study provide empirical data support for teachers and educators, facilitating the refinement of their instructional strategies.
• Comparative analysis: It explores student achievement prediction models in three distinct educational settings: online, offline, and blended teachings. This exploration reveals variances in teaching patterns across these modes, yielding practical advice for educators in applying these prediction models.

Structure of the article

This paper is organized as follows: Section ‘Literature review’ presents a review of related literatures, providing a comprehensive review of the existing literature on student achievement prediction, examines the prevailing issues and identifies the gaps within the current body of research. Section ‘Methodology’ details the methodology employed in this study, introduces the interpretable performance prediction framework and the indicators system used in this paper and outlines the methodology used to conduct the data analysis for this paper. The findings and their implications are discussed in Sections ‘Case study’ and ‘Results’ respectively. The paper concludes with a summary of our key findings in the final Section ‘Discussion and Conclusions’. Table 1 illustrates the list of abbreviations.

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https://doi.org/10.1371/journal.pone.0309838.t001

Literature review

Previous research, student achievement prediction indicators..

Prediction accuracy largely depends on the careful selection of indicators. The initial and most critical step is the selection of appropriate input data. Previous research has identified three key groups of student-related features as pertinent input parameters: historical student performance, student engagement, and demographic data (Tomasevic et al., 2020).

Historical student performance has been consistently identified as a reliable predictor. For instance, DeBerard et al. [ 9 ] demonstrated that high school GPA is a strong predictor of college academic success. Similarly, Shaw et al. [ 10 ] found that combined SAT scores explain about 28% of the variance in first-year college GPA. Moreover, test scores have been used to predict future academic performance in various studies [ 11 ].

Regarding student engagement, a notable correlation with academic achievement has been observed [ 12 ]. Hussain et al. [ 13 ] identified a moderately strong positive correlation between student engagement and academic achievement. With evolving teaching formats like Massive Open Online Courses and the flipped classrooms, several studies have developed predictive models by analyzing student behaviors in learning management systems, such as video interactions, assignment submissions, and forum discussions [ 14 ]. With the innovation of modern educational technology tools, including artificial intelligence tools (such as ChatGPT) and virtual reality, significant roles have been played in enhancing student learning outcomes by integrating with educational theories like constructivism, experiential learning, and collaborative learning. These technologies, by offering immersive and interactive learning experiences, have increased student engagement, motivation, and critical thinking skills, thereby positively impacting academic performance [ 15 , 16 ].

Studies have also considered demographic factors. Research indicates that demographic factors play a moderate role in predictive accuracy, with relevance around 60% in some studies, while others suggest that these variables have a limited impact on prediction precision [ 5 , 17 ]. Additional indicators, such as student collaboration, teacher-student communication, and psychological factors like motivation and attitude, have also been explored. Recent studies emphasize the importance of considering learners’ psychological well-being and cognitive processes in educational settings [ 18 , 19 ].These motivational and coping strategies remarkably influence students’ learning approaches and overall educational outcomes [ 20 ].

The above discussion shows that student achievement is a composite of cognitive, behavioral, skill-based, and emotional outcomes derived from educational experiences [ 21 ]. Although there is a consensus on the selection of certain important indicators, the selection of the dataset for student achievement prediction varies from study to study. Selecting the most suitable dataset depends largely on the specific goals and objectives of the researchers, with no universally accepted guidelines.

Student achievement prediction models.

Originally, conventional statistical methods such as Discriminant Analysis and Multiple Linear Regression were the predominant approaches in the early stages of educational research [ 22 ]. Furthermore, Structural Equation Modeling (SEM) has been widely adopted in the social sciences. However, these traditional methods have often fallen short of delivering consistent and precise predictions or classifications [ 23 ].

Recently, an array of machine learning algorithms has been employed, including Multiple Regression, Probabilistic and Logistic Regression, Neural Networks, Decision Trees, Random Forests (RF), Genetic Algorithms, and Bayesian algorithms. These have shown varied levels of success in achieving high predictive accuracy [ 24 ]. Comparative studies of machine learning methods have been conducted, with Caruana et al. [ 25 ] exploring the performance evaluation of these models. Their research underscores a fundamental point: no single model or method universally excels across all problems and metrics. Tomasevic et al. [ 5 ] used the Open University Learning Analytics Dataset for a regression problem, finding that Artificial Neural Networks (ANN) and Decision Trees were the most effective, while KNN, SVM, and Bayesian linear regression were less successful.

While previous approaches using machine learning models for predicting student achievement have focused on model optimization [ 26 ], there are growing concerns regarding the opaque nature of complex models, which may hinder their broader application [ 27 ].

Interpretable machine learning models.

Nowadays, with the rapid development of artificial intelligence (AI) technology, ML models are being applied in many critical fields, such as education [ 28 , 29 ], healthcare [ 30 – 32 ]. However, as the number of parameters soars, the ’black-box’ nature of neural networks has raised concerns. Interpretable machine learning is a promising tool to alleviate concerns regarding the opacity of machine learning models. It equips ML models with the capability to articulate their processes in a manner comprehensible to humans [ 33 ].

Broadly, interpretable machine learning methods are divided into two categories: self-interpretation models and post-hoc interpretation methods [ 34 ]. Self-interpreting models typically have a simpler structure and include Linear models, Logistic Regression, and Decision Trees. Post-hoc interpretation methods involve either model-independent or model-specific techniques, applicable to various models but may require additional computational resources and analytical expertise.

Post-hoc or model-independent interpretation methods are extensively used in different scenarios. These include Partial Dependence Plot [ 35 ], Individual Conditional Expectation [ 36 ], Permutation Feature Importance [ 37 ], Local Interpretable Model-agnostic Explanations, and the SHAP method. The survey in the field of information resource management revealed that 83.7% of explainable ML applications utilize post-hoc explanation methods, with SHAP (51.2%) and feature importance analysis (34.1%) being the most common. Unlike traditional feature importance which indicates the significance of features without clarifying their impact on predictions, SHAP offers detailed explanations on both sample and feature levels through various visualizations like waterfall diagrams and feature dependency diagrams.

These interpretative approaches have been applied in diverse fields such as medicine, policymaking, and science, aiding in auditing predictions under circumstances like regulatory pressures and the pursuit of fairness [ 35 ]. However, the critical aspect of interpretability in machine learning models within the domain of educational management research remains underexplored.

Research gap

Given the aforementioned limitations, the interpretability of ML is a contentious issue. The various ML algorithms employed often fail to effectively elucidate the relationship between factors influencing students’ academic performance and their grades. Additionally, they struggle to quantify the impact of each feature on the target value and to determine the positive or negative influence of each characteristic. To address these gaps in the literature, our study delves into the following areas:

• Feature Importance Analysis: Our research will quantify the influence of each feature on the prediction of student performance. This involves a detailed examination of the weight and significance of various factors in determining academic outcomes.
• Impact Assessment: We will assess the positive or negative impact of each feature on the target variable. This is crucial for understanding not only the magnitude of the influence but also its direction.
• Model Comparison: By comparing the interpretability and performance of different ML models, our study seeks to identify the most effective approaches for student achievement prediction.
• Practical Implications: We will discuss the practical implications of our findings, focusing on how increased interpretability can enhance educational practices and inform policy-making.

Through this comprehensive approach, our study seeks to bridge the gap in the current research by providing a clearer understanding of the mechanisms behind student achievement prediction models and their implications for educational stakeholders.

Methodology

Development of an interpretable performance prediction framework.

As shown in Fig 1 , we have developed an interpretable framework for performance prediction. The framework’s core involves extracting five key features: academic factors, student engagement, demographic factors, psychological aspects, and self-directed learning abilities. These features form an input vector that accurately represents factors relevant to achievement prediction. The data for this study is sourced from three main systems: the Education Administration System (EAS), the Chaoxing Xuexitong System, and various questionnaires.

https://doi.org/10.1371/journal.pone.0309838.g001

The methodology progresses in three phases. The initial phase involves creating an indicator system from these features. In the subsequent phase, we focus on constructing and elucidating performance prediction models. Four different ML algorithms are applied to our “learning” dataset. Their effectiveness is evaluated using two standard ML metrics: Mean Absolute Error (MAE) and R-squared ( R 2 ). The optimal model is then selected based on these evaluations. The final stage of our methodology is the model interpretability phase, which accounts for the educational significance of the model by analyzing the importance and directional influence of the indicators. This phase aims to provide educators with insights to refine their teaching strategies.

Development of the indicator system

As mentioned in ‘Literature review’ section, prior research insights advocate categorizing student-related features into historical student performance, engagement, and demographic data [ 5 ]. To capture a holistic view of learner characteristics, we have expanded this system to include psychological factors and self-directed learning capabilities to form a student achievement prediction indicator system, as shown in Table 2 . Considering the minimal variation in age, gender, and other demographic factors in our case study, we have chosen to focus solely on the major as the demographic data point.

https://doi.org/10.1371/journal.pone.0309838.t002

Model training

As SHAP is a model-agnostic interpretation framework, which enables it to be applied across a spectrum of common predictive models. This versatility allows SHAP to provide insights into the decision-making process of these models by quantifying the contribution of each feature to the prediction, thereby enhancing our understanding of the model’s behavior regardless of its underlying structure or algorithmic approach. Commonly used ML models for academic achievement prediction include RF, BPNN, SVM, and XGboost. The rationale for selecting these four models is their proficiency as data-driven prediction methods. RF, an ensemble learning technique, amalgamates numerous decision trees, thereby reducing variance relative to individual trees. It is known for its superior average prediction performance. BPNN, a supervised learning algorithm, builds multi-layer neural networks inspired by biological neurons and employs a back-propagation algorithm for training, excelling in handling non-linear relationships and high-dimensional data. SVM has gained recognition for its effectiveness in classification, regression, and time-series prediction. XGBoost, enhancing the Gradient Boosting Decision Tree algorithm, stands out for its accuracy and flexibility.

In this research, a 5-fold cross-validation approach was implemented to fine-tune the hyperparameter to avoid overfit, optimizing them according to the mean value derived from each test set.

Model interpretability

Addressing the opaque nature of ML models, our research employs the SHAP method for interpretability. Developed by Lundberg and Lee in 2017 [ 39 ], SHAP merges various existing approaches to provide a reliable and intuitive explanation of model predictions. It does so by illustrating how predictions shift when certain variables are omitted. The Python SHAP package ( https://github.com/slundberg/shap ), enables the calculation of SHAP values for any selected model, and it is extensively utilized due to its versatility.

SHAP is characterized by three fundamental properties: local accuracy (the sum of feature attributions equals the model output), missingness (zero attribution for non-present features), and consistency (no decrease in feature attribution despite an increased marginal contribution). A notable advantage of SHAP is its model-agnostic nature, making it applicable to any machine learning model.

Data for this study was obtained from the EAS of a Wuhan-based public university. This system provided access to students’ personal information, such as majors and academic grades. In addition, we gathered course-related learning data from the Chaoxing Xuexitong system, a widely used online education platform in China. To obtain data on self-study hours, learning attitudes, and self-directed learning indicators, we employed questionnaires as the methodological instrument. The learning attitude questionnaire adapted from the English-learning Motivation Scale developed by a Chinese scholar Meihua Liu [ 40 ] who is from Tsinghua University, a tool commonly utilized in in EFL teaching and learning in the Chinese context. For assessing self-directed learning capabilities, we used a questionnaire adapted from Jinfen Xu ‘s [ 41 ] self-directed learning capability scale. These questionnaires were administered in class under instructor supervision and lasted approximately 10 minutes each, aiming to evaluate students’ learning attitudes and their aptitude for independent learning. The surveys were conducted midway through each semester. Our dataset encompasses data from 87 students enrolled in the Japanese course for the class of 2021, spanning three different learning modes. It includes nine indicators linked to student grades, amounting to a total of 2349 data entries. Table 3 shows the types of nine indicators.

https://doi.org/10.1371/journal.pone.0309838.t003

While analyzing the datasets, an imbalanced data pattern was noted. To address this, we grouped students into three broad specialty categories: Arts, Science and Technology, and Arts and Sports. This categorization reduced data sparsity by assigning discrete values (1, 2, 3) to these groups.

Ethical considerations

The study was approved by the institutional review board, and the study runs from September 2021 to June 2023. All participants were not at risk if they chose or declined to participate. Parental consent is not required for undergraduate students participating in the study. Additionally, we explained the purpose of the study in the questionnaire, clarified that it was their right to participate or not to participate in the study, and informed all the participants that ‘submitting answers’ is considered informed consent for researchers to use their questionnaire responses and related data retrieved from EAS and Chaoxing platform in publications of the research.

Experimental setup

In this study, we conducted experiments employed PyCharm version 2022.3.3 as the compilation software, and implemented the algorithmic model using Python. The dataset was randomly partitioned into training and test sets in a 4:1 ratio for robust training and evaluation.

As state in the Methodology Section, we employ four classic ML models as our predictive model for academic performance. Table 4 presents the pseudo-code outlining the experimental procedures.

https://doi.org/10.1371/journal.pone.0309838.t004

Comparison of models

To obtain the optimal model parameters, the hyperparameters of the aforementioned four models were optimized separately. Table 5 displays the optimal hyperparameter combinations for the aforementioned four models.

https://doi.org/10.1371/journal.pone.0309838.t005

Table 6 presents the comparison of the task performance of four models. Both BPNN and XGBoost show higher task performance compared to RF, while SVM lags in terms of task performance. The comparison indicates that XGBoost slightly surpasses BPNN, establishing XGBoost as the model with the best predictive performance. Therefore, this study selects the XGBoost model to fit all the data. SHAP values are used for interpretation.

https://doi.org/10.1371/journal.pone.0309838.t006

Exploratory analysis utilizing XGBoost and SHAP

Given the effectiveness of the XGBoost model, it was selected for further analysis using SHAP to explore teaching patterns within the model across various teaching modes. SHAP offers insights into the influence of each indicator per sample, highlighting both positive and negative effects. In the associated figures, color coding is used to represent the magnitude of eigenvalues, with red indicating high values and blue representing low values.

Figs 2 and 3 shows the importance of indicators and a summary plot for offline teaching. The average SHAP value (horizontal axis) indicates the significance of each indicator, with their order of importance shown on the vertical axis in Fig 2 . Key findings include classroom performance, previous exam grades, and student major as the most influential indicators. The impact of eigenvalues on each sample is depicted in Fig 3 , where each row represents an indicator, each dot signifies a sample, and the SHAP value is plotted on the horizontal axis. Further analysis revealed a positive relationship between prior exam grades, self-directed learning ability, learning attitudes, and their effect on academic achievement predictions. Interestingly, occasional absences did not show a substantial negative influence on predicted grades, hinting at a divergence in the dynamics of college classrooms from high school settings. This might be attributed to the independent learning skills prevalent among college students. Moreover, it was noted that students majoring in Arts and Sports tend to have a slightly negative impact on predicted grades.

https://doi.org/10.1371/journal.pone.0309838.g002

https://doi.org/10.1371/journal.pone.0309838.g003

Analysis of online teaching using XGBoost and SHAP

Figs 4 and 5 presents the indicator importance and summary plot for online teaching. A key observation is the increased influence of previous exam grades on the predicted values in comparison to offline settings. This suggests that students with a strong academic foundation tend to be more self-directed, thereby enhancing their predicted performance more remarkably. The disparity in self-directed learning abilities is more evident in online courses, highlighting the detrimental effect of inadequate self-learning skills on performance. Students struggling with self-learning might not receive timely support, leading to poorer outcomes. In this context, classroom performance becomes a less critical predictor, and the influence of a student’s major on predicted scores also diminishes. Interestingly, self-study time shows a positive correlation with predicted grades, while the relationship between quiz scores and performance prediction remains insignificant.

https://doi.org/10.1371/journal.pone.0309838.g004

https://doi.org/10.1371/journal.pone.0309838.g005

Blended teaching: Insights from XGBoost and SHAP

Figs 6 and 7 examines the indicator importance and summary plot for blended teaching. In this teaching mode, the impact of self-directed learning skills is more notable compared to other teaching methods, possibly due to the adoption of flipped classroom techniques. Self-directed learning shows a stronger positive correlation with both previous exam grades and quiz scores. Furthermore, the relevance of attitude towards learning is accentuated, suggesting its growing importance in blended learning environments where independent study is emphasized.

https://doi.org/10.1371/journal.pone.0309838.g006

https://doi.org/10.1371/journal.pone.0309838.g007

Discussion and conclusions

The prediction of academic achievement in higher education has become an increasingly prominent topic within the field of education [ 42 ]. In today’s information age, the tremendous growth of educational institutions’ electronic data “…can be utilized for discovering unknown patterns and trends” [ 43 ].Recent researches on predicting student performance are frequently spearheaded by educators identifying as "AI" educators to identify features that can be used to make predictions [ 44 ], to identify algorithms that can improve predictions [ 45 ], and to quantify aspects of student performance. However, analyzing performance, providing high-quality education strategies for evaluating the students’ performance from these abundant resources are among the prevailing challenges universities face [ 46 ].

