academic procrastination literature review

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Academic interventions for academic procrastination: A review of the literature

Affiliation.

1 a Department of Psychology , Tel-Hai Academic College , Upper Galilee , Israel.
PMID: 29485384
DOI: 10.1080/10852352.2016.1198154

Procrastination is a widespread phenomenon in academic settings. It has been studied from many different theoretical angles, and a variety of causes and consequences have been suggested. Recent studies support the notion that academic procrastination can be seen from a situational perspective and as a failure in learning self-regulation. It suggests that interventions should address situational as well as deficits in self-regulation to help students overcome their procrastinating tendencies. The present review examined the recent literature on causes and consequences of academic procrastination and the limited number of studies of academic interventions for academic procrastination. Findings of this review strengthen the need to further study the topic of academic interventions for academic procrastination and to develop effective interventions. At the end of this review, several suggestions for the development of academic interventions are outlined.

Keywords: Academic interventions; academic procrastination; young college students.

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DOI: 10.1080/10852352.2016.1198154
Corpus ID: 3558145

Academic interventions for academic procrastination: A review of the literature

S. Zacks , Meirav Hen
Published in Journal of Prevention… 27 February 2018
Education, Psychology

90 Citations

And interventions.

Highly Influenced

A Systematic Review of Interventions to Reduce Academic Procrastination and Implications for Instructor-based Classroom Interventions

Academic procrastination in children and adolescents: a scoping review, change of academic procrastination by demographic characteristics: a research on university students, toward a holistic approach to reducing academic procrastination with classroom interventions, academic procrastination in the context of meaning structures of personality: why is it important for the formation of sustainable behavior, psychocentrum review, examining procrastination among university students through the lens of the self-regulated learning model, academic procrastination and academic performance: do learning disabilities matter, psychoeducation of academic procrastination in undergraduate students, 58 references, why students procrastinate: a qualitative approach, exploring reasons and consequences of academic procrastination: an interview study.

Highly Influential

An investigation of the efficacy of acceptance-based behavioral therapy for academic procrastination.

Academic procrastination, emotional intelligence, academic self-efficacy, and gpa, the relationship between procrastination and academic performance: a meta-analysis, reducing academic procrastination through a group treatment program: a pilot study, academic procrastination in college students: the role of self-reported executive function, comparing active delay and procrastination from a self-regulated learning perspective, why not procrastinate development and validation of a new active procrastination scale, academic procrastinators , strategic delayers and something betwixt and between : an interview study, related papers.

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Academic procrastination in children and adolescents: a scoping review.

1. Introduction

2.1. inclusion and exclusion criteria, 2.1.1. documents, 2.1.2. concept, 2.1.3. context, 2.1.4. studies, 2.1.5. participants, 2.1.6. evaluation, 2.2. search strategy, 2.3. sources of information, 2.3.1. formal strategies, 2.3.2. informal strategies, 2.3.3. retrospective strategies, 2.4. coding and identification of records and data extraction, 3.1. production and evolution of publications, 3.2. characteristics of the studies, 3.2.1. participants, 3.2.2. instruments for assessing academic procrastination, 3.2.3. methodology of the studies and type of design, 3.3. content analysis, 3.3.1. investigated correlates, 3.3.2. types of interventions, 4. discussion, 5. conclusions.

Addressing academic procrastination in children and adolescents should consider both individual and contextual factors, as well as appropriate interventions;
There is a need for the development of more appropriate assessment tools to measure academic procrastination in children and adolescents, considering their specific developmental characteristics;
The prevalence of academic procrastination in this population is still understudied, highlighting a research gap that requires further attention;
In summary, further research and interventions are necessary to improve the understanding and management of academic procrastination in children and adolescents.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

Section	Item	PRISMA-ScR Checklist Item	Reported on Page # (Number)
Title
Title	1	Identify the report as a scoping review.	1
Abstract
Structured summary	2	Provide a structured summary including, as applicable: Background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review question(s) and objective(s).	1
Introduction
Rationale	3	Describe the rationale for the review in the context of what is already known. Explain why the review question(s)/objective(s) lend themselves to a scoping review approach.	1–2
Objectives	4	Provide an explicit statement of the question(s) and objective(s) being addressed with reference to their key elements (e.g., population or participants, concepts, and context), or other relevant key elements used to conceptualize the review question(s) and/or objective(s)).	2
Methods
Protocol, and registration	5	Indicate if a review protocol exists, if and where it can be accessed (e.g., web address), and, if available, provide registration information including registration number.	3
Eligibility criteria	6	Specify the characteristics of the sources of evidence (e.g., years considered, language, publication status) used as criteria for eligibility, and provide a rationale.	3, 6
Information sources	7	Describe all information sources (e.g., databases with dates of coverage, contact with authors to identify additional sources) in the search, as well as the date the most recent search was executed.	4
Search	8	Present the full electronic search strategy for at least one database, including any limits used, in order that it could be repeated.	4
Selection of sources of evidence	9	State the process for selecting sources of evidence (i.e., screening, eligibility) included.	5
Data charting process	10	Describe the methods of charting data from the included sources of evidence (e.g., piloted forms, forms that have been tested by the team before their use, whether data charting was carried out independently, in duplicate) and any processes for obtaining and confirming data from investigators.	5
Data items	11	List and define all variables for which data were sought and any assumptions and simplifications made.	5
Critical appraisal of individual sources of evidence	12	If performed, provide a rationale for conducting a critical appraisal of included sources of evidence, describe the methods used, and how this information was used in any data synthesis (if appropriate).	N/A
Summary measures	13	Not applicable for scoping reviews.
Synthesis of results	14	Describe the methods of handling and summarizing the data that were charted.	5
Risk of bias across studies	15	Not applicable for scoping reviews.
Additional analyses	16	Not applicable for scoping reviews.
Results
Selection of sources of evidence	17	Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.	6
Characteristics of sources of evidence	18	For each source of evidence, present characteristics for which data were charted and provide the citations.	6, 7, 8, 10
Critical appraisal within sources of evidence	19	If performed, present data on critical appraisal of included sources of evidence (see item 12).
Results of individual sources of evidence	20	For each included source of evidence, present the relevant data that were charted that relate to the review question(s) and objective(s).
Synthesis of results	21	Summarize and/or present the charting results as they relate to the review question(s) and objective(s).	5, 6, 7, 8, 9
Risk of bias across studies	22	Not applicable for scoping reviews.
Additional analyses	23	Not applicable for scoping reviews.
Discussion
Summary of evidence	24	Summarize the main results (including an overview of concepts, themes, and types of evidence available), explain how they relate to the review question(s) and objectives, and consider the relevance to key groups.	11–12
Limitations	25	Discuss the limitations of the scoping review process.	13
Conclusions	26	Provide a general interpretation of the results with respect to the review question(s) and objective(s), as well as potential implications and/or next steps.	13
Funding
Funding	27	Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.	14

Bibliometric Information			Sample			Design
Author	Year	Doc. Type	Edu Level	N	Country	Design
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Kocoglu and Emiroglu [ ]	2017	Article	PS	31	TUR	RNCT
Shih [ ]	2017	Article	SS	405	TWN	EPF
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Click here to enlarge figure

Criteria	Inclusion	Exclusion
Documents (d)	Journal articles, books, book chapters, and doctoral theses	Magazine articles, editorials, conferences, etc.
Concept (Co)	Academic procrastination	The rest
Context (Co)	Academic context	Other contexts (e.g., general, work or health procrastination)
Studies (s)	Empirical studies	Theoretical reviews and case studies
Participants (p)	Students under 18 years	University students or community samples
Evaluation (e)	Behavioral or reported procrastination measures (i.e., self-reports or hetero reports)	Single-item instruments or unstructured instruments

Instrument	Count
Procrastination Assessment Scale-Student—PASS (Solomon and Rothblum, 1984 [ ])	23
Academic Procrastination Scale—AP-S (Çakıcı, 2003 [ ])	10
Tuckman Procrastination Scale—TPS (Tuckman, 1991 [ ])	8
Aitken Procrastination Inventory—API (Aiken, 1982 [ ])	5
General Procrastination Scale—GPS (Lay, 1986 [ ])	5
Escala de Procrastinación Académica—EPA (Busko, 1998 [ ])	4
Academic Procrastination Questionnaire (Huang, 2009 [ ])	2
Scale developed by authors (Dietz et al., 2007 [ ])	3
Academic Procrastination Scale—APS-S (McCloskey, 2011 [ ])	2
Academic Procrastination Inventory for Middle School Students—API-MSS (Zuo, 2020 [ ])	1
Academic Procrastination Questionnaire (Ran, H., 2010 [ ])	1
Academic Procrastination Scale—MSLQ (Lay and Silverman, 1996 [ ])	1
Academic Procrastination Scale—APS (Lay, 1986 [ ])	1
Academic Procrastination Student Form—APS (Milgram and Amir, 1998 [ ])	1
Academic Procrastination Survey (Savari, K., 2011 [ ])	1
Cuestionario de Procrastinación en el Estudio (CPE; Rosário et al., [ ])	3
Irrational Procrastination Scale—IPS (Steel, 2010 [ ])	1
Melbourne Decision Making Questionnaire (five items procrastination) (Mann et al., 1997 [ ])	1
Scale developed by authors (Depreeuw and Lens, 1998 [ ])	2
Scale developed by authors (Santyasa et al., 2020 [ ])	1
Scale developed by authors (Shih, 2016 [ ])
Scale developed by authors (Ocak and Karatas, 2019 [ ])	1
Academic Procrastination Scale—DPS (Ferrari et al., 1995 [ ])	1
Total	79

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González-Brignardello, M.P.; Sánchez-Elvira Paniagua, A.; López-González, M.Á. Academic Procrastination in Children and Adolescents: A Scoping Review. Children 2023 , 10 , 1016. https://doi.org/10.3390/children10061016

González-Brignardello MP, Sánchez-Elvira Paniagua A, López-González MÁ. Academic Procrastination in Children and Adolescents: A Scoping Review. Children . 2023; 10(6):1016. https://doi.org/10.3390/children10061016

González-Brignardello, Marcela Paz, Angeles Sánchez-Elvira Paniagua, and M. Ángeles López-González. 2023. "Academic Procrastination in Children and Adolescents: A Scoping Review" Children 10, no. 6: 1016. https://doi.org/10.3390/children10061016

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REVIEW article

How study environments foster academic procrastination: overview and recommendations.

$\r\nFrode Svartdal*$

1 Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway
2 Evaluation of Studies and Teaching and Higher Education Research, University of Cologne, Cologne, Germany
3 Department of Psychology, Paderborn University, Paderborn, Germany

Procrastination is common among students, with prevalence estimates double or even triple those of the working population. This inflated prevalence indicates that the academic environment may appear as “procrastination friendly” to students. In the present paper, we identify social, cultural, organizational, and contextual factors that may foster or facilitate procrastination (such as large degree of freedom in the study situation, long deadlines, and temptations and distractions), document their research basis, and provide recommendations for changes in these factors to reduce and prevent procrastination. We argue that increased attention to such procrastination-friendly factors in academic environments is important and that relatively minor measures to reduce their detrimental effects may have substantial benefits for students, institutions, and society.

Procrastination, voluntarily delaying tasks despite expecting to be worse off ( Steel, 2007 ), is common among students. Conservative estimates indicate that at least half of all students habitually procrastinate tasks that are important to them, such as reading for exams, writing term papers, and keeping up with weekly assignments ( Solomon and Rothblum, 1984 ; Tice and Baumeister, 1997 ; Pychyl et al., 2000 ; Schouwenburg, 2004 ; Steel, 2007 ). Consequences are negative, both for academic performance and retention ( Ellis and Knaus, 1977 ; Klassen et al., 2008 ; Zarick and Stonebraker, 2009 ; Grau and Minguillon, 2013 ; Kim and Seo, 2015 ) as well as for health and well-being ( Flett et al., 1995 ; Tice and Baumeister, 1997 ; Stöber and Joormann, 2001 ; Sirois, 2014 ).

Despite the possibility that academic environments may contribute significantly to this situation, the majority of research efforts to clarify mechanisms involved in procrastination has focused on individual variables related to personality, motivation, affect, and others (for reviews, see van Eerde, 2003 ; Steel, 2007 ; Klingsieck, 2013 ). The present paper takes a different view, focusing on situational, social, contextual, cultural, and organizational factors common in academic environments. Based on the procrastination literature, we present a selection of such factors and show how they increase the probability of procrastination. Negative effects may be general in that most students suffer. Often, however, “procrastination-friendly” factors may also affect students differentially, those being prone to procrastination in the first place being particularly vulnerable (e.g., Nordby et al., 2017 ; Visser et al., 2018 ). Thus, ideas on how to address these factors to make the academic environment more “procrastination- un friendly” are important.

We identify nine broad factors known to increase procrastination. The factors selected serve as important examples rather than an exhaustive list. For each factor, we link it to common features of academic environments, providing examples and other forms of documentation to demonstrate its significance in facilitating procrastination. We then formulate specific advice on how the negative influence of each factor may be alleviated or remedied by relatively simple structural, organizational, and educational measures.

Characteristics of Academic Procrastination

Academic procrastination occurs when a student delays work related to academic tasks ( Solomon and Rothblum, 1984 ; Tice and Baumeister, 1997 ; Pychyl et al., 2000 ; Schouwenburg, 2004 ; Steel, 2007 ). For such delays to be regarded as procrastination, the student voluntarily chooses to delay despite expecting to be worse off ( Steel, 2007 ). Thus, there is an important distinction between delays that are sensible and rational (e.g., “I chose to postpone my thesis submission because my supervisor advised me to revise the discussion part”) and those that are not (e.g., “I did not prepare for the seminar today, I watched a movie instead”). In effect, academic procrastination is a form of irrational delay, as the person acts against better judgment.

The delays seen in academic procrastination may result from late onset (e.g., “I did not start writing until just one week before deadline”) and impulsive diversions during work (e.g., “I was working, but got tired and had a coffee with a friend instead”) ( Svartdal et al., 2020 ). As is well documented in the research literature over the past 40 years, such delays and diversions are related to personality factors, as for example impulsiveness and a preference for short-term gratification, deficiencies in planning and self-regulation, low self-efficacy, tiredness, and low energy, and task avoidance ( van Eerde, 2000 ; Steel, 2007 ; Steel et al., 2018 ). The majority of this research has been correlational. Because procrastination is a complex phenomenon unfolding over time and in interaction with situational, social, contextual, cultural, and organizational factors, it is important also to focus on exogenous factors involved in this complex and dynamic phenomenon. The relative lack of such studies is unfortunate and clearly represents a gap in the procrastination field. We argue that this is particularly unfortunate in the academic area, as the student is confronted with situational, social, contextual, cultural, and organizational factors that are prone to instigate and maintain procrastination in tasks that constitute core student activities.