In this research, we have developed the XGB-SHAP model, integrating XGBoost with SHAP, to systematically explore the relationship between grade prediction and diverse indicators across various teaching methods. Focused on university Japanese language classes, our study demonstrated XGBoost’s superior performance over other models, as evidenced by R 2 and MAE metrics. The integration of SHAP offered a clear visual representation, highlighting the mode and directional influence of each indicator and sheds light on the educational implications of ML structures in pedagogy. The study also supported that the XGB-SHAP model can be effectively used in the field of educational management research.

The results reveal that, the study of student achievement prediction, using student-related features, such as student historical achievement, student engagement and demographic data, which have been used as important input features in the previous literature, is not sufficient. With the development of society and the diversification of teaching and learning modes, the importance of self-directed learning skills in the prediction of university students’ performance has been demonstrated in this study. Psychological factors such as attitude towards learning should also be taken into account. The impact of a student’s major on foreign language learning is considerable, which indicate differences in learning environments, cultural factors, motivation to learn foreign languages. While classroom response accuracy and attendance appeared less critical. This suggests a potential shift in focus within higher education classrooms, advocating for a tailored approach to characteristic selection based on teaching modes. This methodology provides educators with a quantitative view of how educational processes affect student achievement.

Our study also shows that the factors influencing student performance vary: offline teaching values classroom performance, while online teaching and blended teaching emphasize independent learning. In blended teaching, quiz scores have a remarkable positive impact, differing from the trends in other modes. This could be attributed to quizzes acting as formative assessments in blended learning, enhancing student participation and providing continual feedback. Consequently, teaching strategies and support systems should be adapted to meet the distinct needs of each teaching mode to optimize learning outcomes.

Acknowledging the formidable technical challenges associated with interpretable machine learning models in practical educational contexts, it is imperative to recognize their substantial contributions in enhancing our comprehension and utility of achievement prediction models. Additionally, they play a pivotal role in mitigating the skepticism harbored by educators towards machine learning models deployed for achievement prediction. Moving forward, there exist several promising avenues for exploration within the realm of interpretable machine models that merit thorough investigation: first, expand the dataset to cover more academic areas, different institutions, and varied student groups. This will test the model’s effectiveness in diverse settings. Second, the refinement and augmentation of existing interpretable models to enhance their accuracy and utility. These directions offer promising avenues for furthering the application and acceptance of interpretable machine learning in educational settings.

Supporting information

S1 file. original data..

https://doi.org/10.1371/journal.pone.0309838.s001

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• Review article
• Open access
• Published: 10 February 2020

Predicting academic success in higher education: literature review and best practices

• Eyman Alyahyan 1 &
• Dilek Düştegör   ORCID: orcid.org/0000-0003-2980-1314 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  3 ( 2020 ) Cite this article

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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success , through which student attributes to focus on , up to which machine learning method is more appropriate to the given problem . This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

Introduction

Computers have become ubiquitous, especially in the last three decades, and are significantly widespread. This has led to the collection of vast volumes of heterogeneous data, which can be utilized for discovering unknown patterns and trends (Han et al., 2011 ), as well as hidden relationships (Sumathi & Sivanandam, 2006 ), using data mining techniques and tools (Fayyad & Stolorz, 1997 ). The analysis methods of data mining can be roughly categorized as: 1) classical statistics methods (e.g. regression analysis, discriminant analysis, and cluster analysis) (Hand, 1998 ), 2) artificial intelligence (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019 ) (e.g. genetic algorithms, neural computing, and fuzzy logic), and 3) machine learning (e.g. neural networks, symbolic learning, and swarm optimization) (Kononenko & Kukar, 2007 ). The latter consists of a combination of advanced statistical methods and AI heuristics. These techniques can benefit various fields through different objectives, such as extracting patterns, predicting behavior, or describing trends. A standard data mining process starts by integrating raw data – from different data sources – which is cleaned to remove noise, duplicated or inconsistent data. After that, the cleaned data is transformed into a concise format that can be understood by data mining tools, through filtering and aggregation techniques. Then, the analysis step identifies the existing interesting patterns, which can be displayed for a better visualization (Han et al., 2011 ) (Fig.  1 ).

standard data mining process (Han et al. 2011 )

Recently data mining has been applied to various fields like healthcare (Kavakiotis et al., 2017 ), business (Massaro, Maritati, & Galiano, 2018 ), and also education (Adekitan, 2018 ). Indeed, the development of educational database management systems created a large number of educational databases, which enabled the application of data mining to extract useful information from this data. This led to the emergence of Education Data Mining (EDM) (Calvet Liñán & Juan Pérez, 2015 ; Dutt, Ismail, & Herawan, 2017 ) as an independent research field. Nowadays, EDM plays a significant role in discovering patterns of knowledge about educational phenomena and the learning process (Anoopkumar & Rahman, 2016 ), including understanding performance (Baker, 2009 ). Especially, data mining has been used for predicting a variety of crucial educational outcomes, like performance (Xing, 2019 ), retention (Parker, Hogan, Eastabrook, Oke, & Wood, 2006 ), success (Martins, Miguéis, Fonseca, & Alves, 2019 ; Richard-Eaglin, 2017 ), satisfaction (Alqurashi, 2019 ), achievement (Willems, Coertjens, Tambuyzer, & Donche, 2018 ), and dropout rate (Pérez, Castellanos, & Correal, 2018 ).

The process of EDM (see Fig.  2 ) is an iterative knowledge discovery process that consists of hypothesis formulation, testing, and refinement (Moscoso-Zea et al., 2016 ; Sarala & Krishnaiah, 2015 ). Despite many publications, including case studies, on educational data mining, it is still difficult for educators – especially if they are a novice to the field of data mining – to effectively apply these techniques to their specific academic problems. Every step described in Fig. 2 necessitates several decisions and set-up of parameters, which directly affect the quality of the obtained result.

Knowledge discovery process in educational institutions (Moscoso-Zea, Andres-Sampedro, & Lujan-Mora, 2016 )

This study aims to fill the described gap, by providing a complete guideline, providing easier access to data mining techniques and enabling all the potential of their application to the field of education. In this study, we specifically focus on the problem of predicting the academic success of students in higher education. For this, the state-of-the-art has been compiled into a systematic process, where all related decisions and parameters are comprehensively covered and explained along with arguments.

In the following, first, section 2 clarifies what is academic success and how it has been defined and measured in various studies with a focus on the factors that can be used for predicting academic success. Then, section 3 presents the methodology adopted for the literature review. Section 4 reviews data mining techniques used in predicting students’ academic success, and compares their predictive accuracy based on various case studies. Section 5 concludes the review, with a recapitulation of the whole process. Finally, section 6 concludes this paper and outlines the future work.

Academic success definition

Student success is a crucial component of higher education institutions because it is considered as an essential criterion for assessing the quality of educational institutions (National Commission for Academic Accreditation &amp, 2015 ). There are several definitions of student success in the literature. In (Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006 ), a definition of student success is synthesized from the literature as “Student success is defined as academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills and competencies, persistence, attainment of educational outcomes, and post-college performance”. While this is a multi-dimensional definition, authors in (York, Gibson, & Rankin, 2015 ) gave an amended definition concentrating on the most important six components, that is to say “Academic achievement, satisfaction, acquisition of skills and competencies, persistence, attainment of learning objectives, and career success” (Fig.  3 ).

Defining academic success and its measurements (York et al., 2015 )

Despite reports calling for more detailed views of the term, the bulk of published researchers measure academic success narrowly as academic achievement. Academic achievement itself is mainly based on Grade Point Average (GPA), or Cumulative Grade Point Average (CGPA) (Parker, Summerfeldt, Hogan, & Majeski, 2004 ), which are grade systems used in universities to assign an assessment scale for students’ academic performance (Choi, 2005 ), or grades (Bunce & Hutchinson, 2009 ). The academic success has also been defined related to students’ persistence, also called academic resilience (Finn & Rock, 1997 ), which in turn is also mainly measured through the grades and GPA, measures of evaluations by far the most widely available in institutions.

Review methodology

Early prediction of students’ performance can help decision makers to provide the needed actions at the right moment, and to plan the appropriate training in order to improve the student’s success rate. Several studies have been published in using data mining methods to predict students’ academic success. One can observe several levels targeted:

Degree level: predicting students’ success at the time of obtention of the degree.

Year level: predicting students’ success by the end of the year.

Course level: predicting students’ success in a specific course.

Exam level: predicting students’ success in an exam for a specific course.

In this study, the literature related to the exam level is excluded as the outcome of a single exam does not necessarily imply a negative outcome.

In terms of coverage, section 4 and 5 only covers articles published within the last 5 years. This restriction was necessary to scale down the search space, due to the popularity of EDM. The literature was searched from Science Direct, ProQuest, IEEE Xplore, Springer Link, EBSCO, JSTOR, and Google Scholar databases, using academic success , academic achievement , student success , educational data mining , data mining techniques , data mining process and predicting students’ academic performance as keywords. While we acknowledge that there may be articles not included in this review, seventeen key articles about data mining techniques that were reviewed in sections 4 and 5 .

Influential factors in predicting academic success

One important decision related to the prediction of students’ academic success in higher education is to clearly define what is academic success. After that, one can think about the potential influential factors, which are dictating the data that needs to be collected and mined.

While a broad variety of factors have been investigated in the literature with respect to their impact on the prediction of students’ academic success (Fig.  4 ), we focus here on prior-academic achievement , student demographics , e-learning activity , psychological attributes , and environments , as our investigation revealed that they are the most commonly reported factors (summarized in Table  1 ). As a matter of fact, the top 2 factors, namely, prior-academic achievement , and student demographics , were presented in 69% of the research papers. This observation is aligned with the results of The previous literature review which emphasized that the grades of internal assessment and CGPA are the most common factors used to predict student performance in EDM (Shahiri, Husain, & Rashid, 2015 ). With more than 40%, prior academic achievement is the most important factor. This is basically the historical baggage of students. It is commonly identified as grades (or any other academic performance indicators) that students obtained in the past (pre-university data, and university-data). The pre-university data includes high school results that help understand the consistency in students’ performance (Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Garg, 2018 ; Mesarić & Šebalj, 2016 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ). They also provide insight into their interest in different topics (i.e., courses grade (Asif et al., 2015 ; Asif et al., 2017 ; Oshodi et al., 2018 ; Singh & Kaur, 2016 )). Additionally, this can also include pre-admission data which is the university entrance test results (Ahmad et al., 2015 ; Mesarić & Šebalj, 2016 ; Oshodi et al., 2018 ). The university-data consists of grades already obtained by the students since entering the university, including semesters GPA or CGPA (Ahmad et al., 2015 ; Almarabeh, 2017 ; Hamoud et al., 2018 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ), courses marks (Al-barrak & Al-razgan, 2016 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ; Sivasakthi, 2017 ) and course assessment grades (e.g. assignment (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mueen et al., 2016 ; Yassein et al., 2017 ); quizzes (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mohamed & Waguih, 2017 ; Yassein et al., 2017 ); lab-work (Almarabeh, 2017 ; Mueen et al., 2016 ; Yassein et al., 2017 ); and attendance (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Mueen et al., 2016 ; Putpuek et al., 2018 ; Yassein et al., 2017 )).

a broad variety of factors potentially impacting the prediction of students’ academic success

Students’ demographic is a topic of divergence in the literature. Several studies indicated its impact on students’ success, for example, gender (Ahmad et al., 2015 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Putpuek et al., 2018 ; Sivasakthi, 2017 ), age (Ahmad et al., 2015 ; Hamoud et al., 2018 ; Mueen et al., 2016 ), race/ethnicity (Ahmad et al., 2015 ), socioeconomic status (Ahmad et al., 2015 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Putpuek et al., 2018 ), and father’s and mother’s background (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ) have been shown to be important. Yet, few studies also reported just the opposite, for gender in particular (Almarabeh, 2017 ; Garg, 2018 ).

Some attributes related to the student’s environment were found to be impactful information such as program type (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ), class type (Mueen et al., 2016 ; Sivasakthi, 2017 ) and semester period (Mesarić & Šebalj, 2016 ).

Among the reviewed papers, also many researchers used Student E-learning Activity information, such as a number of login times, number of discussion board entries, number / total time material viewed (Hamoud et al., 2018 ), as influential attributes and their impact, though minor, were reported.

The psychological attributes are determined as the interests and personal behavior of the student; several studies have shown them to be impactful on students’ academic success. To be more precise, student interest (Hamoud et al., 2018 ), the behavior towards study (Hamoud et al., 2018 ; Mueen et al., 2016 ), stress and anxiety (Hamoud et al., 2018 ; Putpuek et al., 2018 ), self-regulation and time of preoccupation (Garg, 2018 ; Hamoud et al., 2018 ), and motivation (Mueen et al., 2016 ), were found to influence success.

Data mining techniques for prediction of students’ academic success

The design of a prediction model using data mining techniques requires the instantiation of many characteristics, like the type of the model to build, or methods and techniques to apply (Witten, Frank, Hall, & Pal, 2016 ). This section defines these attributes, provide some of their instances, and reveal the statistics of their occurrence among the reviewed papers grouped by the target variable in the student success prediction, that is to say, degree level, year level, and course level.

Degree level

Several case studies have been published, seeking prediction of academic success at the degree level. One can observe two main approaches in term of the model to build: classification where CGPA that is targeted is a category as multi class problem such as (a letter grade (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) or overall rating (Al-barrak & Al-razgan, 2016 ; Putpuek et al., 2018 )) or binary class problem such as (pass/fail (Hamoud et al., 2018 ; Oshodi et al., 2018 )). As for the other approach, it is the regression where the numerical value of CGPA is predicted (Asif et al., 2017 ). We can also observe a broad variety in terms of the department students belongs to, from architecture (Oshodi et al., 2018 ), to education (Putpuek et al., 2018 ), with a majority in technical fields (Adekitan & Salau, 2019 ; Al-barrak & Al-razgan, 2016 ; Asif et al., 2015 ; Hamoud et al., 2018 ). An interesting finding is related to predictors: studies that included university-data, especially grades from first 2 years of the program, yielded better performance than studies that included only demographics (Putpuek et al., 2018 ), or only pre-university data (Oshodi et al., 2018 ). Details regarding the algorithm used, the sample size, the best accuracy and corresponding method, as well as the software environment that was used are all in Table  2 .

Less case studies have been reported, seeking prediction of academic success at the year level. Yet, the observations regarding these studies are very similar to the one related to degree level (reported in previous section). Similar to previous sub-section, studies that included only social conditions and pre-university data gave the worse accuracy (Singh & Kaur, 2016 ), while including university-data improved results (Anuradha & Velmurugan, 2015 ). Nevertheless, it is interesting to note that even the best accuracy in (Anuradha & Velmurugan, 2015 ) is inferior to the accuracy in (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) reported in previous section. This can be explained by the fact that in (Anuradha & Velmurugan, 2015 ), only 1 year of past university-data is included while in (Asif et al., 2015 ; Asif et al., 2017 ), 2 years of past university-data and in (Adekitan & Salau, 2019 ) 3 years of past university-data is covered. Other details for these methods are in Table  3 .

Course level

Finally, some studies can be reported, seeking the prediction of academic success at the course level. As already mentioned in degree level and year level sections, the comparative work gives accuracies of 62% to 89% while predicting success at a course level can give accuracies more than 89%, which can be seen as a more straightforward task than predicting success at degree level or year level. The best accuracy is obtained in course level with 93%. In (Garg, 2018 ), the target course was an advanced programming course while the influential factor was a previous programming course, also a prerequisite course. This demonstrates how important it is to have a field knowledge and use this knowledge to guide the decisions in the process and target important features. All other details for these methods are in Table  4 .