How Is Academic Procrastination Measured?

Academic procrastination is typically measured with self-report tools, as is general procrastination. In measuring academic procrastination, some scales focus on general tendencies to delay tasks unnecessarily, with few if any items covering academic tasks specifically. For example, the General Procrastination Scale (20 items; Lay, 1986 ), academic version, has 16 items common with the general version and four items addressing academic tasks specifically (e.g., Item 2, “I do not do assignments until just before they are to be handed in”). Similarly, the Tuckman procrastination scale (16 items; Tuckman, 1991 ) measures academic procrastination solely by general items (e.g., item 1 “I needlessly delay finishing jobs, even when they’re important”). Other academic procrastination scales focus on academic tasks exclusively, such as the Academic Procrastination State Inventory (APSI; Schouwenburg, 1995 ) and the Procrastination Assessment Scale (PASS; Solomon and Rothblum, 1984 ). The PASS contains 44 questions that address various forms of academic tasks (e.g., studying for an exam, writing a term paper) in terms of how often they are procrastinated, to which extent such procrastination represents a problem, and willingness to change.

Importantly, scores on academic procrastination scales have been validated against procrastination in real academic tasks. For example, Tuckman compared scores on his scale against actual performance points on voluntary homework assignments, where students had the opportunity to write and submit written material to gain extra course credits. He found a negative correlation, r =−0.54, between these measures, concluding that “students are well aware of their own tendencies and can report them with great accuracy” (p. 9). More recent findings (e.g., Tice and Baumeister, 1997 ; Steel et al., 2018 ) confirm a relatively close correspondence between students’ self-reported procrastination and relevant behavioral measures.

Detrimental Effects of Academic Procrastination

It is important to recognize that procrastination is not only an issue related to effective academic work. Although performance (grades) is negatively related to procrastination (for review, see Kim and Seo, 2015 ), other important problems associated with procrastination are stress, reduced well-being, and mental and physical health problems (e.g., Tice and Baumeister, 1997 ). For academic procrastination, the increased stress associated with procrastination seems to be important (e.g., Sirois, 2007 , 2014 ). Recognition of the procrastination problem as a health issue, as well as a performance issue, is imperative. In Norway, as well as in other European countries, surveys of student health indicate that an increasing number of students report psychological problems, often of serious nature. For example, in a large-scale survey among Norwegian students, the Students’ Health and Wellbeing Study ( Knapstad et al., 2018 ; N = 50,000), 29% of all students reported serious psychological problems. We do not know the role of procrastination in this situation, but it is likely that procrastination may be a contributing factor as well as a consequence. Hence, the role of the environmental factors in encouraging procrastinating is important to assess from a health perspective also.

Social and Contextual Factors Facilitating Procrastination

Rationale for selection of factors.

In the sections to come, we address situational, social, contextual, cultural, and organizational factors that are documented as facilitators of procrastination. In selection of factors, the authors first discussed a larger pool of factors and evaluated their relation to the academic situation. Then, based on expert judgment, we selected nine factors that met the following criteria: They (a) reflect well-documented research findings in the procrastination field; (b) represent factors present in the academic situation beyond the student’s control (e.g., long deadlines), or factors that cannot easily be remedied by the student independently of educational, social, or organizational measures (e.g., task aversion); and that (c) measures taken to change the factor is likely to reduce procrastination. The discussion of each factor is not intended as a complete review, as a review at this stage of research would be premature. Rather, for each factor, we highlight central findings connecting the factor to procrastination research, its relation to the academic environment, and remedies that may alleviate the detrimental effects associated with a given factor. Table 1 presents an overview of the factors discussed.

Table 1. Factors reliably associated with procrastination, and their relation to the study environment.

Note that the factors are quite heterogeneous. Some factors (e.g., large degree of freedom in the study situation, long deadlines) identify organizational and structural properties of the academic environment, whereas others emphasize subjective evaluations (e.g., task aversiveness). Also note that the factors discussed may demonstrate “main effects” as most students may be affected, as well as interactive effects where individual characteristics act as moderators. For example, temptations and distractions in the academic environment may be detrimental for most students, but particularly so for individuals high in impulsivity and distractibility (e.g., Steel et al., 2018 ). Furthermore, the order of factors discussed does not indicate differences in importance. In fact, the effect sizes associated with each factor may be difficult to quantify in academic contexts. Finally, a caution on the use of the term “factor.” We use this term to denote facets or variables in the academic settings that identify features known to relate strongly to procrastination. As these are exogenous factors in the procrastination equation, they represent potential conditions that can be altered in order to affect the probability of procrastination. In the present context, we do not make strong assumptions about causality; rather, we argue that such potential causal relations should receive increased attention in future research.

Large Degree of Freedom in the Study Situation

Relevant research.

In his comprehensive review of research on procrastination, Steel (2007) coined procrastination a quintessential self-regulatory failure. Procrastinators are present-oriented and impulsive and tend to score low on tests measuring conscientiousness and planning, and high on susceptibility to temptation ( Lay and Schouwenburg, 1993 ; van Eerde, 2003 ; Steel, 2010 ). Procrastinators make plans, only to reverse them when encountering distractions and temptations during goal implementation ( Steel et al., 2018 ). Hence, procrastinators are particularly vulnerable when working under unstructured conditions and when long-term plans are delegated to the individual.

Relation to the Academic Environment

Results from qualitative studies exemplify the negative role of freedom in the study situation in several ways, as too little regulations in studies ( Grunschel et al., 2013 ), low degree of external structure ( Klingsieck et al., 2013 ), or insufficient direction of lecturers ( Patrzek et al., 2012 ). Overall, students reported feeling lost and overwhelmed by the task of planning a whole course of studies, a semester, or even an exam phase on their own. Thus, students lacking self-management skills such as planning and prioritizing tasks (e.g., Lay and Schouwenburg, 1993 ) and metacognitive learning strategies (e.g., Wolters, 2003 ; Howell and Watson, 2007 ) should feel particularly lost when facing a situation with a large degree of freedom. The autonomy associated with a large degree of freedom in the study situation makes the student particularly vulnerable if skills are low (→Low focus on study skills training) and if the student fails to develop good habits and routines. Habits help people accomplish more and procrastinate less (e.g., Steel et al., 2018 ). Of note, study topics may vary in how much freedom they offer to the student. Some study programs are strictly structured and may even involve a common study group from start to finish (e.g., medicine), whereas other study topics are less structured and may also, by the nature of their contents, appear as more “procrastination friendly” (e.g., Nordby et al., 2017 ).

While direct procrastination prevention and intervention programs train the self-management skill of students (for a summary, see van Eerde and Klingsieck, 2018 ), remedies should also be implemented on the level of study programs and the level of courses. Especially for beginning students, unnecessary options present opportunities for students to procrastinate and should be accompanied by remedial measures. For example, Ariely and Wertenbroch (2002) compared student performance under no-choice fixed working schedules determined by the teacher versus choice working schedules (the students could determine their own schedules) and found that performance was better when students had to follow the no-choice fixed working schedules. If possible, a detailed syllabus including a “timetable” of the course, all deadlines, expected learning outcomes, and resources such as literature can help downsize the large degree of freedom of a study situation (cf. Eberly et al., 2001 ). Concerning the study program, an orientation event in the first semester or even each semester might support students in seeing the program’s inherent structure. One should not only focus on the contents of the program but also on the best way to run through the program. An individual twist to the orientation could be a short workshop in which each student is encouraged to plan her or his semester, thereby downsizing the large degree of freedom by establishing a unique structure which, ideally, should take into account all other activities they wish to make time for (e.g., sports, family, job), as well. Teaching styles that support student autonomy ( Codina et al., 2018 ) may also be helpful. Finally, note that a large degree of freedom in the study situation is not alleviated by the introduction of more external control. Indeed, procrastination research demonstrates that external control is associated with increased procrastination (e.g., Janssen and Carton, 1999 ). We argue instead that unnecessary freedom should be reduced, as in the Ariely and Wertenbroch (2002) study discussed.

Long Deadlines

The idea of hyperbolic discounting helps to explain why we procrastinate the start of an activity. For example, according to the Temporal Motivation Theory (TMT; Steel and König, 2006 ; Gröpel and Steel, 2008 ), motivation increases as a function of the expectancy of an outcome and the size or value of a goal, but decreases as the time span before this outcome lengthens and impulsiveness increases. Thus, procrastination is more likely to occur if the outcome of an activity offers rewards in the distant future, and more so if impulsiveness is high (as is the case in procrastinators). Hence, immediate temptations often come to dominate over distant rewarding goals.

Results from qualitative ( Schraw et al., 2007 ) and quantitative studies ( Tice and Baumeister, 1997 ; Schouwenburg and Groenewoud, 2001 ) support the idea that the tendency to procrastinate decreases as the deadline for the task in question is approaching. Students find tentative due dates as especially frustrating ( Schraw et al., 2007 ). In the absence of deadlines, students often set deadlines for themselves. Although such deadlines may work to reduce procrastination, they may actually reduce performance ( Ariely and Wertenbroch, 2002 ). Other research, focusing on planning, has demonstrated that individuals tend to underestimate the necessary time it takes to complete tasks (the planning fallacy; Kahneman and Tversky, 1979 ; Kahneman and Lovallo, 1993 ) and to prefer longer deadlines when allowed to choose ( Solomon and Rothblum, 1984 ). Recently, Zhu et al. (2019) demonstrated that long deadlines induce an inference of the focal task as more difficult, thereby making the student to allocate more time and resources to the task. However, the downside is that such elevated resource estimates may induce longer intention-action gaps (time before starting the task) and higher likelihood of quitting.

While students with a broad range of self-management skills are able to deal with long and tentative deadline by breaking distant goals into nearer sub-goals themselves, students who lack these skills would benefit from structural arrangements defining sub-goals with timely deadlines. For instance, having students hand in an outline for a paper after the first third of the semester, the first draft after the second third, and the final draft at the end of the semester help to break a distant goal down to nearer sub-goals. Ideally, this scaffolding of self-regulating learning and writing might function as a model for future tasks with long deadlines. In general, making goals proximate (e.g., in the form of sub-goals) may help the student increase performance and reduce procrastination (e.g., Steel et al., 2018 ). Also, as reviewed by Gollwitzer and Sheeran (2006) , adapting specific implementation intentions (“if-then”-plans rather than overall goal intentions) may have a strong effect on goal attainment. When students experience difficulties in goal striving, focusing on the main obstacle hindering progress is recommended (mental contrasting; e.g., Duckworth et al., 2011 ).

Task Aversiveness

Procrastination can be understood as a form of short-term mood-regulation ( Sirois and Pychyl, 2013 ). Bad mood and negative feelings associated with a task is often repaired by avoiding the task and engaging in a pleasant task instead. The role of task aversiveness in triggering procrastination has received strong support (for a summary, see Steel, 2007 ). Closer examination of the task aversiveness literature demonstrates that aversive tasks are characterized by lower autonomy, lower task significance, boredom, resentment, frustration, and difficulty ( Milgram et al., 1988 ; Milgram et al., 1995 ; Blunt and Pychyl, 2000 ; Steel, 2007 ). Moreover, Lay (1992) found that procrastinators tend to perceive common tasks in everyday life as more aversive compared to non-procrastinators, suggesting that procrastinators face the world with a negative bias toward task execution in general. As aversive conditions tend to motivate negatively by avoidance or escape, passivity is a likely effect ( Veale, 2008 ). In sum, working under negative motivation is common in procrastinators, and a negative motivational regime is associated with passivity.

As study-related tasks typically are imposed by others (teachers, exams), they represent an important part of the academic environment for students. Such conditions are known to induce aversiveness and thereby procrastination. For example, when applying the Procrastination Assessment Scale-Students ( Solomon and Rothblum, 1984 ), one prominent dimension turns out to be aversiveness of task . Time sampling as well as daily logs also show that the more students dislike a task, the more they procrastinate ( Steel, 2007 ). Results of qualitative interview studies support these findings ( Grunschel et al., 2013 ; Klingsieck et al., 2013 ; Visser et al., 2018 ).

Why students perceive academic tasks as aversive may be traced to the fact that students entering the university often lack adequate study skills to successfully managing mastery tasks 1 . Considering academic writing, for example, The Stanford Study of Writing indicates that, for most writers, the transition from high school to college writing is enormously challenging ( Rogers, 2008 ). Moreover, university students report a variety of problems associated with academic writing (e.g., being aware of not being able to meet expected standards; Achieve Inc., 2005 ). In the last decades, universities have addressed the need for training academic writing by implementing writing centers. However, as discussed in another section (→Low focus on study skills training), instruction covering study skills is rarely provided. Thus, students often perceive academic tasks as aversive due to their lack of perceived competence. This effect may be amplified by low academic self-efficacy commonly seen in new students. Academic self-efficacy is negatively correlated to procrastination ( r = −0.44; van Eerde, 2003 ), indicating that procrastinators perceive academic tasks as even more difficult (and therefore more aversive) compared to others. Indeed, a recent study 2 found that students perceive academic tasks (e.g., present at a seminar) as more aversive compared to non-academic tasks (e.g., clean one’s apartment), but for both categories, aversiveness scores correlated positively with dispositional procrastination scores.

The Self-Determination Theory ( Deci and Ryan, 2002 ) suggests that tasks and conditions which meet a learner’s need for autonomy, competence, and relatedness support the internalization of extrinsic regulations and values, which in turn makes the task less aversive. Learners are more likely to internalize a learning goal if they embrace the meaningfulness or rationale of a task or activity if the underlying task or activity promotes their feeling of competence and if they are able to connect with other learners and experience a feeling of relatedness. Thus, formulating meaningful learning goals that lead to learning activities that fit the students’ competence level will make the task less aversive. Carefully crafted group tasks (→Inefficient group work) can also reduce procrastination. These kinds of tasks should foster the self-determination of learners. If one then embeds the learning activities in realistic learning settings, learners might even get interested in the learning activity. Game-based learning provides an innovative possibility for learning settings ( Breuer and Bente, 2010 ). Finally, as discussed elsewhere (→Low focus on study skills training), programs for students entering the university should not shy away from offering training even in the most basic study skills.