Data mining process model for student success prediction

This section compiles as a set of guidelines the various steps to take while using educational data mining techniques for student success prediction; all decisions needed to be taken at various stages of the process are explained, along with a shortlist of best practices collected from the literature. The proposed framework (Fig.  5 ) has been derived from well-known processes (Ahmad et al., 2015 ; Huang, 2011 ; Pittman, 2008 ). It consists of six main stages: 1) data collection, 2) data initial preparation, 3) statistical analysis, 4) data preprocessing, 5) data mining implementation, and 6) result evaluation. These stages are detailed in the next subsections.

Stages of the EDM framework

Data collection

In educational data mining, the needed information can be extracted from multiple sources. As indicated in Table 1 , the most influential factor observed in the literature is Prior Academic Achievement. Related data, that is to say, pre-university or university-data, can easily be retrieved from the university Student Information System (SIS) that are so widely used nowadays. SIS can also provide some student demographics (e.g. age, gender, ethnicity), but socio-economic status might not be available explicitly. In that case, this could either be deduced from existing data, or it might be directly acquired from students through surveys. Similarly, students’ environment related information also can be extracted from the SIS, while psychological data would probably need the student to fill a survey. Finally, students’ e-learning activities can be obtained from e-learning system logs (Table  5 ).

Initial preparation of data

In its original form, the data (also called raw data) is usually not ready for analysis and modeling. Data sets that are mostly obtained from merging tables in the various systems cited in Table 5 might contain missing data, inconsistent data, incorrect data, miscoded data, and duplicate data. This is why the raw data needs to go through an initial preparation (Fig.  6 ), consisting of 1) selection, 2) cleaning, and 3) derivation of new variables. This is a vital step, and usually the most time consuming (CrowdFlower, 2016 ).

Initial Preparation of Data

Data selection

The dimension of the data gathered can be significant, especially while using prior academic achievements (e.g. if all past courses are included both from high-school and completed undergraduate years). This can negatively impact the computational complexity. Furthermore, including all the gathered data in the analysis can yield below optimal prediction results, especially in case of data redundancy, or data dependency. Thus, it is crucial to determine which attributes are important, or needs to be included in the analysis. This requires a good understanding of the data mining goals as well as the data itself (Pyle, Editor, & Cerra, 1999 ). Data selection, also called “Dimensionality Reduction” (Liu & Motoda, 1998 ), consists in vertical (attributes/variables) selection and horizontal (instance/records) selection (García, Luengo, & Herrera, 2015 ; Nisbet, Elder, & Miner, 2009 ; Pérez et al., 2015 ) (Table  6 ). Also, it is worth noticing that models obtained from a reduced number of features will be easier to understand (Pyle et al., 1999 ).

Data cleaning

Data sources tend to be inconsistent, contain noises, and usually suffer from missing values (Linoff & Berry, 2011 ). When a value is not stored for a variable, it is considered as missing data. When a value is in an abnormal distance from the other values in the dataset, it is called an outlier. Literature reveals that missing values and outliers are very common in the field of EDM. Thus, it is important to know how to handle them without compromising the quality of the prediction. All things considered, dealing with missing values or outliers cannot be done by a general procedure, and several methods need to be considered within the context of the problem. Nevertheless, we try to here to summarize the main approaches observed in the literature and Table  7 provides a succinct summary of them.

If not treated, missing value becomes a problem for some classifiers. For example, Support Vector Machines (SVMs), Neural Networks (NN), Naive Bayes, and Logistic Regression require full observation (Pelckmans, De Brabanter, Suykens, & De Moor, 2005 ; Salman & Vomlel, 2017 ; Schumacker, 2012 ), however, decision trees and random forests can handle missing data (Aleryani, Wang, De, & Iglesia, 2018 ). There are two strategies to deal with missing values. The first one is a listwise deletion, and it consists in deleting either the record (row deletion, when missing values are few) or the attribute/variable (column deletion, when missing values are too many). The second strategy, imputation, that derives the missing value from the remainder of the data (e.g. median, mean, a constant value for numerical value, or randomly selected value from missing values distribution (McCarthy, McCarthy, Ceccucci, & Halawi, 2019 ; Nisbet et al., 2009 )).

Outliers data are also known as anomalies, can easily be identified by visual means, creating a histogram, stem and leaf plots or box plots and looking for very high or very low values. Once identified, outliers can be removed from the modeling data. Another possibility is to converts the numeric variable to a categorical variable (i.e. bin the data) or leaves the outliers in the data (McCarthy et al., 2019 ).

Derivation of new variables

New variables can be derived from existing variables by combining them (Nisbet et al., 2009 ). When done based on domain knowledge, this can improve the data mining system (Feelders, Daniels, & Holsheimer, 2000 ). For example, GPA is a common variable that can be obtained from SIS system. If taken as it is, a student’s GPA reflects his/her average in a given semester. However, this does not explicitly say anything about this student’s trend over several semesters. For the same GPA, one student could be in a steady state, going through an increasing trend, or experiencing a drastic performance drop. Thus, calculating the difference in GPA between consecutive semesters will add an extra information. While there is no systematic method for deriving new variables, Table  8 recapitulates the instances that we observed in the EDM literature dedicated to success prediction.

Statistical analysis

Preliminary statistical analysis, especially through visualization, allows to better understand the data before moving to more sophisticated data mining tasks and algorithms (McCarthy et al., 2019 ). Table  9 summarizes the statistics commonly derived depending on the data type. Data mining tools contain descriptive statistical capabilities. Dedicated tools like STATISTICA (Jascaniene, Nowak, Kostrzewa-Nowak, & Kolbowicz, 2013 ) and SPSS (L. A. D. of S. University of California and F. Foundation for Open Access Statistics, 2004 ) can also provide tremendous insight.

It is important to note that this step can especially help planning further steps in DM process, including data pre-processing to identify the outliers, determining the patterns of missing data, study the distribution of each variable and identify the relationship between independent variables and the target variable (see Table  10 ). Furthermore, statistical analysis is used in the interpreting stage to explain the results of the DM model (Pyle et al., 1999 ).

Data preprocessing

The last step before the analysis of the data and modeling is preprocessing, which consists of 1) data transformation, 2) how to handle imbalanced data sets, and 3) feature selection (Fig.  7 ).

Data Preprocessing

Data transformation

Data transformation is a necessary process to eliminate dissimilarities in the dataset, thus it becomes more appropriate for data mining (Osborne, 2002 ). In EDM for success prediction, we can observe the following operations:

Normalization of numeric attributes: this is a scaling technique used when the data includes varying scales, and the used data mining algorithm cannot provide a clear assumptions of the data distribution (Patro & Sahu, 2015 ). We can cite K-nearest neighbors and artificial neural networks (How to Normalize and Standardize Your Machine Learning Data in Weka, n.d. ) as examples of such algorithms. Normalizing the data may improve the accuracy and the efficiency of the mining algorithms, and provide better results (Shalabi & Al-Kasasbeh, 2006 ). The common normalization techniques are min-max (MM), decimal scaling, Z-score (ZS), median and MAD, double sigmoid (DS), tanh, and bi-weight normalizations (Kabir, Ahmad, & Swamy, 2015 ).

Discretization: The simplest method of discretization binning (García et al., 2015 ), converts a continuous numeric variable into a series of categories by creating a finite number of bins and assigning a specific number of values to each attribute in each bin. Discretization is a necessary step when using DM techniques that allow only for categorical variables (Liu, Hussain, Tan, & Dash, 2002 ; Maimon & Rokach, 2005 ) such as C4.5 (Quinlan, 2014 ), Apriori (Agrawal, 2005 ) and Naïve Bayes (Flores, Gámez, Martínez, & Puerta, 2011 ). Discretization also increases the accuracy of the models by overcoming noisy data, and by identifying outliers’ values. Finally, discrete features are easier to understand, handle, and explain.

Convert to numeric variables: Most DM algorithms offer better results using a numeric variable. Therefore, data needs to be converted into numerical variables, using any of these methods:

Encode labels using a value between [0 and N (class-1)34 ] where N is the number of labels (Why One-Hot Encode Data in Machine Learning, n.d. ).

A dummy variable is a binary variable denoted as (0 or 1) to represent one level of a categorical variable, where (1) reflects the presence of level and (0) reflects the absence of level. One dummy variable will be created for each present level (Mayhew & Simonoff, 2015 ).

Combining levels: this allows reducing the number of levels in categorical variables and improving model performance. This is done by simply combining similar levels into alike groups through domain (Simple Methods to deal with Categorical Variables in Predictive Modeling, n.d. ).

However, note that all these methods do not necessarily lead to improved results. Therefore, it is important to repeat the modeling process by trying different preprocessing scenarios, evaluate the performance of the model, and identify the best results. Table  11 . recapitulates the various EDM application of preprocessing methods.

Imbalanced datasets

It is common in EDM applications that the dataset is imbalanced, meaning that the number of samples from one class is significantly less than the samples from other classes (e.g. number of failing students vs passing students) (El-Sayed, Mahmood, Meguid, & Hefny, 2015 ; Qazi & Raza, 2012 ). This lack of balance may negatively impact the performance of data mining algorithms (Chotmongkol & Jitpimolmard, 1993 ; Khoshgoftaar, Golawala, & Van Hulse, 2007 ; Maheshwari, Jain, & Jadon, 2017 ; Qazi & Raza, 2012 ). Re-sampling (under or over-sampling) is the solution of choice (Chotmongkol & Jitpimolmard, 1993 ; Kaur & Gosain, 2018 ; Maheshwari et al., 2017 ). Under-sampling consists in removing instances from the major class, either randomly or by some techniques to balance the classes. Oversampling consists of increasing the number of instances in the minor class, either by randomly duplicating some samples, or by synthetically generating samples (Chawla, Bowyer, Hall, & Kegelmeyer, 2002 ) (see Table  12 ).

Feature selection

When the data set is prepared and ready for modeling, then the important variables can be chosen and submitted to the modeling algorithm. This step, called feature selection, is an important strategy to be followed to mining the data (Liu & Motoda, 1998 ). Feature selection aims to choose a subset of attributes from the input data with the capability of giving an efficient description for the input data while reducing effects from unrelated variables while preserving sufficient prediction results (Guyon & Elisseeff, 2003 ). Feature selection enables reduced computation time, improved prediction performance while allowing a better understanding of the data (Chandrashekar & Sahin, 2014 ). Feature selection methods are classified into filter and wrapper methods (Kohavi & John, 1997 ). Filter methods work as preprocessing to rank the features, so high-ranking features are identified and applied to the predictor. In wrapper methods, the criterion for selecting the feature is the performance of the forecasting device, meaning that the predictor is wrapped on a search algorithm which will find a subset that gives the highest predictor performance. Moreover, there are embedded methods (Blum & Langley, 1997 ; Guyon & Elisseeff, 2003 ; P. (Institute for the S. of L. and E. Langley, 1994 ) which include variable selection as part of the training process without the need for splitting the data into training and testing sets. However, most data mining tools contains embedded feature selection methods making it easy to try them and chose the best one.

Data mining implementation

Data mining models.

Two types of data mining models are commonly used in EDM applications for success prediction: predictive and descriptive (Kantardzic, 2003 ). Predictive models apply supervised learning functions to provide estimation for expected values of dependent variables according to the features of relevant independent variables (Bramer, 2016 ). Descriptive models are used to produce patterns that describe the fundamental structure, relations, and interconnectedness of the mined data by applying unsupervised learning functions on it (Peng, Kou, Shi, & Chen, 2008 ). Typical examples of predictive models are classification (Umadevi & Marseline, 2017 ) and regression (Bragança, Portela, & Santos, 2018 ), while clustering (Dutt et al., 2017 ) and association (Zhang, Niu, Li, & Zhang, 2018 ), produce descriptive models. As stated in section 4 , classification is the most used method, followed by regression and clustering. The most commonly used classification techniques are Bayesian networks, neural networks, decision trees (Romero & Ventura, 2010 ). Common regression techniques are linear regression and logistic regression analysis (Siguenza-Guzman, Saquicela, Avila-Ordóñez, Vandewalle, & Cattrysse, 2015 ). Clustering uses techniques like neural networks, K-means algorithms, fuzzy clustering and discrimination analysis (Dutt et al., 2017 ). Table  13 shows the recurrence of specific algorithms based on the literature review that we performed.

In the process, first one needs to choose a model, namely predictive or descriptive. Then, the algorithms to build the models are chosen from the 10 techniques considered as the top 10 in DM in terms of performance, always prefer models that are interpretable and understandable such as DT and linear models (Wu et al., 2008 ). Once the algorithms have been chosen, they require to be configured before they are applied. The user must provide suitable values for the parameters in advance in order to obtain good results for the models. There are various strategies to tune parameters for EDM algorithms, used to find the most useful performing parameters. The trial and error approach is one of the simplest and easiest methods for non-expert users (Ruano, Ribes, Sin, Seco, & Ferrer, 2010 ). It consists of performing numerous experiments by modifying the parameters’ values until finding the most beneficial performing parameters.

Data mining tools

Data mining has a stack of open source tools such as machine learning tools which supports the researcher in analyzing the dataset using several algorithms. Such tools are vastly used for predictive analysis, visualization, and statistical modeling. WEKA is the most used tool for predictive modeling (Jayaprakash, 2018 ). This can be explained by its many pre-built tools for data pre-processing, classification, association rules, regression, and visualization, as well as its user-friendliness, and accessibility even to a novice in programming or data mining. But we can also cite RapidMiner and Clementine as stated in Table 4 .

Results evaluation

As several models are usually built, it is important to evaluate them and select the most appropriate. While evaluating the performance of classification algorithms, normally the confusion matrix as shown in Table  14 is used. This table gathers four important metrics related to a given success prediction model:

True Positive (TP): number of successful students classified correctly as “successful”.

False Positive (FP): number of successful students incorrectly classified as “non-successful”.

True Negative (TN): number of did not successful students classified correctly as “non-successful”.

False Negative (FN): number of did not successful students classified incorrectly as “successful”.

Different performance measures are included to evaluate the model of each classifier, almost all measures of performance are based on the confusion matrix and the numbers in it. To produce more accurate results, these measures are evaluated together. In this research, we’ll focus on the measures used in the classification problems. The measures commonly used in the literature are provided in Table  15 .

Early student performance prediction can help universities to provide timely actions, like planning for appropriate training to improve students’ success rate. Exploring educational data can certainly help in achieving the desired educational goals. By applying EDM techniques, it is possible to develop prediction models to improve student success. However, using data mining techniques can be daunting and challenging for non-technical persons. Despite the many dedicated software’s, this is still not a straightforward process, involving many decisions. This study presents a clear set of guidelines to follow for using EDM for success prediction. The study was limited to undergraduate level, however the same principles can be easily adapted to graduate level. It has been prepared for those people who are novice in data mining, machine learning or artificial intelligence.

A variety of factors have been investigated in the literature related to its impact on predicting students ‘academic success which was measured as academic achievement, as our investigation showed that prior-academic achievement, student demographics, e-learning activity, psychological attributes, are the most common factors reported. In terms of prediction techniques, many algorithms have been applied to predict student success under the classification technique.

Moreover, a six stages framework is proposed, and each stage is presented in detail. While technical background is kept to a minimum, as this not the scope of this study, all possible design and implementation decisions are covered, along with best practices compiled from the relevant literature.

It is an important implication of this review that educators and non-proficient users are encouraged to applied EDM techniques for undergraduate students from any discipline (e.g. social sciences). While reported findings are based on the literature (e.g. potential definition of academic success, features to measure it, important factors), any available additional data can easily be included in the analysis, including faculty data (e.g. competence, criteria of recruitment, academic qualifications) may be to discover new determinants.

Availability of data and materials

Not applicable.

Abbreviations

(Probabilistic) neural network

Classification

• Data mining

Decision tree

Educational data mining

K-nearest neighbors

Logistic regression

Naive Bayes

Neural network

Random forest

Rule induction

Random tree

Tree ensemble

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Alyahyan, E., Düştegör, D. Predicting academic success in higher education: literature review and best practices. Int J Educ Technol High Educ 17 , 3 (2020). https://doi.org/10.1186/s41239-020-0177-7

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Annual Review of Psychology

Volume 61, 2010, review article, the psychology of academic achievement.

• Philip H. Winne 1 , and John C. Nesbit 1
• View Affiliations Hide Affiliations Affiliations: Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; email: [email protected] , [email protected]
• Vol. 61:653-678 (Volume publication date January 2010) https://doi.org/10.1146/annurev.psych.093008.100348
• © Annual Reviews

Educational psychology has generated a prolific array of findings about factors that influence and correlate with academic achievement. We review select findings from this voluminous literature and identify two domains of psychology: heuristics that describe generic relations between instructional designs and learning, which we call the psychology of “the way things are,” and findings about metacognition and self-regulated learning that demonstrate learners selectively apply and change their use of those heuristics, which we call the psychology of “the way learners make things.” Distinguishing these domains highlights a need to marry two approaches to research methodology: the classical approach, which we describe as snapshot, bookend, between-group experimentation; and a microgenetic approach that traces proximal cause-effect bonds over time to validate theoretical accounts of how learning generates achievements. We argue for fusing these methods to advance a validated psychology of academic achievement.

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In This Article Expand or collapse the "in this article" section Academic Achievement

Introduction, general overviews.

• National and International Reports
• Measuring Academic Achievement
• Intelligence
• Personality
• Students’ Familial Background
• Other Variables Predicting Academic Achievement

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• Adolescence
• Changing Professional and Academic Identities
• Formative Assessment
• Gender and Achievement
• Gifted Education
• High-stakes Testing
• Learning Strategies
• Linguistically Inclusive Pedagogy
• Mathematics Instruction and Interventions for Students with Disabilities
• Methodological Approaches for Impact Evaluation in Educational Settings
• Response to Intervention
• Single-sex Education
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Academic Achievement by Ricarda Steinmayr , Anja Meißner , Anne F. Weidinger , Linda Wirthwein LAST REVIEWED: 30 July 2014 LAST MODIFIED: 30 July 2014 DOI: 10.1093/obo/9780199756810-0108

Academic achievement represents performance outcomes that indicate the extent to which a person has accomplished specific goals that were the focus of activities in instructional environments, specifically in school, college, and university. School systems mostly define cognitive goals that either apply across multiple subject areas (e.g., critical thinking) or include the acquisition of knowledge and understanding in a specific intellectual domain (e.g., numeracy, literacy, science, history). Therefore, academic achievement should be considered to be a multifaceted construct that comprises different domains of learning. Because the field of academic achievement is very wide-ranging and covers a broad variety of educational outcomes, the definition of academic achievement depends on the indicators used to measure it. Among the many criteria that indicate academic achievement, there are very general indicators such as procedural and declarative knowledge acquired in an educational system, more curricular-based criteria such as grades or performance on an educational achievement test, and cumulative indicators of academic achievement such as educational degrees and certificates. All criteria have in common that they represent intellectual endeavors and thus, more or less, mirror the intellectual capacity of a person. In developed societies, academic achievement plays an important role in every person’s life. Academic achievement as measured by the GPA (grade point average) or by standardized assessments designed for selection purpose such as the SAT (Scholastic Assessment Test) determines whether a student will have the opportunity to continue his or her education (e.g., to attend a university). Therefore, academic achievement defines whether one can take part in higher education, and based on the educational degrees one attains, influences one’s vocational career after education. Besides the relevance for an individual, academic achievement is of utmost importance for the wealth of a nation and its prosperity. The strong association between a society’s level of academic achievement and positive socioeconomic development is one reason for conducting international studies on academic achievement, such as PISA (Programme for International Student Assessment), administered by the OECD (Organisation for Economic Co-operation and Development). The results of these studies provide information about different indicators of a nation’s academic achievement; such information is used to analyze the strengths and weaknesses of a nation’s educational system and to guide educational policy decisions. Given the individual and societal importance of academic achievement, it is not surprising that academic achievement is the research focus of many scientists; for example, in psychology or educational disciplines. This article focuses on the explanation, determination, enhancement, and assessment of academic achievement as investigated by educational psychologists.

The exploration of academic achievement has led to numerous empirical studies and fundamental progress such as the development of the first intelligence test by Binet and Simon. Introductory textbooks such as Woolfolk 2007 provide theoretical and empirical insight into the determinants of academic achievement and its assessment. However, as academic achievement is a broad topic, several textbooks have focused mainly on selected aspects of academic achievement, such as enhancing academic achievement or specific predictors of academic achievement. A thorough, short, and informative overview of academic achievement is provided in Spinath 2012 . Spinath 2012 emphasizes the importance of academic achievement with regard to different perspectives (such as for individuals and societies, as well as psychological and educational research). Walberg 1986 is an early synthesis of existing research on the educational effects of the time but it still influences current research such as investigations of predictors of academic achievement in some of the large-scale academic achievement assessment studies (e.g., Programme for International Student Assessment, PISA). Walberg 1986 highlights the relevance of research syntheses (such as reviews and meta-analyses) as an initial point for the improvement of educational processes. A current work, Hattie 2009 , provides an overview of the empirical findings on academic achievement by distinguishing between individual, home, and scholastic determinants of academic achievement according to theoretical assumptions. However, Spinath 2012 points out that it is more appropriate to speak of “predictors” instead of determinants of academic achievement because the mostly cross-sectional nature of the underlying research does not allow causal conclusions to be drawn. Large-scale scholastic achievement assessments such as PISA (see OECD 2010 ) provide an overview of the current state of research on academic achievement, as these studies have investigated established predictors of academic achievement on an international level. Furthermore, these studies, for the first time, have enabled nations to compare their educational systems with other nations and to evaluate them on this basis. However, it should be mentioned critically that this approach may, to some degree, overestimate the practical significance of differences between the countries. Moreover, the studies have increased the amount of attention paid to the role of family background and the educational system in the development of individual performance. The quality of teaching, in particular, has been emphasized as a predictor of student achievement. Altogether, there are valuable cross-sectional studies investigating many predictors of academic achievement. A further focus in educational research has been placed on tertiary educational research. Richardson, et al. 2012 subsumes the individual correlates of university students’ performance.

Hattie, John A. C. 2009. Visible learning: A synthesis of over 800 meta-analyses relating to achievement . London: Routledge.

A quantitative synthesis of 815 meta-analyses covering English-speaking research on the achievement of school-aged students. According to Hattie, the influences of quality teaching represent the most powerful determinants of learning. Thereafter, Hattie published Visible Learning for Teachers (London and New York: Routledge, 2012) so that the results could be transferred to the classroom.

OECD. 2010. PISA 2009 key findings . Vols. 1–6.

These six volumes illustrate the results of the Programme for International Student Assessment (PISA) 2009—the most extensive international scholastic achievement assessment—regarding the competencies of fifteen-year-old students all over the world in reading, mathematics, and science. Furthermore, the presented results cover the effects of student learning behavior, social background, and scholastic resources. Unlimited online access.

Richardson, Michelle, Charles Abraham, and Rod Bond. 2012. Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin 138:353–387.

DOI: 10.1037/a0026838

A current and comprehensive review concerning the prediction of university students’ performance, illustrating self-efficacy to be the strongest correlate of tertiary grade point average (GPA). Cognitive constructs (high school GPA, American College Test), as well as further motivational factors (grade goal, academic self-efficacy) have medium effect sizes.

Spinath, Birgit. 2012. Academic achievement. In Encyclopedia of human behavior . 2d ed. Edited by Vilanayur S. Ramachandran, 1–8. San Diego, CA: Academic Press.

A current introduction to academic achievement, subsuming research on indicators and predictors of achievement as well as reasons for differences in education caused by gender and socioeconomic resources. The chapter provides further references on the topic.

Walberg, Herbert J. 1986. Syntheses of research on teaching. In Handbook of research on teaching . 3d ed. Edited by Merlin C. Wittrock, 214–229. New York: Macmillan.

A quantitative and qualitative aggregation of a variety of reviews and quantitative syntheses as an overview of early research on educational outcomes. Walberg found nine factors to be central to the determination of school learning.

Woolfolk, Anita. 2007. Educational psychology . 10th ed. Boston: Pearson.

Woolfolk represents a comprehensive basic work that is founded on an understandable and practical communication of knowledge. The perspectives of students as scholastic learners as well as teachers are the focus of attention. Suitable for undergraduate and graduate students. Currently presented in the 12th edition.

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ORIGINAL RESEARCH article

Academic achievement, self-concept, personality and emotional intelligence in primary education. analysis by gender and cultural group.

• 1 Department of Developmental and Educational Psychology, University of Granada, Melilla, Spain
• 2 Early Childhood and Primary Education School “Pedro de Estopiñán”, Melilla, Spain

A review of the scientific literature shows that many studies have analyzed the relationship between academic achievement and different psychological constructs, such as self-concept, personality, and emotional intelligence. The present work has two main objectives. First, to analyze the academic achievement, as well as the self-concept, personality and emotional intelligence, according to gender and cultural origin of the participants (European vs. Amazigh). Secondly, to identify what dimensions of self-concept, personality and emotional intelligence predict academic achievement. For this, a final sample consisting of 407 students enrolled in the last 2 years of Primary Education were utilized for the study. By gender, 192 were boys (47.2%) and 215 girls (52.8%), with an average age of 10.74 years old. By cultural group, 142 were of European origin (34.9%) and 265 of Amazigh origin (65.1%). The academic achievements were evaluated from the grades obtained in three school subjects: Natural Sciences, Spanish Language and Literature, and Mathematics, and the instruments used for data collection of the psychological constructs analyzed were the Self-Concept Test-Form 5, the Short-Form Big Five Questionnaire for Children, and the BarOn Emotional Quotient Inventory: Youth Version-Short. Based on the objectives set, first, the grades in the subject of Spanish Language and Literature varied depending on the gender of the students. Likewise, differences were found in self-concept, personality, and emotional intelligence according to gender. Also, the physical self-concept varied according to the cultural group. Regarding the second objective, in the predictive analysis for each of the subjects of the curriculum of Primary Education, the academic self-concept showed a greater predictive value. However, so did other dimensions of self-concept, personality and emotional intelligence. The need to carry out a comprehensive education in schools that addresses the promotion of not only academic but also personal and social competences is discussed. Also, that the study of the variables that affect gender differences must be deepened.

Introduction

A review of the scientific literature has shown that many studies have analyzed the relationship between academic achievement and different psychological constructs such as self-concept ( Susperreguy et al., 2018 ; Wolff et al., 2018 ; Sewasew and Schroeders, 2019 ), personality ( Janošević and Petrović, 2019 ; Perret et al., 2019 ; Smith-Woolley et al., 2019 ), and emotional intelligence ( Corcoran et al., 2018 ; Deighton et al., 2019 ; Piqueras et al., 2019 ). In this work, these psychological constructs are analyzed together with primary school children by gender and cultural group. Gender has been a highly studied variable since there are differences between boys and girls in academic performance as well as in the psychological constructs mentioned above ( Chrisler and McCreary, 2010 ; Voyer and Voyer, 2014 ; Carvalho, 2016 ; Herrera et al., 2017 ; Janošević and Petrović, 2019 ). There are also studies that analyze the possible differences that may exist in the school context between children from different cultures ( Schmitt et al., 2007 ; Strayhorn, 2010 ; Cvencek et al., 2018 ; Min et al., 2018 ). In this sense, there is a disadvantage in the school context for children of minority culture. The present study has been developed in Melilla, a Spanish city located in North Africa, close to Morocco. In their schools, children of European culture and children of Amazigh culture (also known as Berber) have been together from early childhood education. In addition, the predictive value of each of the dimensions that integrate self-concept, personality and emotional intelligence regarding the grades in three subjects of the Primary Education curriculum are analyzed. The psychological constructs analyzed in the present study are described below.

Self-Concept

Many research studies have highlighted that the psychological construction of a positive self-concept by the students, during their academic stage, leads to success in educational environments and social and emotional situations ( Eccles, 2009 ; Harter, 2012 ; Nasir and Lin, 2012 ; Chen et al., 2013 ). Therefore, the positive self-concept acquired in the formative years could help in the development of the strategies and skills needed for confronting life challenges ( Huang, 2011 ). It has also been found that self-concept is positively associated with different factors such as the individual experiencing greater happiness ( Hunagund and Hangal, 2014 ); a greater and better academic performance ( Salami and Ogundokun, 2009 ); greater and more pro-social behaviors ( Schwarzer and Fuchs, 2009 ); and lastly, an overall greater well-being ( Mamata and Sharma, 2013 ).

Among the different models that link self-concept and academic performance, we found the Reciprocal Effects Model (REM), with a theoretical, methodological and empirical review conducted by Marsh and Martin (2011) . This model argues that academic self-concept and performance mutually re-enforce themselves, with one producing advances in the other.

Starting with the evolution perspective, the Developmental Equilibrium Hypothesis has also been highlighted. The objective of this hypothesis is centered on achieving equilibrium between two factors that are directly related: self-concept and academic performance ( Marsh et al., 2016a , b ). Hence, achieving a state of equilibrium has important implications for the development of the individual, but it cannot be ignored that each individual’s development of self-concept is different depending on the personal, emotional, and social characteristics surrounding them ( Eccles, 2009 ; Murayama et al., 2013 ; Paramanik et al., 2014 ).

The studies that relate self-concept with school or academic performance are exhaustive in the first educational stages as well as higher education ( Guay et al., 2010 ; Möller et al., 2011 ; Skaalvik and Skjaalvik, 2013 ). The student’s self-concept, and the academic self-concept within it, has a strong influence on student self-efficacy ( Ferla et al., 2009 ). Additionally, academic self-concept significantly correlates with school adjustment in Primary Education ( Wosu, 2013 ; Mensah, 2014 ) and predicts academic achievement ( Marsh and Martin, 2011 ; Guo et al., 2016 ). Therefore, in this research it is expected to find such predictive value.

The results from cross-cultural studies have shown that a negative self-concept had detrimental effects on the academic performance of the students from the different samples and countries ( Marsh and Hau, 2003 ; Seaton et al., 2010 ; Nagengast and Marsh, 2012 ). Cvencek et al. (2018) , when analyzing primary school students from a minority group and a majority group in North America, found that the academic performance, as well as the academic self-concept of the children from the minority group, were lower as compared to those from majority group. Similar results that show the disadvantage of minority groups in schools are found in other studies ( Strayhorn, 2010 ). According to these results, it would be expected that in the present study children of Amazigh cultural origin obtained lower scores than those of European cultural origin in their academic performance and academic self-concept.

Another variable that has been analyzed along with self-concept and academic performance has been gender ( Chrisler and McCreary, 2010 ; DiPrete and Jennings, 2012 ). Thus, in the meta-analysis study by Voyer and Voyer (2014) , it was shown that a certain advantage in school performance existed in women, with their results showing differences in favor of the women for the Language subject. Differences according to gender were also found in self-concept ( Nagy et al., 2010 ). Huang (2013) , in a meta-analysis study, identified that the women had a greater self-concept in the subject matter or courses related to language, as well as the arts as compared to the men. Therefore, in this study we expect to find that girls obtain higher grades than boys in Spanish Language and Literature as well as academic self-concept.

Personality

In general terms, personality and self-concept predict satisfaction with life ( Parker et al., 2008 ). Also, personality moderates the effects of the frame of reference that are central for the shaping of self-concept ( Jonkmann et al., 2012 ).

Within the models of personality, the Five Factor Model ( McCrae and Costa, 1997 ) has been the most developed ( Herrera et al., 2018 ), and it represents the dominant conceptualization of the structure of personality in current literature. It postulates that the five great factors of personality (emotional instability, extraversion, intellect/imagination, agreeableness, and conscientiousness) are found at the highest level in the hierarchy of personality.

Among the strongest arguments utilized to show that the measurements of personality, based on the Big-Five Factor Structure ( Goldberg, 1990 , 1992 ), correlate with academic performance, we find the evidence that supports the importance of the personality factors to predict behaviors that are socially valued and the recognition of personality as a component of the individual’s will ( Chamorro-Premuzic et al., 2006 ). In this respect, the scientific literature shows studies that relate personality, through the five-factor model, with academic performance. Thus, agreeableness, and intellect/imagination (also known as openness) are related to academic performance ( Poropat, 2009 ; Smith-Woolley et al., 2019 ). Specifically, conscientiousness predicts academic achievement ( O′Connor and Paunonen, 2007 ), which is expected to be found in the present study.

Personality has been analyzed in different cultures ( Allik et al., 2012 ). A good example of a broad study, which included 56 countries, is the one conducted by Schmitt et al. (2007) . Among the main results, it was found that the five-factor structure of personality was robust among the main regions of the world. Also, the inhabitants from South America and East Asia were significantly different in their intellect/imagination characteristics as compared to the rest of the world regions. Thus, while the South American and European countries tended to occupy a higher position in openness, the cultures from East Asia were found in lower positions. This is attributed, among other factors, in that the Asian cultures are more collective, so that the openness dimension could be difficult to clearly identify, as proposed in the starting theoretical model. Based on these results, differences in personality dimensions are expected to be found among children of European and Amazigh cultural origin.