Temptations and Distractions

Individuals are tuned toward attainment of positive outcomes and escape from or avoidance of aversive events. In procrastinators, this picture is exaggerated, with current attractive and aversive events dominating over distant ones. Procrastinators tend to be impulsive and present-biased ( van Eerde, 2003 ; Steel, 2007 ), scoring high on scales measuring susceptibility to temptation, distractibility, and impulsivity ( Steel et al., 2018 ). In fact, the correlation between distractibility and procrastination is very high, r = 0.64–0.72. Thus, procrastinators are especially vulnerable to environments with an abundance of temptations and distractors, as such environments tend to capture attention and divert planned behavior into more pleasurable activities available here and now. When working with aversive tasks (→Task aversiveness), this tendency increases, as the student will be motivated to escape the aversive situation as well as divert to something attractive ( Tice et al., 2001 ).

Academic environments offer a large number of temptations and distraction, Internet access being a prime example (e.g., Reinecke and Hofmann, 2016 ). Mobile phones and laptops may have internet access everywhere on campus, presenting a continuous temptation and distractor, even during lectures. Universities tend to rely on web-based information and registration systems, and there is an increasing emphasis on digital utilities designed to assist learning, all necessitating continuous Internet access. The downside is that this situation presents a continuous challenge to students, especially those low in self-control ( Panek, 2014 ). Internet use has often been shown to conflict with other goals and obligations ( Quan-Haase and Young, 2010 ; Reinecke and Hofmann, 2016 ), and Lepp et al. (2015) demonstrated that total usage of mobile phones among undergraduates is negatively related to academic performance. Procrastination implies that the individual spends less time on focal tasks ( Lay, 1992 ), and time spent on distracting tasks add to the problems procrastinators already experience. Internet multitasking (accessing the Internet while doing something else) is positively correlated with procrastination ( Reinecke et al., 2018a , b ), indicating that procrastinators are especially prone to suffer when Internet access remains unrestricted.

Intervention studies ( Hinsch and Sheldon, 2013 ) have demonstrated that reduction in leisure-related Internet use results in decreased procrastination and increased life satisfaction. Hence, limiting the availability of Internet use is a simple way of reducing these problems. Several companies practice restriction on use of mobile phones/laptops during meetings, and universities may consider similar measures. Universities may arrange wifi-free zones for teaching and studying, and teachers may ask students to turn off their laptops/phones during classes. For many, such advice may seem counterintuitive, as the use of “modern technology” in education is generally welcomed. However, given the detrimental effects associated with unrestricted Internet use seen in the part of the student population struggling with procrastination (i.e., half or more of all students), our advice is clear.

Limited Information for Proper Self-Monitoring

In self-regulated activities, three factors are particularly important for students ( Baumeister and Heatherton, 1996 ): The student must have some standard to aim for (e.g., obtain a good grade in a course), monitor progress toward this standard, and correct as necessary if progress deviates from what is necessary to reach the standard. Although all three factors are important, Baumeister and Heatherton (1996 , p. 56) pointed out that monitoring is crucial: “Over and over, we found that managing attention was the most common and often the most effective form of self-regulation and that attentional problems presaged a great many varieties of self-regulation failure.” As procrastination is considered a prime example of a self-regulation failure ( Steel, 2007 ), it is likely that managing attention when working toward long-term goals is particularly vulnerable in procrastinators.

Due to the large degree of freedom in the study situation, the successful student needs information to keep an updated track of status, given long-term plans. Unfortunately, the study situation typically provides limited information. In many cases, exams (often held at the end of the semester) are the main source of feedback for students. Other kinds of information on progress (e.g., time spent at the university, participation in classes, observation of other students) may be unreliable as indicators of being on track. Furthermore, as consequences of procrastination are positive in the short term but not so in the longer term, learning is biased in favor of immediate positive consequences, and corrective action from long-term negative consequences is less likely.

Measures that reflect goal-striving according to plan should be implemented. From the institutional/teacher perspective, such measures should focus on reading plans, course progress, and submissions, and should not be mixed up with study performance (e.g., grades). For example, as procrastination is a reliable predictor of study effort, high procrastinators spending less time in self-directed work ( Lay, 1992 ; Svartdal et al., 2020 ), actual time spent on self-directed studying may be relevant information for many. Self-testing, recommended as an effective learning strategy (→Low focus on study skills training), also assists self-monitoring. Activity diaries, inspired by behavioral activation for depression interventions (e.g., Jacobson et al., 2001 ), may increase students’ awareness of how they spend their time as students. In recent years, several mobile apps have been developed to help students keep track of how they spend their time in the study situation (e.g., Dute et al., 2016 ), but little is known about the effect such apps may have in reducing procrastination.

Low Focus on Study Skills Training

In a qualitative study, Grunschel et al. (2013) found that students reported a lack of study skills as a notable reason for academic procrastination. One likely explanation is that low skills make tasks more effort demanding, and individuals are more likely to procrastinate on effort-demanding tasks ( Milgram et al., 1988 ). Low academic skills also make academic tasks more frustrating, boring, and difficult, which are also factors reliably associated with task aversiveness ( Blunt and Pychyl, 2000 ). As discussed in another section, task aversiveness is a reliable predictor for procrastination (→Task aversiveness).

A large part of academic work is spent on self-directed learning, and the skills needed to properly maneuver in such an environment is essential for student success ( Kreber et al., 2005 ). Unfortunately, most students have not received instruction on effective and timely study skills (e.g., Dunlosky et al., 2013 ; Dunlosky and Rawson, 2015 ), and universities are slow in implementing effective skills instruction ( Goffe and Kauper, 2014 ; Wieman and Gilbert, 2015 ). Teachers’ knowledge of effective study strategies is also lacking ( Morehead et al., 2016 ; Blasiman et al., 2017 ).

Study skill training programs produce beneficial effects in terms of academic performance and retention ( Hattie et al., 1996 ; Gettinger and Seibert, 2002 ; Robbins et al., 2004 ; Wibrowski et al., 2017 ). Moreover, studies point out that learning how to study effectively cannot be separated from course contents and the process of learning ( Weinstein et al., 2000 ; Durkin and Main, 2002 ; Wingate, 2007 ). That is, study skills training should be tailored for study programs or courses. They should suit the instructional context and teaching practices, expected achievement outcomes, and promote a high degree of learner activity. However, the impact of such skill learning interventions diminishes over time ( Wibrowski et al., 2017 ), suggesting that repetition may be crucial. Thus, dedicating a portion of instruction time or having a study skill seminar at the beginning of each semester or course may be a good strategy. Different interventions may be considered depending on the course tasks ( Schraw et al., 2007 ), students’ abilities and performance level ( Hattie et al., 1996 ). Furthermore, as knowledge of study skills are not automatically translated into good study habits, academic self-efficacy (see next section) is important for circumventing procrastination ( Klassen et al., 2008 ).

Lack of Self-Efficacy-Building Opportunities

Self-efficacy, our belief in our ability to manage a task, influences how willing we are to take on domain-specific challenges. The higher self-efficacy, the more likely we will take on a task ( Bandura and Schunk, 1981 ). Even when ability to perform a task is high, but self-efficacy for that ability is low, the likelihood of prioritizing the task goes down, and procrastination is likely ( Haycock et al., 1998 ; Klassen et al., 2008 ). Importantly, the relation between self-efficacy and procrastination is relatively strong and negative, r = −0.44 ( van Eerde, 2003 ).