As for gender, differences have also been found. For example, the academic achievement in Primary Education is related to a higher conscientiousness in girls than in boys ( Janošević and Petrović, 2019 ).

Emotional Intelligence

Another factor that should be taken into account, as related to the academic achievements and school adjustment, is the emotional intelligence (EI). The models or theoretical approaches of EI are different ( Cherniss, 2010 ; Herrera et al., 2017 ). On the one hand, models have been identified that are based on the processing of emotional information, which are focused on basic emotional abilities ( Brackett et al., 2011 ). On the other hand, mixed models of EI have also been identified, which involve both intellectual and personality factors. The socio-emotional competence model by Bar-On (2006) forms part of the second group. In it, different dimensions are identified: intrapersonal, interpersonal, stress management, adaptability, and general mood.

Numerous research studies have examined the relationship between EI and academic performance ( Pulido and Herrera, 2017 ). They have also analyzed their relationship with other variables such as adjustment and permanence in the school context ( Hogan et al., 2010 ; Szczygieł and Mikolajczak, 2017 ), coping styles ( MacCann et al., 2011 ), the degree of social competence ( Franco et al., 2017 ), and school motivation ( Usán and Salavera, 2018 ).

Emotional intelligence has also been analyzed in groups with different ethnic or cultural origins ( Dewi et al., 2017 ; Min et al., 2018 ), and according to gender, differences were found in EI as well. Thus, for example, Herrera et al. (2017) obtained results that showed that girls in primary schools in Colombia exceeded the boys in the interpersonal dimension, while the boys stood out in the adaptability dimension. Similarly, Ferrándiz et al. (2012) identified that Spanish girls had higher scores in the interpersonal dimensions and the boys had higher scores in adaptability and general mood. Accordingly, we expect to find differences in emotional intelligence based on the cultural origin and gender of primary school children in this study.

As a function of what has been described until now, the present work has two main objectives. Firstly, to analyze the academic performance, as well as self-concept, personality and emotional intelligence, as a function of gender and cultural origin (European vs. Amazigh) of the participants. It is important to mention that the research study took place in the autonomous city of Melilla, a Spanish city that neighbors Morocco, with unique social, cultural and economic characteristics. In it, people from different cultures co-habit: European, Amazigh (also known as Berber, and who come from the Moroccan Rif), Sephardic and Hindu, although the majority of the population is of European and Amazigh descent and culture. The children with an Amazigh culture origin cohabit live and grow between their maternal culture, which counts with the Tamazight (a dialect that is orally transmitted) as a means of communication, and the European culture, with Spanish being the language employed at school and administrative environments of the city ( Herrera et al., 2011 ).

Secondly, to identify which dimensions of self-concept, personality and emotional intelligence predict academic performance.

In addition, different hypotheses are raised based on the results found in the scientific literature that addresses the research topics described above.

Hypothesis 1 . Academic grades differ depending on the gender and cultural origin of students. Thus, for example, as indicated by Voyer and Voyer (2014) , girls will achieve higher grades than boys in the subject of Spanish Language and Literature. Likewise, children of cultural origin different from the school (i.e., children of Amazigh culture) will obtain lower grades than Spanish children ( Strayhorn, 2010 ).

Hypothesis 2 . The psychology constructs evaluated (self-concept, personality and emotional intelligence) differ according to gender and cultural origin. Among other issues, it is expected to find that girls have a higher academic self-concept than boys ( Chrisler and McCreary, 2010 ), higher scores in the personality dimension of conscientiousness ( Janošević and Petrović, 2019 ) as well as in the interpersonal EI dimension ( Ferrándiz et al., 2012 ; Herrera et al., 2017 ). Likewise, children of European cultural origin are expected to obtain higher scores than those of Amazigh cultural origin in academic self-concept ( Cvencek et al., 2018 ), intellect/imagination ( Schmitt et al., 2007 ) and in the intrapersonal and interpersonal EI dimensions ( Dewi et al., 2017 ; Min et al., 2018 ).

Hypothesis 3 . Academic self-concept ( Marsh and Martin, 2011 ; Guo et al., 2016 ), conscientiousness ( O′Connor and Paunonen, 2007 ) and adaptability ( Hogan et al., 2010 ) predict academic achievement.

Materials and Methods

Participants.

A non-probabilistic sampling was used. Initially, 422 Primary school students were included in the research study. Nevertheless, once the non-valid cases were eliminated, defined as those who did not complete the evaluation instruments, or whose scores did not comply to what was set, the final sample was comprised of 407 students. These students were enrolled in eight of the twelve public early childhood and primary education centers in the autonomous city of Melilla, Spain (see Table 1 ), with a minimum age of 10 and a maximum of 12 years old. The description of the participants according to cultural origin, gender, grade and age is presented in Table 2 .

Table 1. Distribution of participants according to the center of early childhood and primary education.

Table 2. Distribution of participants according to cultural origin, gender, grade, and age.

The children of European cultural origin are mainly of Spanish nationality and Catholic religion. They were born in the autonomous city of Melilla or elsewhere in the Iberian Peninsula. Their parents were born in Melilla or have changed their residence to this city for professional reasons (mainly to work in public administration or in the army). Children of Amazigh cultural origin were born in the autonomous city of Melilla, so their nationality is Spanish, or they reside in that city. Many of them are Muslims and have family in Morocco so, given the short distance away, they usually travel at weekends or holidays to Moroccan cities close to Melilla. Rearing practices of children in families of each cultural group developed, among other things, based on cultural values and identities that define them. Thus, for example, the raising of children of Amazigh cultural origin is similar to that of children in the Rif region of Morocco. However, these same children socialize not only with children of their own cultural group but also with children of European cultural origin in a Spanish city, that is, the autonomous city of Melilla. The same can be indicated for children of European cultural origin.

Instruments

Academic achievement.

The final grades of the students of the school subjects Natural Sciences, Spanish Language and Literature, and Mathematics were obtained through a registry, provided by the student’s teachers. These were classified as insufficient (0–4.9 points), sufficient (5–5.9 points), good (6–6.9 points), notable (7–8.9 points) and outstanding (9–10 points).

A Self-Concept Test-Form 5 (AF-5, García and Musitu, 2001 ) was utilized. It is composed of 30 items that evaluate the self-concept of an individual in academic (e.g., “I do my homework well”), social (e.g., “I make friends easily”), emotional (e.g., “I am afraid of some things”), family (e.g., “I feel that my parents love me”) and physical (e.g., “I take good care of my physical health”) contexts. This form has to be answered according to an attributive scale ranging from 1 to 99, according to how the item adjusts to what the individual evaluated thinks of it. For example, if a phrase indicates “music helps human well-being” and the student strongly agrees, he/she would answer with a high number, such as 94. But if the student disagreed, he/she would choose a low number, for example 9. Esnaola et al. (2011) , when analyzing the psychometric properties of this test in the Spanish population from 12 to 84 years old, indicated that its total reliability was α = 0.74. The index of internal consistency, Cronbach’s alpha , calculated for the present work, had a value of α = 0.795. Also, its factorial or construct validity was corroborated in other research works ( Elosua and Muñiz, 2010 ; Malo et al., 2011 ).

For the evaluation of personality, the Short-Form Big Five Questionnaire for Children (S-BFQ-C, Beatton and Frijters, 2012 ) was utilized. It is based on the model of personality structured by five factors (Big-Five Factor Structure), formulated by Goldberg (1990 , 1992) . These factors are denominated as emotional instability (e.g., “I am often sad”), extraversion (e.g., “I make friends easily”), intellect/imagination (e.g., “When the teacher explains something, I understand immediately”), agreeableness (e.g., “I share my things with other people”) and conscientiousness (e.g., “During class I concentrate on the things I do”), creating the Big Five Questionnaire-Children (BFQ-C). This questionnaire, is directed at children aged between 9 to 15 years old, and was designed and validated by Barbaranelli et al. (2003) . In its initial version, its psychometric properties were analyzed with Italian children, although there are studies that have analyzed them in other populations such as for example the German ( Muris et al., 2005 ), Spanish ( Carrasco et al., 2005 ) or Argentinian ( Cupani and Ruarte, 2008 ) populations. Nevertheless, one of the problems of this instrument is its length, given that is composed by 65 items, 13 for each scale. This is the reason why Beatton and Frijters (2012) , in a broader study that sought to measure the effects of personality and satisfaction with life on the happiness of Australian youth aged from 9 to 14 years old, reduced the BFQ-C to a shorter version. This shorter version, named S-BFQ-C, is composed by 30 items, so that each of the scales is composed by 6 items. In this version, the questions have to be answered using a Likert -type scale with 5 response options (1 = Almost never; 5 = Almost always). The reliability, measured with Cronbach’s Alpha , was found to be between 0.60 and 0.80 for each of the five scales. For the present study, the total reliability found was α = 0.783.

The BarOn Emotional Quotient Inventory: Youth Version-Short (EQ-i: YV-S, Bar-On and Parker, 2000 ) was used. It is directed at children aged from 7 to 18 years old, and is composed of 30 items which have to be answered with a Likert scale with four possible responses (1 = Very seldom or Not true of me, 4 = Very often or True of me). Six items shape each of the following scales: intrapersonal (e.g., “It is easy to tell people how I feel”), interpersonal (e.g., “I care what happens to other people”), adaptability (e.g., “I can come up with good answers to hard questions”), stress management (e.g., “I can stay calm when I am upset”), and positive impression (e.g., “I like everyone I meet”). This last scale is useful for eliminating the cases of high social desirability. The sum of the first four scales provides the total EQ.

The reliability or internal consistency of the EQ-i YV-S scale oscillates between 0.65 and 0.87 ( Bar-On and Parker, 2000 ). For this study, the total reliability was α = 0.745. Its internal structure was confirmed in Spanish ( Esnaola et al., 2016 ), Hungarian ( Kun et al., 2012 ), Mexican ( Esnaola et al., 2018b ), English ( Davis and Wigelsworth, 2018 ) and Chinese ( Esnaola et al., 2018a ) populations.

Information Collection

In the first place, the participation of the management teams of the 12 early childhood and primary school education centers in Melilla was solicited. Of these, eight centers answered affirmatively. Afterward, within each center, the professor-tutor from each class or classes interested were contacted. A group meeting was conducted with the parents from each group-class, where information was provided about the objectives of the research study. The authorization of the children’s parents for the exclusive use of the results obtained, for educational and scientific purposes, was requested.

Once this process was finished, a document was provided to the teachers-tutors of each participating class which explained how to access the web program utilized for the management of the student’s grades in order to download this information in pdf format. Once this information was downloaded, they were asked to write down, in a double-entry table provided for each student, the final grades obtained in the subjects of Natural Sciences, Spanish Language and Literature, and Mathematics, using the scoring system of insufficient, sufficient, good, notable or outstanding. Teachers provided students’ grades to researchers at the end of the academic year.

The AF-5, the S-BFQ-C and the EQ-i: YV-S questionnaires were administered in the first school term to the students in fifth and sixth grade of Primary Education, collectively according to group-class. The maximum time provided for this was 55 min. Previously, the students were told that there were no right or wrong answers, and that they should answer with total sincerity, given that the test was anonymous. Also, that they should not write their name; and that what they were about to answer did not have any relation with the school grades; and lastly, that they should read the questions, and if they had any doubts (for example, not understanding a term), they should raise their hand so that the question could be resolved.

In order to be able to relate the results of the evaluation of the different psychological constructs and the academic grades, the teacher of each class assigned a number to each student. This number was recorded both in the grades provided by him/her and on the first page of each of the questionnaires administered.

Statistical Analysis of the Data

Before proceeding with the statistical analysis, from the 422 students who participated, it was determined if there were students who had not completed the three evaluation tests, and also if they obtained high scores in the positive impression scale of the EQ-i: YV-S. This resulted in the elimination of 15 individuals, resulting in a final sample of 407 students.

The statistical program IBM SPSS Statistics 23 was used to carry out the statistical analysis. Descriptive statistics were utilized to describe the data (frequencies, percentages, mean and standard deviation). In other words, to answer the first research objective and the first two hypotheses, two Analysis of variance (ANOVA) were performed in which the Academic achievement was used as the dependent variable in one case, and self-concept, personality and EI as dependent variables in the other. In both cases, the independent variables were gender (boy or girl) and cultural group (European vs. Amazigh). The effect size was calculated with the partial eta-squared as the post hoc test, through the use of the Bonferroni test.

To address the second objective and the third hypothesis, three multiple linear regression analysis (with the enter method) were conducted, in which each subject was introduced as the dependent variable, with the predictive variables being the different dimensions which comprised the self-concept, personality and EI constructs. To justify the method used, the non-autocorrelation of the data was determined, using the Durbin Watson test, and the non-existence of multicollinearity, through the Variance Inflation Factor.

Academic Achievement by Gender and Cultural Group

All the subjects had a maximum of five points, and were scored as: 1 = Insufficient, 2 = Sufficient, 3 = Good, 4 = Notable, 5 = Outstanding. The mean grade in Natural Sciences was 3.26 ( SD = 1.33), for Spanish Language and Literature it was 3.33 ( SD = 1.24) and in Mathematics, it was 3.19 ( SD = 1.25).

Academic achievement as a function of the student’s gender and cultural group is presented in Table 3 . The analysis of variance performed as a function of gender and cultural group showed that there were differences according to gender for the subject Spanish Language and Literature, F = 5.812, p = 0.016, Eta2p = 0.014, so that the girls obtained higher grades than the boys, t = 0.313, p = 0.016. No differences were found neither in Nature Sciences, F = 0.763, p = 0.383, Eta2p = 0.002, nor Mathematics, F = 1.692, p = 0.194, Eta2p = 0.004. On their part, no differences were found as a function of the cultural group, F Natural Sciences = 0.376, p = 0.540, Eta2p = 0.001; F Language and Literature = 0.565, p = 0.453, Eta2p = 0.001; F Mathematics = 0.576, p = 0.448, Eta2p = 0.001.

Table 3. Academic achievement by gender and cultural group.

Self-Concept, Personality and EI by Gender and Cultural Group

The analysis of variance results (see Supplementary Table S1 ) showed that there were significant differences as a function of gender for self-concept, more specifically in academic self-concept, with the girls achieving higher grades in post hoc comparisons using the Bonferroni test, t = 0.667, p = 0.007, and self-esteem, t = 1.139, p < 0.001, where the boys stood out. Likewise, differences were found in personality in favor of the girls within the conscientiousness, t = 1.136, p = 0.018, and agreeableness dimensions, t = 1.641, p = 0.001. Also, with respect to the EI, the girls had a higher score in the interpersonal scale, t = 1.016, p = 0.007, while the boys had a higher score in the stress management, t = 1.513, p < 0.001, and adaptability, t = 1.110, p = 0.008. Lastly, with respect to the analysis according to cultural group, there were only significant differences in the physical self-concept, with higher scores reached by the children of Amazigh cultural origin, t = 0.420, p = 0.036.

Predictive Value of the Different Dimensions Evaluated With Respect to Academic Achievement

In first place, a linear regression analysis was conducted, where the dependent variable was the subject Natural Sciences and the predictive variables were the five dimensions of the self-concept, the five dimensions from personality and the four dimensions from EI (see Table 4 ). The model was significant with values F = 11.003, p < 0.001. Likewise, the coefficient of determination was R 2 = 0.311 (adjusted R 2 = 0.282). Durbin–Watson’s d test showed that there was no auto-correlation in the data ( d = 1.583). Values of the Durbin Watson test between 1.5 and 2.5 indicate that the data are not correlated ( Durbin and Watson, 1951 ). Also, the Variance Inflation Factor (VIF) obtained values lower than 5, so multicollinearity was not present ( Berry and Feldman, 1985 ; Belsley, 1991 ).

Table 4. Regression analysis of the different dimensions analyzed with respect to the natural sciences subject.

In the order from greater to lesser predictive value, the dimensions were: academic self-concept, physical self-concept, intrapersonal, intellect/imagination, and family self-concept. The physical self-concept, as well as intrapersonal intelligence, negatively predicted the grades in Natural Sciences.

In second place, as related to the subject Spanish Language and Literature (see Table 5 ), the model was significant with values of F = 10.442, p < 0.001 and with a coefficient of determination of R 2 = 0.299, adjusted R 2 = 0.271. The data was not correlated ( d = 1.672) and no multicollinearity was found.

Table 5. Regression analysis of the different dimensions analyzed with respect to the Spanish language and literature subject.

Once again, the academic self-concept dimension had the greatest predictive value, followed by the physical self-concept, intrapersonal intelligence, and intellect/imagination dimensions. The negative predictions remained the same.

In third and last place, for the subject of Mathematics (see Table 6 ), the model had a statistical significance, as shown by F = 10.790, p < 0.001. The coefficient of determination obtained was R 2 = 0.306, adjusted R 2 = 0.278. The data was not correlated ( d = 1.600) and multicollinearity was not present.

Table 6. Regression analysis of the different dimensions analyzed with respect to the mathematics subject.

The predictive dimensions were academic self-concept, physical self-concept (in a negative manner), adaptability, intellect/imagination, and conscientiousness.

Based on the hypotheses set, first, the grades of the Spanish Language and Literature school subject varied depending on the gender of the students, which coincided with the results from other studies, which highlighted the girls’ higher grades ( Huang, 2013 ; Voyer and Voyer, 2014 ). In this regard, it could be argued that academic and social expectations are different depending on gender ( Voyer and Voyer, 2014 ). Likewise, the influence of socialization on the formation of gender behaviors must be taken into account in accordance with the cultural norms of masculinity and femininity ( Gibb et al., 2008 ). Gender differences in academic achievement remain between different countries, regardless of their political, economic or social equality ( Stoet and Geary, 2015 ). However, it is noteworthy that in adulthood women occupy fewer representations of political, economic and academic leadership than men.

Contrary to expectations ( Strayhorn, 2010 ; Whaley and Noël, 2012 ), children of Amazigh origin did not obtain lower grades than those of European origin. These results may be due to the fact that in the city of Melilla children of both cultures are educated from early childhood education in schools where the language used is Spanish. Thus, the academic performance at the end of Primary Education does not differ depending on the cultural origin of the students. However, it is necessary to show that early childhood teachers dedicate great efforts so that children of Amazigh cultural origin develop the linguistic skills necessary for the correct learning and use of the Spanish language ( Herrera et al., 2011 ). Therefore, hypothesis 1 is partially confirmed. That is, the results found indicate that academic achievement varies according to gender but not the cultural origin of the students.

Likewise, differences were found according to gender in self-concept, specifically in the academic self-concept and self-esteem; for personality, within the factors of conscientiousness and agreeableness; in addition to emotional intelligence, particularly in the interpersonal, stress management and adaptability scales. As for the differences found for self-concept according to gender ( Nagy et al., 2010 ), the results found for academic self-concept showed differences in favor of the girls ( Malo et al., 2011 ). Nevertheless, other factors should be taken into account, such as the academic responsibilities associated to school success and failure, given that, for example, the boys in Compulsory Secondary Education attribute their academic success to their skills, while the girls attribute them to their effort ( Inglés et al., 2012 ). As for emotional self-concept or self-esteem, the boys exceeded the girls ( Xie et al., 2019 ). Cross-cultural studies show that differences in self-esteem according to gender are maintained in different countries, although their magnitude differ according to the cultural differences found in the socioeconomic, sociodemographic, gender equality and cultural value indicators ( Bleidorn et al., 2016 ). In this respect, the emotion literacy programs, based on the development of emotional intelligence, could be a useful tool for the development of self-esteem ( Cheung et al., 2014 ).

As for the differences in the personality dimensions conscientiousness and agreeableness in favor of the girls, the results were in agreement with previous studies ( Rahafar et al., 2017 ; Janošević and Petrović, 2019 ). Within the differences in EI according to gender, the girls scored higher in the interpersonal scale, while the boys did so in stress management and adaptability ( Ferrándiz et al., 2012 ; Herrera et al., 2017 ). In this way, the girls showed competencies and skills that were higher than the boys in empathy, social responsibility, and interpersonal relationships. On the contrary, the boys stood out in stress tolerance and impulse control (stress management), as well as in reality-testing, flexibility, and problem-solving (adaptability). These differences, as a function of gender, could be due to cultural factors and family rearing practices differentiated as a function of gender ( Joseph and Newman, 2010 ).

Also, the physical self-concept varied according to the cultural origin, where children from the Amazigh culture obtained higher scores than children of European culture origin. This may be due to the influence of cultural values (their own, meaning Amazigh, as well as the context in which they live in, given that the children are socialized in a European context), with respect to body image and physical self-concept ( Marsh et al., 2007 ).

Based on the results found, the second hypothesis is partially confirmed. The three psychological constructs evaluated differ according to gender in the expected direction but only in the self-concept are differences found according to the cultural origin. Although it was expected to find differences in favor of children of European cultural origin in academic self-concept ( Cvencek et al., 2018 ), they have been found in physical self-concept in favor of children of Amazigh cultural origin. As previously indicated, children of European and Amazigh culture develop in the same school contexts from the early educational stages. Thus, educational policies developed in schools may have contributed to eliminating the possible socio-cultural disadvantages of children of Amazigh cultural origin. This implies, therefore, that there are no differences depending on the cultural group in the academic self-concept.

In the predictive analysis developed for each of the school subjects of the curriculum of Primary Education, with the aim of answering the second objective and the third hypothesis of the study, the academic self-concept showed a greater predictive value ( Marsh and Martin, 2011 ; Jansen et al., 2015 ; Guo et al., 2016 ; Lösch et al., 2017 ; Susperreguy et al., 2018 ). This result confirms the third hypothesis. That is, the relevance of academic self-concept in school performance. However, so did other dimensions of self-concept. More specifically, the physical self-concept negatively predicted the academic results in the three subjects evaluated ( Lohbeck et al., 2016 ). Children who participated in the study are in the process of transition from childhood to adolescence. Biological changes in their bodies due to this stage of evolutionary development as well as greater attention to appearance and physical abilities may interfere at the end of Primary Education in their academic performance. Furthermore, the family self-concept predicted the grades of the Natural Sciences school subject. This last result points to the influence of the family on self-concept as well as academic results ( Corrás et al., 2017 ; Mortimer et al., 2017 ; Häfner et al., 2018 ).

Personality also predicted the academic results in the three school subjects from the Primary Education curriculum analyzed ( O′Connor and Paunonen, 2007 ; Spengler et al., 2016 ; Bergold and Steinmayr, 2018 ), i.e., the intellect/imagination dimension for the three subjects and conscientiousness for Mathematics. In the first case, it may be because intellect/imagination or openness is a personality dimension that reflects cognitive exploration ( DeYoung, 2015 ). It refers to the ability and tendency to find, understand and use complex patterns of both sensory and abstract information. Therefore, those children who score higher in intellect/imagination will get better academic results than those with lower scores. In the second case, conscientiousness relates to responsibility, persistence, trustworthiness, and being purposeful ( Conrad and Patry, 2012 ). Children with high conscientiousness can develop a variety of effective learning strategies, which may be associated with higher academic performance in Mathematics.

Likewise, EI predicted academic achievement in every case ( Salami and Ogundokun, 2009 ; Hogan et al., 2010 ; Brackett et al., 2011 ; MacCann et al., 2011 ). More specifically, the intrapersonal scale predicted it for the subjects of Natural Sciences and Spanish Language and Literature. Intrapersonal intelligence involves the knowledge and labeling of one’s own feelings. This ability may contribute to achieving better grades in both subjects of the curriculum. For example, in the subject of Spanish Language and Literature it can facilitate the communicative skills related to the reading of different kinds of texts, their reflection and their understanding. On the other hand, in the subject of Nature Sciences it can contribute to interpret reality in order to address the solution to the different problems that arise, as well as to explain and predict natural phenomena and to face the need to develop critical attitudes before the consequences that result from scientific advances. In the case of the Mathematics subject, the adaptability scale predicted the academic achievement. Adaptability implies abilities such as being able to adjust one’s emotions and behaviors to changing situations or conditions, which is closely related to mathematical thinking.

In general, scientific literature shows that academic achievement is related to self-concept ( Susperreguy et al., 2018 ; Wolff et al., 2018 ; Sewasew and Schroeders, 2019 ), personality ( Perret et al., 2019 ; Smith-Woolley et al., 2019 ), and EI ( Corcoran et al., 2018 ; Deighton et al., 2019 ; Piqueras et al., 2019 ). Also, that within these construct, academic self-concept ( Ferla et al., 2009 ; Guay et al., 2010 ; Chen et al., 2013 ; Marsh et al., 2014 ), intellect/imagination ( Poropat, 2009 ; Smith-Woolley et al., 2019 ), and adaptability ( MacCann et al., 2011 ; Szczygieł and Mikolajczak, 2017 ) correlate significantly with academic achievement. In this research the predictive value of the dimensions of self-concept, personality and EI regarding the academic grades obtained in three subjects of the Primary Education curriculum has been established. One of its strengths is that it analyzes the predictive value of these psychological constructs together, not separately as in other studies.

In addition, the study has been developed in a multicultural context where children of European and Amazigh cultural origin coexist. Children of Amazigh cultural origin usually have access to early childhood education centers with a lower knowledge of the Spanish language than children of European cultural origin ( Herrera et al., 2011 ). Although studies carried out with groups of cultural minorities show differences in their school performance ( Strayhorn, 2010 ; Whaley and Noël, 2012 ), in the present study they are not at the end of Primary Education. This fact may be due to the linguistic policy developed in Melilla educational centers, which means that the mother language of children of Amazigh origin does not represent a disadvantage for academic achievement.

Further, gender differences found in the study seem to be more relevant than cultural differences. In fact, they are only in the physical self-concept in the latter case. Personality can mediate in adapting to school demands, so that girls are more conscientiousness than boys and follow norms in a more adaptive way ( Carvalho, 2016 ). Moreover, since girls excel in their academic self-concept, their self-efficacy may also be superior to that of boys, which contributes to a better school adjustment ( Ferla et al., 2009 ). Girls also have greater interpersonal intelligence, indicating better empathy, social responsibility and interpersonal relationships ( Ferrándiz et al., 2012 ). Such non-cognitive abilities can stimulate the development of positive interpersonal relationships in the classroom with both the teachers and their peers. These individual differences may be due to family and social influences where, for example, girls are expected to be more emotionally expressive than boys ( Meshkat and Nejati, 2017 ). In this same direction it could explain why children have greater self-esteem and stress management that girls.

Practical Implications for Education

In light of the results obtained in the present research study, the need to carry out a comprehensive education in schools that addresses the promotion of not only academic but also personal, social and emotional competences, are underlined ( Cherniss, 2010 ; Hunagund and Hangal, 2014 ; Herrera et al., 2017 ; Szczygieł and Mikolajczak, 2017 ; Corcoran et al., 2018 ; Cvencek et al., 2018 ). For this, the application of the principles derived from Positive Psychology in the education field would be an adequate strategy ( Suldo et al., 2015 ; Chodkiewicz and Boyle, 2017 ; Domitrovich et al., 2017 ; Shoshani and Slone, 2017 ). Thus, intellectual, procedural and emotional aspects have to be worked on in learning, the latter being clear drivers of learning. The pleasant emotions experienced by children in educational settings will allow greater happiness and emotional well-being in them ( Gil and Martínez, 2016 ). For it, teachers must be trained in good teaching practices that allow the interest of students to learn as well as guide them in the emotional domain ( Castillo et al., 2013 ; Oberle et al., 2016 ; Conners-Burrow et al., 2017 ).

Likewise, schools must respond to the gender and cultural differences of students ( Chrisler and McCreary, 2010 ; DiPrete and Jennings, 2012 ), particularly the first based on the results of this study. Thus, for example, the development of greater self-esteem in girls ( Bleidorn et al., 2016 ; Xie et al., 2019 ) should be encouraged. As indicated by Cheung et al. (2014) , emotional literacy programs that are based on emotional intelligence are an appropriate strategy for promoting self-esteem. Similarly, gender differences must be taken into account in response to other factors such as cultural group, family beliefs and parenting practices ( Chrisler and McCreary, 2010 ; Joseph and Newman, 2010 ; Nagy et al., 2010 ; Allik et al., 2012 ; Marsh et al., 2015 ).

Study Limitations and Proposal for Future Research

The present study has been developed taking into account only the last two school years of the education stage of Primary Education, just before the transition to Compulsory Secondary Education. Given that the scientific literature shows evolutionary changes in the development of the constructs analyzed ( Huang, 2011 ; Murayama et al., 2013 ; Marsh et al., 2015 ; Bleidorn et al., 2016 ), longitudinal studies could be conducted in future research studies from Primary Education to Compulsory Secondary Education in order to determine the magnitude and direction of these changes.

On the other hand, all the instruments for data collection used to evaluate the psychological constructs analyzed in the present study are based on self-report measures. Different types of measuring instruments (self-report measures and performance measures) should be combined in future studies ( Petrides et al., 2010 ; Mayer et al., 2012 ).

Gender differences in academic achievement as well as the psychological constructs analyzed have been revealed. However, it has to deepen the analysis of personal variables, family, social and cultural factors that contribute to that, even though women get better scores on their school performance across the different educational stages, at adulthood that reach fewer representations than men in leadership positions ( Stoet and Geary, 2015 ).

Finally, given the cultural diversity in schools it is necessary to develop studies that analyze academic achievement as well as its relationship with different psychological variables in students of different cultural groups. Cross-cultural studies comparing different countries are necessary ( Marsh and Hau, 2003 ; Nagengast and Marsh, 2012 ; Bleidorn et al., 2016 ; Min et al., 2018 ) but teachers have to know how to deal with coexistence and cultural diversity within the classrooms.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the Research Commission, Faculty of Educational Sciences and Sports, University of Granada, Melilla, Spain. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

LH, MA-L, and LM shared conception, design, and the final version of the work, were jointly accountable for the content of the work, ensured that all aspects related to accuracy or integrity of the study were investigated and resolved in an appropriate way, and shared the internal consistency of the manuscript. MA-L and LM contributions were mainly in the theoretical part and in revising it critically. LH contribution was mainly in methodological question and data analysis.

This research was co-financed by the Research Group Development, Education, Diversity, and Culture: Interdisciplinary Analysis (HUM-742).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.03075/full#supplementary-material

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Keywords : academic achievement, self-concept, personality, emotional intelligence, gender, cultural group

Citation: Herrera L, Al-Lal M and Mohamed L (2020) Academic Achievement, Self-Concept, Personality and Emotional Intelligence in Primary Education. Analysis by Gender and Cultural Group. Front. Psychol. 10:3075. doi: 10.3389/fpsyg.2019.03075

Received: 12 September 2019; Accepted: 28 December 2019; Published: 22 January 2020.

Reviewed by:

Copyright © 2020 Herrera, Al-Lal and Mohamed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lucía Herrera, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Approaching literature review for academic purposes: The Literature Review Checklist

Debora f.b. leite.

I Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR

II Universidade Federal de Pernambuco, Pernambuco, PE, BR

III Hospital das Clinicas, Universidade Federal de Pernambuco, Pernambuco, PE, BR

Maria Auxiliadora Soares Padilha

Jose g. cecatti.

A sophisticated literature review (LR) can result in a robust dissertation/thesis by scrutinizing the main problem examined by the academic study; anticipating research hypotheses, methods and results; and maintaining the interest of the audience in how the dissertation/thesis will provide solutions for the current gaps in a particular field. Unfortunately, little guidance is available on elaborating LRs, and writing an LR chapter is not a linear process. An LR translates students’ abilities in information literacy, the language domain, and critical writing. Students in postgraduate programs should be systematically trained in these skills. Therefore, this paper discusses the purposes of LRs in dissertations and theses. Second, the paper considers five steps for developing a review: defining the main topic, searching the literature, analyzing the results, writing the review and reflecting on the writing. Ultimately, this study proposes a twelve-item LR checklist. By clearly stating the desired achievements, this checklist allows Masters and Ph.D. students to continuously assess their own progress in elaborating an LR. Institutions aiming to strengthen students’ necessary skills in critical academic writing should also use this tool.

INTRODUCTION

Writing the literature review (LR) is often viewed as a difficult task that can be a point of writer’s block and procrastination ( 1 ) in postgraduate life. Disagreements on the definitions or classifications of LRs ( 2 ) may confuse students about their purpose and scope, as well as how to perform an LR. Interestingly, at many universities, the LR is still an important element in any academic work, despite the more recent trend of producing scientific articles rather than classical theses.

The LR is not an isolated section of the thesis/dissertation or a copy of the background section of a research proposal. It identifies the state-of-the-art knowledge in a particular field, clarifies information that is already known, elucidates implications of the problem being analyzed, links theory and practice ( 3 - 5 ), highlights gaps in the current literature, and places the dissertation/thesis within the research agenda of that field. Additionally, by writing the LR, postgraduate students will comprehend the structure of the subject and elaborate on their cognitive connections ( 3 ) while analyzing and synthesizing data with increasing maturity.