Self-efficacy is one of the strongest predictors of academic performance ( Klomegah, 2007 ), yet is often neglected in course instruction. We have long known that students develop their self-efficacy for any academic task by gradually increasing proficiency with it ( Bandura, 1997 ). Furthermore, as self-efficacy tends to be context-specific and will not automatically transfer over different tasks or activities ( Zimmerman and Cleary, 2006 ), a relatively broad set of on efficacy-building experiences, course by course, is necessary (→Lack of study skill training), though not necessarily enough on its own ( Kurtovic et al., 2019 ). Other research has recently indicated that self-efficacy may be indirectly rather than directly related to academic procrastination ( Li et al., 2020 ), and that self-efficacy for self-regulation, for example, may be a strong predictor ( Zhang et al., 2018 ).

To improve self-efficacy, instructors can create more opportunities for mastery experiences by breaking down course assignments into manageable bits that are not too easy but still are possible for students to succeed at ( Bandura, 1997 ), and by helping students self-reflect on their performance such that they feel more self-efficacious in the forethought phase of subsequent work ( Zimmerman, 2000 ). As self-efficacy increases, and the likelihood of engaging in a task goes up ( Ames, 1992 ), anxiety goes down ( Haycock et al., 1998 ), establishing a virtuous circle of self-efficacy instead of a vicious circle of procrastination ( Wäschle et al., 2014 ). This can be done through in-class activities or short assignments where the goal is to scaffold student learning with positive feedback and concrete information for how to improve on increasingly challenging versions of the task ( Tuckman and Schouwenburg, 2004 ).

Inefficient Group Work

Students often work in groups (e.g., discussion groups, seminars), but often lack the basic skills for making group work effective. Group work also increases the probability of social loafing, the tendency for individuals to demonstrate less effort when working collectively than when working individually ( Karau and Williams, 1993 ). Students may therefore often prefer to work alone as an alternative. However, working alone is associated with increased procrastination ( Klingsieck et al., 2013 ). Qualitative evidence suggests that group work with interdependence between group members may reduce academic procrastination ( Klingsieck et al., 2013 ). In support, results from educational psychology have shown positive effects of interdependent group work on individual effort in settings of cooperative learning. These studies also demonstrate beneficial effects of interdependence on social support, self-esteem, and health outcomes of group members ( Johnson and Johnson, 2002 , 2009 ). Taken together, these findings indicate the potential benefit of group work with interdependence, which may be harnessed in educational settings to reduce academic procrastination.

Although the beneficial effects of student group work in higher education seem evident ( Springer et al., 1999 ; Johnson and Johnson, 2002 ), group work is neglected in curricula of many study programs, leading students to work individually on tasks and assignments and thus possibly promoting procrastination. Students in such programs may not always feel inclined to form study groups on their own and create more favorable group work conditions instead. This is especially unfortunate as methods and tools for group learning and studying abound.

Group work with interdependence may be well suited to reduce procrastination among group members. Implementing group work with interdependence should be quite straightforward, for example by having groups work on projects or by adapting individual assignments to become interdependent tasks. The latter can be achieved by designing subtasks that need to be completed sequentially by assembling groups in such a way that each member contributes unique skills, or by formulating group-level goals and rewards ( Weber and Hertel, 2007 ).

Influence of Peers

Prior research has indicated quite complex findings regarding the role of peers in facilitating or inhibiting procrastination (e.g., Nordby et al., 2017 ). Of the different ways in which peers may influence procrastination, three factors seem to be particularly important: social norms, observational learning, and distraction. Harris and Sutton (1983) suggested that an organization’s norms can either encourage or discourage procrastination, depending on whether norms suggest a prompt or delayed processing of tasks. Observational learning can support acquisition, inhibition, and triggering of many types of human behavior ( Bandura, 1985 ), including procrastination. Thus, learning from others may also influence procrastination as well as strategies against it.

With regard to social norms, Ackerman and Gross (2005) found less procrastination among students when perceived norms suggested to start promptly. Social learning of procrastination or strategies against it have not been demonstrated empirically. However, on a more general level, observational learning has been shown to influence students’ self-regulatory skills (e.g., Zimmerman and Schunk, 2004 ). Indirect support for this notion also comes from Klingsieck et al. (2013) and Nordby et al. (2017) , who report that peer behavior is taken into account by procrastinating students. With regard to social distraction, an early study reported peer influence to be a possible, yet not very frequent reason for procrastination ( Solomon and Rothblum, 1984 ). Both qualitative ( Klingsieck et al., 2013 ) and quantitative ( Chen et al., 2016 ) evidence support the idea that distraction by peers can be a source of academic procrastination. A lack of social integration has also been reported an antecedent of academic procrastination ( Patrzek et al., 2012 ), suggesting a balanced judgment on the role of peers and social contacts.

Communication of social norms to start tasks promptly can occur through regular class instruction, thus supporting timely beginning of students with a disposition to procrastinate. Social cognitive theory predicts that social learning is facilitated, among others, by the salience of both model behavior and vicarious reinforcements ( Bandura, 1985 ). Letting students reflect on and share their experiences with procrastination and strategies against it may support more productive observational learning.

This paper discusses nine factors characteristic of student study environments that, singly and in combination, increase the probability of procrastination. Clearly, given the high prevalence of academic procrastination, it is important to have an increased awareness of such risk factors and how they can be handled in order to prevent and reduce procrastination. Although we cannot control what students do, we can control how institutions encourage more productive behaviors for student success. We now briefly discuss how policymakers, universities, teachers, and students should approach these issues.

Do the Factors Point to Common Problem Areas?

Yes. We argue that the nine factors discussed can be loosely grouped into three themes (see Figure 1 ). First, four or five of the factors discussed (i.e., long deadlines, large degree of freedom in the study situation, temptations and distractions, poor self-monitoring information, and low focus on skills training), while being contextual and situational in nature, all relate directly to students’ ability to effectively self-regulate in the study situation. In effect, our overview indicates that the core problem of procrastination, poor self-regulation ( Tice et al., 2001 ; Steel, 2007 ; Hagger et al., 2010 ), is amplified by common aspects of the student environment. An important implication of this insight is that training in self-regulation techniques among students (which we recommend) should not only be tailored to the specific needs of the students (cf. Valenzuela et al., 2020 ) but should also be supplemented with specific contextual and organizational measures that can support productive self-regulation. Since it is well known that self-regulation in the academic setting is important for performance (e.g., Duckworth and Seligman, 2005 ), it is paradoxical that academic institutions organize academic student life in ways counter to this insight.

Figure 1. How procrastination-friendly factors relate to important themes in education.

Note that the problems in self-regulation seen in procrastination episodes may relate to skills factors (e.g., planning, monitoring), speaking for relevant skills training to strengthen self-regulation. However, often factors that undermine effective self-regulation are of primary importance in procrastination (e.g., Tice et al., 2001 ). For example, low energy and tiredness may render the individual more vulnerable to task-irrelevant temptations and distractions and increase task aversiveness, which in turn increases the probability of procrastination ( Tice et al., 2001 ; Baumeister and Tierney, 2011 ). Insufficient sleep, common in the student population (e.g., Lund et al., 2010 ), is an important source of low energy and tiredness. Importantly, Knapstad et al. (2018) found that the most frequently reported health problem (as measured by the Somatic Symptoms Scale, SSS-8; Gierk et al., 2014 ) among a large sample of Norwegian students was a “Feeling of tiredness and low energy,” 45% of the students indicating that they were “fairly much or “very much” affected. This suggests that factors that undermine self-regulation among students should receive increased attention.