At the same time, the LR transforms the student and hints at the contents of other chapters for the reader. First, the LR explains the research question; second, it supports the hypothesis, objectives, and methods of the research project; and finally, it facilitates a description of the student’s interpretation of the results and his/her conclusions. For scholars, the LR is an introductory chapter ( 6 ). If it is well written, it demonstrates the student’s understanding of and maturity in a particular topic. A sound and sophisticated LR can indicate a robust dissertation/thesis.

A consensus on the best method to elaborate a dissertation/thesis has not been achieved. The LR can be a distinct chapter or included in different sections; it can be part of the introduction chapter, part of each research topic, or part of each published paper ( 7 ). However, scholars view the LR as an integral part of the main body of an academic work because it is intrinsically connected to other sections ( Figure 1 ) and is frequently present. The structure of the LR depends on the conventions of a particular discipline, the rules of the department, and the student’s and supervisor’s areas of expertise, needs and interests.

Interestingly, many postgraduate students choose to submit their LR to peer-reviewed journals. As LRs are critical evaluations of current knowledge, they are indeed publishable material, even in the form of narrative or systematic reviews. However, systematic reviews have specific patterns 1 ( 8 ) that may not entirely fit with the questions posed in the dissertation/thesis. Additionally, the scope of a systematic review may be too narrow, and the strict criteria for study inclusion may omit important information from the dissertation/thesis. Therefore, this essay discusses the definition of an LR is and methods to develop an LR in the context of an academic dissertation/thesis. Finally, we suggest a checklist to evaluate an LR.

WHAT IS A LITERATURE REVIEW IN A THESIS?

Conducting research and writing a dissertation/thesis translates rational thinking and enthusiasm ( 9 ). While a strong body of literature that instructs students on research methodology, data analysis and writing scientific papers exists, little guidance on performing LRs is available. The LR is a unique opportunity to assess and contrast various arguments and theories, not just summarize them. The research results should not be discussed within the LR, but the postgraduate student tends to write a comprehensive LR while reflecting on his or her own findings ( 10 ).

Many people believe that writing an LR is a lonely and linear process. Supervisors or the institutions assume that the Ph.D. student has mastered the relevant techniques and vocabulary associated with his/her subject and conducts a self-reflection about previously published findings. Indeed, while elaborating the LR, the student should aggregate diverse skills, which mainly rely on his/her own commitment to mastering them. Thus, less supervision should be required ( 11 ). However, the parameters described above might not currently be the case for many students ( 11 , 12 ), and the lack of formal and systematic training on writing LRs is an important concern ( 11 ).

An institutional environment devoted to active learning will provide students the opportunity to continuously reflect on LRs, which will form a dialogue between the postgraduate student and the current literature in a particular field ( 13 ). Postgraduate students will be interpreting studies by other researchers, and, according to Hart (1998) ( 3 ), the outcomes of the LR in a dissertation/thesis include the following:

• To identify what research has been performed and what topics require further investigation in a particular field of knowledge;
• To determine the context of the problem;
• To recognize the main methodologies and techniques that have been used in the past;
• To place the current research project within the historical, methodological and theoretical context of a particular field;
• To identify significant aspects of the topic;
• To elucidate the implications of the topic;
• To offer an alternative perspective;
• To discern how the studied subject is structured;
• To improve the student’s subject vocabulary in a particular field; and
• To characterize the links between theory and practice.

A sound LR translates the postgraduate student’s expertise in academic and scientific writing: it expresses his/her level of comfort with synthesizing ideas ( 11 ). The LR reveals how well the postgraduate student has proceeded in three domains: an effective literature search, the language domain, and critical writing.

Effective literature search

All students should be trained in gathering appropriate data for specific purposes, and information literacy skills are a cornerstone. These skills are defined as “an individual’s ability to know when they need information, to identify information that can help them address the issue or problem at hand, and to locate, evaluate, and use that information effectively” ( 14 ). Librarian support is of vital importance in coaching the appropriate use of Boolean logic (AND, OR, NOT) and other tools for highly efficient literature searches (e.g., quotation marks and truncation), as is the appropriate management of electronic databases.

Language domain

Academic writing must be concise and precise: unnecessary words distract the reader from the essential content ( 15 ). In this context, reading about issues distant from the research topic ( 16 ) may increase students’ general vocabulary and familiarity with grammar. Ultimately, reading diverse materials facilitates and encourages the writing process itself.

Critical writing

Critical judgment includes critical reading, thinking and writing. It supposes a student’s analytical reflection about what he/she has read. The student should delineate the basic elements of the topic, characterize the most relevant claims, identify relationships, and finally contrast those relationships ( 17 ). Each scientific document highlights the perspective of the author, and students will become more confident in judging the supporting evidence and underlying premises of a study and constructing their own counterargument as they read more articles. A paucity of integration or contradictory perspectives indicates lower levels of cognitive complexity ( 12 ).

Thus, while elaborating an LR, the postgraduate student should achieve the highest category of Bloom’s cognitive skills: evaluation ( 12 ). The writer should not only summarize data and understand each topic but also be able to make judgments based on objective criteria, compare resources and findings, identify discrepancies due to methodology, and construct his/her own argument ( 12 ). As a result, the student will be sufficiently confident to show his/her own voice .

Writing a consistent LR is an intense and complex activity that reveals the training and long-lasting academic skills of a writer. It is not a lonely or linear process. However, students are unlikely to be prepared to write an LR if they have not mastered the aforementioned domains ( 10 ). An institutional environment that supports student learning is crucial.

Different institutions employ distinct methods to promote students’ learning processes. First, many universities propose modules to develop behind the scenes activities that enhance self-reflection about general skills (e.g., the skills we have mastered and the skills we need to develop further), behaviors that should be incorporated (e.g., self-criticism about one’s own thoughts), and each student’s role in the advancement of his/her field. Lectures or workshops about LRs themselves are useful because they describe the purposes of the LR and how it fits into the whole picture of a student’s work. These activities may explain what type of discussion an LR must involve, the importance of defining the correct scope, the reasons to include a particular resource, and the main role of critical reading.

Some pedagogic services that promote a continuous improvement in study and academic skills are equally important. Examples include workshops about time management, the accomplishment of personal objectives, active learning, and foreign languages for nonnative speakers. Additionally, opportunities to converse with other students promotes an awareness of others’ experiences and difficulties. Ultimately, the supervisor’s role in providing feedback and setting deadlines is crucial in developing students’ abilities and in strengthening students’ writing quality ( 12 ).

HOW SHOULD A LITERATURE REVIEW BE DEVELOPED?

A consensus on the appropriate method for elaborating an LR is not available, but four main steps are generally accepted: defining the main topic, searching the literature, analyzing the results, and writing ( 6 ). We suggest a fifth step: reflecting on the information that has been written in previous publications ( Figure 2 ).

First step: Defining the main topic

Planning an LR is directly linked to the research main question of the thesis and occurs in parallel to students’ training in the three domains discussed above. The planning stage helps organize ideas, delimit the scope of the LR ( 11 ), and avoid the wasting of time in the process. Planning includes the following steps:

• Reflecting on the scope of the LR: postgraduate students will have assumptions about what material must be addressed and what information is not essential to an LR ( 13 , 18 ). Cooper’s Taxonomy of Literature Reviews 2 systematizes the writing process through six characteristics and nonmutually exclusive categories. The focus refers to the reviewer’s most important points of interest, while the goals concern what students want to achieve with the LR. The perspective assumes answers to the student’s own view of the LR and how he/she presents a particular issue. The coverage defines how comprehensive the student is in presenting the literature, and the organization determines the sequence of arguments. The audience is defined as the group for whom the LR is written.
• Designating sections and subsections: Headings and subheadings should be specific, explanatory and have a coherent sequence throughout the text ( 4 ). They simulate an inverted pyramid, with an increasing level of reflection and depth of argument.
• Identifying keywords: The relevant keywords for each LR section should be listed to guide the literature search. This list should mirror what Hart (1998) ( 3 ) advocates as subject vocabulary . The keywords will also be useful when the student is writing the LR since they guide the reader through the text.
• Delineating the time interval and language of documents to be retrieved in the second step. The most recently published documents should be considered, but relevant texts published before a predefined cutoff year can be included if they are classic documents in that field. Extra care should be employed when translating documents.

Second step: Searching the literature

The ability to gather adequate information from the literature must be addressed in postgraduate programs. Librarian support is important, particularly for accessing difficult texts. This step comprises the following components:

• Searching the literature itself: This process consists of defining which databases (electronic or dissertation/thesis repositories), official documents, and books will be searched and then actively conducting the search. Information literacy skills have a central role in this stage. While searching electronic databases, controlled vocabulary (e.g., Medical Subject Headings, or MeSH, for the PubMed database) or specific standardized syntax rules may need to be applied.

In addition, two other approaches are suggested. First, a review of the reference list of each document might be useful for identifying relevant publications to be included and important opinions to be assessed. This step is also relevant for referencing the original studies and leading authors in that field. Moreover, students can directly contact the experts on a particular topic to consult with them regarding their experience or use them as a source of additional unpublished documents.

Before submitting a dissertation/thesis, the electronic search strategy should be repeated. This process will ensure that the most recently published papers will be considered in the LR.

• Selecting documents for inclusion: Generally, the most recent literature will be included in the form of published peer-reviewed papers. Assess books and unpublished material, such as conference abstracts, academic texts and government reports, are also important to assess since the gray literature also offers valuable information. However, since these materials are not peer-reviewed, we recommend that they are carefully added to the LR.

This task is an important exercise in time management. First, students should read the title and abstract to understand whether that document suits their purposes, addresses the research question, and helps develop the topic of interest. Then, they should scan the full text, determine how it is structured, group it with similar documents, and verify whether other arguments might be considered ( 5 ).

Third step: Analyzing the results

Critical reading and thinking skills are important in this step. This step consists of the following components:

• Reading documents: The student may read various texts in depth according to LR sections and subsections ( defining the main topic ), which is not a passive activity ( 1 ). Some questions should be asked to practice critical analysis skills, as listed below. Is the research question evident and articulated with previous knowledge? What are the authors’ research goals and theoretical orientations, and how do they interact? Are the authors’ claims related to other scholars’ research? Do the authors consider different perspectives? Was the research project designed and conducted properly? Are the results and discussion plausible, and are they consistent with the research objectives and methodology? What are the strengths and limitations of this work? How do the authors support their findings? How does this work contribute to the current research topic? ( 1 , 19 )
• Taking notes: Students who systematically take notes on each document are more readily able to establish similarities or differences with other documents and to highlight personal observations. This approach reinforces the student’s ideas about the next step and helps develop his/her own academic voice ( 1 , 13 ). Voice recognition software ( 16 ), mind maps ( 5 ), flowcharts, tables, spreadsheets, personal comments on the referenced texts, and note-taking apps are all available tools for managing these observations, and the student him/herself should use the tool that best improves his/her learning. Additionally, when a student is considering submitting an LR to a peer-reviewed journal, notes should be taken on the activities performed in all five steps to ensure that they are able to be replicated.

Fourth step: Writing

The recognition of when a student is able and ready to write after a sufficient period of reading and thinking is likely a difficult task. Some students can produce a review in a single long work session. However, as discussed above, writing is not a linear process, and students do not need to write LRs according to a specific sequence of sections. Writing an LR is a time-consuming task, and some scholars believe that a period of at least six months is sufficient ( 6 ). An LR, and academic writing in general, expresses the writer’s proper thoughts, conclusions about others’ work ( 6 , 10 , 13 , 16 ), and decisions about methods to progress in the chosen field of knowledge. Thus, each student is expected to present a different learning and writing trajectory.

In this step, writing methods should be considered; then, editing, citing and correct referencing should complete this stage, at least temporarily. Freewriting techniques may be a good starting point for brainstorming ideas and improving the understanding of the information that has been read ( 1 ). Students should consider the following parameters when creating an agenda for writing the LR: two-hour writing blocks (at minimum), with prespecified tasks that are possible to complete in one section; short (minutes) and long breaks (days or weeks) to allow sufficient time for mental rest and reflection; and short- and long-term goals to motivate the writing itself ( 20 ). With increasing experience, this scheme can vary widely, and it is not a straightforward rule. Importantly, each discipline has a different way of writing ( 1 ), and each department has its own preferred styles for citations and references.

Fifth step: Reflecting on the writing

In this step, the postgraduate student should ask him/herself the same questions as in the analyzing the results step, which can take more time than anticipated. Ambiguities, repeated ideas, and a lack of coherence may not be noted when the student is immersed in the writing task for long periods. The whole effort will likely be a work in progress, and continuous refinements in the written material will occur once the writing process has begun.

LITERATURE REVIEW CHECKLIST

In contrast to review papers, the LR of a dissertation/thesis should not be a standalone piece or work. Instead, it should present the student as a scholar and should maintain the interest of the audience in how that dissertation/thesis will provide solutions for the current gaps in a particular field.

A checklist for evaluating an LR is convenient for students’ continuous academic development and research transparency: it clearly states the desired achievements for the LR of a dissertation/thesis. Here, we present an LR checklist developed from an LR scoring rubric ( 11 ). For a critical analysis of an LR, we maintain the five categories but offer twelve criteria that are not scaled ( Figure 3 ). The criteria all have the same importance and are not mutually exclusive.

First category: Coverage

1. justified criteria exist for the inclusion and exclusion of literature in the review.

This criterion builds on the main topic and areas covered by the LR ( 18 ). While experts may be confident in retrieving and selecting literature, postgraduate students must convince their audience about the adequacy of their search strategy and their reasons for intentionally selecting what material to cover ( 11 ). References from different fields of knowledge provide distinct perspective, but narrowing the scope of coverage may be important in areas with a large body of existing knowledge.

Second category: Synthesis

2. a critical examination of the state of the field exists.

A critical examination is an assessment of distinct aspects in the field ( 1 ) along with a constructive argument. It is not a negative critique but an expression of the student’s understanding of how other scholars have added to the topic ( 1 ), and the student should analyze and contextualize contradictory statements. A writer’s personal bias (beliefs or political involvement) have been shown to influence the structure and writing of a document; therefore, the cultural and paradigmatic background guide how the theories are revised and presented ( 13 ). However, an honest judgment is important when considering different perspectives.

3. The topic or problem is clearly placed in the context of the broader scholarly literature

The broader scholarly literature should be related to the chosen main topic for the LR ( how to develop the literature review section). The LR can cover the literature from one or more disciplines, depending on its scope, but it should always offer a new perspective. In addition, students should be careful in citing and referencing previous publications. As a rule, original studies and primary references should generally be included. Systematic and narrative reviews present summarized data, and it may be important to cite them, particularly for issues that should be understood but do not require a detailed description. Similarly, quotations highlight the exact statement from another publication. However, excessive referencing may disclose lower levels of analysis and synthesis by the student.

4. The LR is critically placed in the historical context of the field

Situating the LR in its historical context shows the level of comfort of the student in addressing a particular topic. Instead of only presenting statements and theories in a temporal approach, which occasionally follows a linear timeline, the LR should authentically characterize the student’s academic work in the state-of-art techniques in their particular field of knowledge. Thus, the LR should reinforce why the dissertation/thesis represents original work in the chosen research field.

5. Ambiguities in definitions are considered and resolved

Distinct theories on the same topic may exist in different disciplines, and one discipline may consider multiple concepts to explain one topic. These misunderstandings should be addressed and contemplated. The LR should not synthesize all theories or concepts at the same time. Although this approach might demonstrate in-depth reading on a particular topic, it can reveal a student’s inability to comprehend and synthesize his/her research problem.

6. Important variables and phenomena relevant to the topic are articulated

The LR is a unique opportunity to articulate ideas and arguments and to purpose new relationships between them ( 10 , 11 ). More importantly, a sound LR will outline to the audience how these important variables and phenomena will be addressed in the current academic work. Indeed, the LR should build a bidirectional link with the remaining sections and ground the connections between all of the sections ( Figure 1 ).

7. A synthesized new perspective on the literature has been established

The LR is a ‘creative inquiry’ ( 13 ) in which the student elaborates his/her own discourse, builds on previous knowledge in the field, and describes his/her own perspective while interpreting others’ work ( 13 , 17 ). Thus, students should articulate the current knowledge, not accept the results at face value ( 11 , 13 , 17 ), and improve their own cognitive abilities ( 12 ).

Third category: Methodology

8. the main methodologies and research techniques that have been used in the field are identified and their advantages and disadvantages are discussed.

The LR is expected to distinguish the research that has been completed from investigations that remain to be performed, address the benefits and limitations of the main methods applied to date, and consider the strategies for addressing the expected limitations described above. While placing his/her research within the methodological context of a particular topic, the LR will justify the methodology of the study and substantiate the student’s interpretations.

9. Ideas and theories in the field are related to research methodologies

The audience expects the writer to analyze and synthesize methodological approaches in the field. The findings should be explained according to the strengths and limitations of previous research methods, and students must avoid interpretations that are not supported by the analyzed literature. This criterion translates to the student’s comprehension of the applicability and types of answers provided by different research methodologies, even those using a quantitative or qualitative research approach.

Fourth category: Significance

10. the scholarly significance of the research problem is rationalized.

The LR is an introductory section of a dissertation/thesis and will present the postgraduate student as a scholar in a particular field ( 11 ). Therefore, the LR should discuss how the research problem is currently addressed in the discipline being investigated or in different disciplines, depending on the scope of the LR. The LR explains the academic paradigms in the topic of interest ( 13 ) and methods to advance the field from these starting points. However, an excess number of personal citations—whether referencing the student’s research or studies by his/her research team—may reflect a narrow literature search and a lack of comprehensive synthesis of ideas and arguments.

11. The practical significance of the research problem is rationalized

The practical significance indicates a student’s comprehensive understanding of research terminology (e.g., risk versus associated factor), methodology (e.g., efficacy versus effectiveness) and plausible interpretations in the context of the field. Notably, the academic argument about a topic may not always reflect the debate in real life terms. For example, using a quantitative approach in epidemiology, statistically significant differences between groups do not explain all of the factors involved in a particular problem ( 21 ). Therefore, excessive faith in p -values may reflect lower levels of critical evaluation of the context and implications of a research problem by the student.

Fifth category: Rhetoric

12. the lr was written with a coherent, clear structure that supported the review.

This category strictly relates to the language domain: the text should be coherent and presented in a logical sequence, regardless of which organizational ( 18 ) approach is chosen. The beginning of each section/subsection should state what themes will be addressed, paragraphs should be carefully linked to each other ( 10 ), and the first sentence of each paragraph should generally summarize the content. Additionally, the student’s statements are clear, sound, and linked to other scholars’ works, and precise and concise language that follows standardized writing conventions (e.g., in terms of active/passive voice and verb tenses) is used. Attention to grammar, such as orthography and punctuation, indicates prudence and supports a robust dissertation/thesis. Ultimately, all of these strategies provide fluency and consistency for the text.

Although the scoring rubric was initially proposed for postgraduate programs in education research, we are convinced that this checklist is a valuable tool for all academic areas. It enables the monitoring of students’ learning curves and a concentrated effort on any criteria that are not yet achieved. For institutions, the checklist is a guide to support supervisors’ feedback, improve students’ writing skills, and highlight the learning goals of each program. These criteria do not form a linear sequence, but ideally, all twelve achievements should be perceived in the LR.

CONCLUSIONS

A single correct method to classify, evaluate and guide the elaboration of an LR has not been established. In this essay, we have suggested directions for planning, structuring and critically evaluating an LR. The planning of the scope of an LR and approaches to complete it is a valuable effort, and the five steps represent a rational starting point. An institutional environment devoted to active learning will support students in continuously reflecting on LRs, which will form a dialogue between the writer and the current literature in a particular field ( 13 ).

The completion of an LR is a challenging and necessary process for understanding one’s own field of expertise. Knowledge is always transitory, but our responsibility as scholars is to provide a critical contribution to our field, allowing others to think through our work. Good researchers are grounded in sophisticated LRs, which reveal a writer’s training and long-lasting academic skills. We recommend using the LR checklist as a tool for strengthening the skills necessary for critical academic writing.

AUTHOR CONTRIBUTIONS

Leite DFB has initially conceived the idea and has written the first draft of this review. Padilha MAS and Cecatti JG have supervised data interpretation and critically reviewed the manuscript. All authors have read the draft and agreed with this submission. Authors are responsible for all aspects of this academic piece.

ACKNOWLEDGMENTS

We are grateful to all of the professors of the ‘Getting Started with Graduate Research and Generic Skills’ module at University College Cork, Cork, Ireland, for suggesting and supporting this article. Funding: DFBL has granted scholarship from Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) to take part of her Ph.D. studies in Ireland (process number 88881.134512/2016-01). There is no participation from sponsors on authors’ decision to write or to submit this manuscript.

No potential conflict of interest was reported.

1 The questions posed in systematic reviews usually follow the ‘PICOS’ acronym: Population, Intervention, Comparison, Outcomes, Study design.

2 In 1988, Cooper proposed a taxonomy that aims to facilitate students’ and institutions’ understanding of literature reviews. Six characteristics with specific categories are briefly described: Focus: research outcomes, research methodologies, theories, or practices and applications; Goals: integration (generalization, conflict resolution, and linguistic bridge-building), criticism, or identification of central issues; Perspective: neutral representation or espousal of a position; Coverage: exhaustive, exhaustive with selective citations, representative, central or pivotal; Organization: historical, conceptual, or methodological; and Audience: specialized scholars, general scholars, practitioners or policymakers, or the general public.

Literature Reviews

What is a Literature Review?

• Steps for Creating a Literature Review
• Providing Evidence / Critical Analysis
• Challenges when writing a Literature Review
• Systematic Literature Reviews

A literature review is an academic text that surveys, synthesizes, and critically evaluates the existing literature on a specific topic. It is typically required for theses, dissertations, or long reports and  serves several key purposes:

• Surveying the Literature : It involves a comprehensive search and examination of relevant academic books, journal articles, and other sources related to the chosen topic.
• Synthesizing Information : The literature review summarizes and organizes the information found in the literature, often identifying patterns, themes, and gaps in the current knowledge.
• Critical Analysis : It critically analyzes the collected information, highlighting limitations, gaps, and areas of controversy, and suggests directions for future research.
• Establishing Context : It places the current research within the broader context of the field, demonstrating how the new research builds on or diverges from previous studies.

Types of Literature Reviews

Literature reviews can take various forms, including:

• Narrative Reviews : These provide a qualitative summary of the literature and are often used to give a broad overview of a topic. They may be less structured and more subjective, focusing on synthesizing the literature to support a particular viewpoint.
• Systematic Reviews : These are more rigorous and structured, following a specific methodology to identify, evaluate, and synthesize all relevant studies on a particular question. They aim to minimize bias and provide a comprehensive summary of the existing evidence.
• Integrative Reviews : Similar to systematic reviews, but they aim to generate new knowledge by integrating findings from different studies to develop new theories or frameworks.

Importance of Literature Reviews

• Foundation for Research : They provide a solid background for new research projects, helping to justify the research question and methodology.

Identifying Gaps : Literature reviews highlight areas where knowledge is lacking, guiding future research efforts.

• Building Credibility : Demonstrating familiarity with existing research enhances the credibility of the researcher and their work.

In summary, a literature review is a critical component of academic research that helps to frame the current state of knowledge, identify gaps, and provide  a basis for new research.

The research, the body of current literature, and the particular objectives should all influence the structure of a literature review. It is also critical to remember that creating a literature review is an ongoing process - as one reads and analyzes the literature, one's understanding may change, which could require rearranging the literature review.

Paré, G. and Kitsiou, S. (2017) 'Methods for Literature Reviews' , in: Lau, F. and Kuziemsky, C. (eds.)  Handbook of eHealth evaluation: an evidence-based approach . Victoria (BC): University of Victoria.

Perplexity AI (2024) Perplexity AI response to Kathy Neville, 31 July.       

Royal Literary Fund (2024)  The structure of a literature review.  Available at: https://www.rlf.org.uk/resources/the-structure-of-a-literature-review/ (Accessed: 23 July 2024).

Library Services for Undergraduate Research (2024) Literature review: a definition . Available at: https://libguides.wustl.edu/our?p=302677 (Accessed: 31 July 2024).

Further Reading:

Methods for Literature Reviews

Literature Review (The University of Edinburgh)

Literature Reviews (University of Sheffield)

• How to Write a Literature Review Paper? Wee, Bert Van ; Banister, David ISBN: 0144-1647

• Next: Steps for Creating a Literature Review >>
• Last Updated: Sep 4, 2024 11:43 AM
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REVIEW OF ACADEMIC ACHIEVEMENT AND INFLUENCING FACTORS

Extensive research has been done, to study the factors influencing the Academic Achievement. Research has been going on, in the area of Academic Achievement for decades. The available literature is presented under the following subheadings.

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KEYWORDS Student's perspective, student performance, learning facilities and communication skills.w Management ABSTRACT Purpose-The study aims to investigate the factors influencing students' academic performance. Design/ Methodology/ Approach-This study uses ex post facto research design. An instrument measuring students' academic performance has been used to collect data from the management students. Findings-The study revealed that there is a positive and statistically significant impact of learning facilities, communication skills and proper guidance from parents on student academic performance. Originality Value-The study attempts to identify factors other than traditionally studied.

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Academic performance represents how well a student is achieving tasks and studies. This study show factors associated that affects academic performance of students such as; course, study habit, learning style, motivation, professor, and social factors. Based on the investigation of 42 astronomy students, this study analyses and evaluates the factors affecting academic performance of astronomy technology students and the relationship of their general weighted average to the rated factors. It shows that there are significant differences in the factors affecting academic performance. There is a marked negative relationship between the general weighted average and the factors affecting academic performance. It appears that students are optimistic in their academic performance, confident with their study habits, assertive with their learning styles, emphatic about their motivation, expresses more compassion in their studies compared to their social life, and their professors have shown professional, ethical and moral attitudes. The department should continue reinforcing students' reasoning and thinking skills especially if it is related to astronomy.

Sylvester Onyeka

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Katleho Makatjane

In this paper, the PLS-PM model has been estimated as to directly and indirectly identify factors that influence academic performance of the first year students at NUL. Sample used to utilise the task was 46. The estimated PLS-PM model was found stable and satisfying the SEM conditions. Several measure were established and found that 63% of variation of OWM is been explained by all those factors that are found to be significant. Also, seven factors were retained with factor loadings in the range of 0.4 to 0.81. Furthermore, the results of the discriminant analysis revealed that, 54% of female students are enrolled to the university while only 46% is for male students each year. 1. Introduction In all countries of the world, education is the most important sector of living; hence the major resources are plunged into it as an investment to human resource and the development of the country. The educational performance is influenced by various components including admission points, socio economic status and school foundation. Acato (2006) [1] ; Geiser and Santelices (2007) [18] all contend that admission points which are a reflection of the past performance has some impact on future performance of students. Tertiary institutions in Austria have found that a selection rank based on a student's overall performance is a predictor of success for most courses. As documented by Berthelot, Ross, and Tremblay (2001) [5] , the study agrees with the literature that admission points really distress the performance of university students and that is why the basic university entry admission points is a diploma points or mature age points. However, Berg (2012) [4] defines education as the conveyance of learning, aptitudes and information from teachers to students is lacking to capture what is truly vital about being and getting to be educated. Learning is taken to mean any change in behavior, knowledge, understanding, skills or capabilities which the greenhorn retains which cannot be ascribed simply to the physical growth or to the development of inherited behavior patterns. In the current study, two techniques are used to check two different issues. The first technique is the use of the structural equation model (SEM) through employment of the Partial Least Square Path Model (PLS-PM) to identify the factors that influences the academic performance of first year students at the National University of Lesotho (NUL) directly and indirectly. And lastly, in assessing the enrollment rate at the university, the K th nearest neighbor discriminant analysis with the discriminating factor as the sex structure of the student is engaged.

JOSEPHAT NCHUNGO

Ahmad Thawabieh

Aims: This study aimed to investigate the factors that affect students' achievement. Study Design: Quantitative descriptive & qualitative designs were employed in this study. Place and Duration of Study: The study was conducted in Tafila Technical University (TTU), Jordan, during Feb – May 2015. Methodology: The sample of the study consisted of 488 students (219 males and 269 females). The researcher used two methods to collect data; a questionnaire was developed to collect quantitative data, it consisted of 5 sections; the first section includes items for demographic information (gender, academic year, college and students' accumulated average). The other 4 sections were the questionnaire domains; each domain represents the achievement problems from students perspectives related to that domain; domain1 represents achievement problems related to students (10 items), domain 2: problems related to the faculties (7 items), domain 3: Problems related to courses (9 items), domain 4 problems related to test administration (13 items). In order to collect a qualitative data about factors affecting students' achievement, the researcher used focus group discussion (FGD). Results: The results indicated that the following factors affect students' achievement: courses, test administration, students, and faculties. The results indicated also statistical significant differences 2 (P = .05) attributed to gender on the achievement problems associated with test administration, courses and faculties; female students had higher mean in problems associated with courses and test administration, while male students were suffering more from problems associated with faculties. Finally, there are statistically significant differences (P = .05) attributed to colleges on the achievement problems associated with students and faculties; humanity college students have more problems related to students domain, while scientific colleges students have more problems associated with faculty domain. Conclusion: This study is aimed to determine the key factors that influencing students' achievement, the study showed that students' achievement was affected by the factors identified by the researcher; faculties, courses, students and test administration. Students vary in the degree of the effect of these factors according to their gender and the college they study in. The student performance would be improved if the academic institution leaders minimize the influence of the proposed factors and taking care of the psychological factors that influence students' achievement by increasing the role of counseling centers at the universities, providing better environment for assessing students' achievement, faculties must be more fair in assessing their students, Faculties Development Centers at Jordanian universities may need to focus on developing the methods of assessment that used by faculties, and faculties and administrators should advise the students about the factors that affect their achievement and how to overcome these factors. The academic achievement of the students depends on many factors; only 4 of them have been identified by this study. There may be other factors which may have a direct effect on students' achievement, such as; the influence of socioeconomic factors, teacher-student ratio, students attendance in the class, and mother and father education. Based on the findings of this study and in order to generalize the results, the researcher suggests that research should be extended to all Jordanian universities.

Arun Christopher T

Introduction In this era of globalization and technological revolution, education is considered as a first step for every human activity. It plays a vital role in the development of human capital and is linked with an individual's well-being and opportunities for better living (Battle & Lewis, 2002). It ensures the acquisition of knowledge and skills that enable individuals to increase their productivity and improve their quality of life. Hence school education plays a major part of every child's life. Academic Achievement is the focal point of school education system. Parents want their children to perform well leading to increased focus on academic achievement. Moreover, the quality of students' performance remains at top priority for government and educators. Researchers have long been interested in exploring variables contributing effectively for quality of performance of learners. These variables are inside and outside school that affect students' quality of academic achievement. Some factors may be termed as student factors, family factors, school factors and peer factors (Crosnoe, Johnson & Elder, 2004). This paper highlights some of the demographic factors viz. gender, family size, family income, parents' education, socioeconomic status (SES), and type of institution. Unfortunately, defining and measuring the quality of education is not a simple issue and the complexity of this process increases due to the changing values of quality attributes associated with the different stakeholders' view point (Blevins, 2009; Parri, 2006).

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Academic achievement is one of the essential elements that businesses examine when employing new graduates. This problem calls for intensive survey investigation to find out the Factors Affecting the Academic Performance of Grade 11 students at SKSU-Laboratory High School. This research was conducted to identify the factors affecting the academic performance of students at SKSU-Laboratory High School, which would help provide suggestions that would promote better academic performance that would interest the students in the study area. This study used a questionnaire for quantitative research instruments and has also used a customized method such as an online survey for the study. This study was conducted to explore the different factors that affect students’ academic performance. This study states that the students&#39; environment has excellent effects, enhancing or weakening their academic performance. There are various factors inside and outside school that contribute to the qual...

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Zhijie Wang

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I received my M.A. degree in Applied Linguistics from Fuzhou University, China. During my M.A. studies, I began exploring how translanguaging benefits bilingual and multilingual students in diverse educational settings, such as English as a Foreign Language (EFL) and English as a Medium of Instruction (EMI) classrooms. In 2024, I joined a doctoral program in Second Language Acquisition (SLA). Currently, I work as a Chinese teaching assistant in the Department of Asian Languages and Cultures, where I apply the benefits of translanguaging discovered in my research to my teaching practices. My main research interests focus on investigating translanguaging through the lens of positive psychology, with a primary goal of examining its impact on student engagement and achievement emotions in EFL learning.