Second, the academic context can be designed to redress the skills and motivational issues that are often associated with procrastination. Low focus on study skills training and relative lack of efficacy-building opportunities represent a problematic combination that may themselves contribute to students perceiving academic tasks as aversive, thereby increasing the probability of procrastination. All these combined represent a disadvantageous motivational regime for academic work. The present overview identified specific organizational measures that institutions can take to change this situation. As discussed, increased focus on study skills training in concert with regular teaching may be a solution, as repeated mastery experiences will build self-efficacy as well as reduce task aversion.

Third, we should address the social factors that distract students from their academic work. By acknowledging that procrastination is a trap for students working alone, more opportunities can be made to encourage more collaborative work with others. It is important to carefully design group work in that it resembles interdependent group work. Furthermore, group work with student peers can be deliberately designed to increase student accountability, facilitating more need for self-regulation and offering students the opportunity to observe others with more productive self-regulation skills.

Given the Large Number of Factors Discussed, Are Some Particularly Important?

We have not attempted to identify effect sizes to each of the variables discussed, and for many such estimates do not exist. Comparing the factors is, therefore, extremely difficult. Further, as several of the factors discussed have been linked to procrastination in correlational research, causality must be inferred with caution. Nevertheless, all the factors discussed have potentially large causal power to instigate and sustain procrastination. Overall, the factors examined focus on larger problem areas (i.e., self-regulation, skills and motivation, social factors), but each factor identifies concrete measures to be considered to implement changes.

In approaching such factors, all should ask: What can be changed on my part? Several of the factors (e.g., large degree of freedom in the study situation, long deadlines, temptations and distractions) address organizational and educational issues that should be addressed by organizations and teachers. Others (e.g., task aversiveness) imply more complex instructor-student interactions. For example, negative emotions in task aversiveness should be approached by teachers and students in cooperation by reducing task-associated risks and imbuing the tasks with personal relevance ( van Grinsven and Tillema, 2006 ; Rowe et al., 2015 ), by enabling and encouraging student ownership of learning tasks ( Rowe et al., 2015 ), and by facilitating frequent successful learning experiences that increase self-efficacy.

Does It Make Sense to Implement Changes in One or Few Factors, Leaving Out Others?

Given an abundance of factors discussed, each capable of instigating procrastination, the high occurrence of procrastination in the student population is not at all surprising. Would it help, then, to change one or perhaps a few factors? One possible answer is that focusing on one factor is better than doing nothing. However, the downside of such an approach is that this single factor may not generate noticeable changes alone. Our recommendation would rather be to evaluate several or all factors and then implement changes as suitable within a single course, across courses, or in study programs. Note here that several of the factors discussed are relatively closely interwoven. For example, a large degree of freedom in the study situation often also implies long deadlines, suggesting that two factors may be addressed at once.

In such evaluations, it should be noted that each of the factors discussed is presented at a rather abstract level, so that relevance and concrete implementations in various settings must be carefully considered. For example, study topics vary by their very nature in how much freedom they represent for the student. Some study programs are already strictly structured and typically involve a common study group from start to finish, indicating that such programs do not need an increased focus on structure. Other programs are less structured and may also, by the nature of their study contents, be more “procrastination friendly” (e.g., Nordby et al., 2017 ). In other cases, such as study skills training and efficacy-building opportunities, “the more, the better” seems appropriate when closely linked to actual course learning tasks.

In evaluating the need for implementation of changes, the relevant factor should be assessed not only at the institutional level but—probably more importantly—at the program and course level. This applies not only to a need-based evaluation (“What do students need in order to reduce their procrastination?”), but also to a competence evaluation (“Can we provide the necessary work required for this implementation?”). Note also that some measures may be quite easy to plan on paper, but difficult to implement in a more complex system of rules and bureaucracy. For example, although long deadlines should be warned against (they induce procrastination), finding alternative solutions that can handle shorter deadline in a proper way may require changes (e.g., legal or practical) that are not easily possible to implement.

Where to Start?

In developing prevention or interventions programs concerning procrastination, one has to keep the interplay between personal factors (i.e., student characteristics) and contextual factors (i.e., institutions, courses, and teachers) in mind. As can be seen from Table 2 , the recommendations on the institutional, course, and teacher side will only fully unfold their effectiveness if students are simultaneously prepared to work on their self-regulatory skills. Thus, the recommendations we present in this paper should be accompanied by a culture of goal-focused self-regulation training programs. And, as discussed, self-regulation training programs, whether preventive or interventional, should not be administered without paying attention to contextual procrastination-friendly factors.

Table 2. Recommended measures to reduce procrastination.

Given the high prevalence estimates of procrastination among students, a closer look at procrastination-friendly factors in the academic environment is clearly warranted. The present paper identifies nine such factors and provides suggestions on how they may be changed in order to understand, prevent, and reduce academic procrastination. Clearly, more research is needed in this area, both with regard to the factors themselves (how many are they?) as well as to their interplay and relative importance. Given the potential beneficial effects for students, institutions, and society, we conclude that researchers should pay increased attention to social, cultural, organizational, and contextual factors in their endeavors to understand academic procrastination.

Author Contributions

FS initiated the project, wrote the introduction and discussion parts. All authors contributed at least one section each to the review and edited the complete draft.

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.

Acknowledgments

We thank Piers Steel and Efim Nemtcan for valuable comments on an earlier draft of this manuscript. Publication charges were covered by the publication fund of UiT The Arctic University of Norway.

^ We use «study skills» in a broad sense, referring to skills needed on the part of the student to successfully master various aspects of study tasks (cf. Tressel et al., 2019 ).
^ Svartdal et al. (2020) . Unpublished data.

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Keywords : academic procrastination, study environments, social factors, self-regulation, impulsivity, task aversiveness

Citation: Svartdal F, Dahl TI, Gamst-Klaussen T, Koppenborg M and Klingsieck KB (2020) How Study Environments Foster Academic Procrastination: Overview and Recommendations. Front. Psychol. 11:540910. doi: 10.3389/fpsyg.2020.540910

Received: 06 March 2020; Accepted: 12 October 2020; Published: 02 November 2020.

Reviewed by:

Copyright © 2020 Svartdal, Dahl, Gamst-Klaussen, Koppenborg and Klingsieck. 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: Frode Svartdal, [email protected]

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Published: 03 July 2024

Predictive analysis of college students’ academic procrastination behavior based on a decision tree model

Pu Song 1 ,
Xiangwei Liu 2 ,
Xuan Cai 3 ,
Mengmeng Zhong 4 ,
Qingqing Wang 5 &
Xiangmei Zhu 6

Humanities and Social Sciences Communications volume 11 , Article number: 869 ( 2024 ) Cite this article

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Predicting academic procrastination among college students in the context of a public crisis could provide essential academic support and decision-making strategies for higher education institutions to promote student psychological health. Notably, research focusing on predicting academic procrastination behavior among college students in the context of a global crisis is still limited. The purpose of this study is to address this gap by constructing a predictive model based on the decision tree algorithm to predict academic procrastination behavior among college students. A total of 776 college students from the Guangxi Zhuang Autonomous Region of China participated in this study. The study gathered data from multiple aspects relevant to academic procrastination behavior, including demographic information, academic achievements, subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging. Descriptive statistical analysis was conducted utilizing SPSS version 26.0, and decision tree model analysis was performed with Modeler 18.0. The findings of this study identified eight predictive factors of college students’ academic procrastination in order of importance: subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic performance, and sense of school belonging. The model accuracy was 85.78%, and indicating a relatively high level of prediction. The findings of this study not only provided a new perspective for understanding academic procrastination but also offered practical guidance for educators on how to mitigate this behavior.

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Introduction.

The recent occurrence of public crisis, such as COVID-19, has caused unprecedented impacts on all levels of education (Toquero, 2020 ), particularly affecting the psychological well-being and learning of college students. The challenge of COVID-19 not only disrupts students’ academic and daily lives (Gamage et al., 2020 ; Tarkar, 2020 ) but also exacerbates academic procrastination problems. Academics further highlight that factors such as psychological well-being, life satisfaction, and learning environment contribute to the changes in academic procrastination behavior during COVID-19 (Arifiana et al., 2020 ; Peixoto et al., 2021 ; Svartdal et al., 2020 ). Therefore, investigating the predictive factors and underlying mechanisms behind college students’ academic procrastination behavior is not only crucial for better understanding their psychological changes and behavioral adjustments, but also critical for developing effective strategies to improve academic performance and well-being.

Academic procrastination behavior can be attributed to a combination of biological, psychological, and social factors, highlighting the multiple aspects and complexity of this phenomenon. It is not that uncommon for students to experience negative emotions such as anxiety and depression during the pandemic. These negative emotions not only affect the psychological well-being of students but could also lead to an increase in academic procrastination behavior (Bolbolian et al., 2021 ; Deng et al., 2022 ). Conversely, self-esteem and life autonomy can enhance intrinsic motivation and resilience when facing challenges and difficulties, thereby reducing the likelihood of procrastination behavior during crises (Tian et al., 2023 ; Yang et al., 2021 ). Moreover, subjective well-being may serve as an alleviating factor that helps students to stay positive and reduce procrastination behavior (García-Ros et al., 2023 ). Addictive behavior related to mobile phones is not new in the age of information technology. However, it is noteworthy that depression and anxiety are significantly linked with smartphone addiction, particularly during public crises, and are further associated with academic procrastination behavior (Albursan et al., 2022 ; Ratan et al., 2021 ). Excessive reliance on smartphones for information and social interaction could distract students’ attention and diminish learning efficiency, thereby worsening academic procrastination behavior (Albursan et al., 2022 ).

Pro-environmental behavior reflects a sense of social responsibility and organizational ability, which are particularly important for maintaining academic discipline and reducing tendencies toward academic procrastination (Yuriev et al., 2020 ). Studies posit that a sense of school belonging could reduce academic procrastination tendencies. Dong and Izadpanah ( 2022 ) found in their study that a strong sense of school belonging can provide emotional support and enhance social connections. These social interactions and supports promote a positive perception of the learning environment and thus mitigate academic procrastination behavior. Therefore, it can be seen that the interaction between personal and environmental factors has a significant impact on academic procrastination behavior including the time of crises. Furthermore, academic achievement is a significant predictor of academic procrastination as it reflects learning efficiency and motivation (Wu et al., 2020 ). Students who achieve high scores typically possess greater self-efficacy and goal orientation, thereby reducing tendencies to procrastinate (Goroshit & Hen, 2021 ).

A thorough investigation of the impact of these predictors on academic procrastination will enrich the understanding of the complex interplay of procrastination behavior, and provide insights for designing effective interventions. Academic procrastination is a problem to tackle even before the pandemic took place. It is important to note that this study contributes to understanding academic procrastination behavior during pandemic situations. Despite the pandemic subsiding now, stakeholders in education could still leverage these insights to better prepare for future crises beyond the pandemic era. Notably, prior studies that utilize the decision tree method to predict academic procrastination are still scarce in the literature. The decision tree model is one of the algorithms of machine learning for data mining (Sharma & Kumar, 2016 ). It involves classification or prediction based on attribute tests and outcomes (Wu et al., 2008 ).

The decision tree model has undergone several accuracy simplifications and improvements by Ross Quinlan ( 1987 , 1996 ). The merits of the decision tree model lie not only in its high predictive accuracy but also in its strength to decompose a complex decision-making process into a series of simpler decisions, thereby providing a solution that is generally easier to interpret (Quinlan, 1987 , 1996 ). For instance, previous studies have utilized decision tree models to identify innovative behavior (Safavian & Landgrebe, 1991 ; Skrbinjek & Dermol, 2019 ), and have accurately predicted factors influencing innovation success and failure (Park, 2019 ). In addition, decision tree models have been applied across various disciplines of research including marketing and psychology, to predict consumer satisfaction, attitudes, and loyalty (Han et al., 2019 ). Hence, the decision tree method is appropriate for this study to predict college students’ academic procrastination behavior and to identify variables that can serve as predictors of academic procrastination.

Literature review

Academic procrastination.

Academic procrastination was first proposed by Solomon and Rothblum in 1984, they defined it as the act of postponing planned tasks and persistently delaying or deferring task completion within the academic context (Solomon & Rothblum, 1984 ; Svartdal et al., 2020 ). However, the academic community has not yet formed a unified definition or conceptualization of academic procrastination. The majority of scholars agree that academic procrastination typically refers to the behavior of postponing and delaying mandatory academic tasks without sufficient justifications (Ferrari & Camilleri, 2021 ). Some scholars further refine this definition by suggesting that academic procrastination is the behavior of choosing to delay task completion despite being well aware of the negative consequences (Steel, 2007 ; Wang et al., 2021 ).

Apart from that, researchers also postulate that academic procrastination consists of different dimensions such as behavior, cognition, and emotion (Saplavska & Jerkunkova, 2018 ). This is mainly characterized by cognitive distortions and irrational delays in initiating or completing academic tasks (Schouwenburg, 1995 ). Cai ( 2017 ) also endorsed Schouwenburg’s viewpoint and considered academic procrastination as the discrepancy between task planning and implementation that involves irrational delays in planning or completing academic tasks as well as remediation and summary after delays. Based on the above discussions, this study defines academic procrastination as the delaying behaviour exhibited by students during academic tasks. This behavior primarily involves intentionally postponing or avoiding the completion of academic tasks, and may or may not result in failing to complete the given tasks within specified timeframes.

The predictor of academic procrastination

Extensive literature suggests that researchers are deeply interested in understanding the influencing factors of academic procrastination (Abdi Zarrin & Gracia, 2020 ; Nwosu et al., 2020 ; Wang et al., 2021 ). The selection of predictors in this study is grounded in the Biopsychosocial Model and a review of the literature, which includes subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging. George Engel ( 1981 ) proposed a Biopsychosocial Model to explain the impact of psychological and social factors on individual behavior. During public crises, this model can be particularly useful in understanding how students’ behaviors, psychological reactions, and social environments interact and influence their academic procrastination and well-being.

Subjective well-being typically encompasses indicators such as life satisfaction, positive affect, and negative affect (Yilmaz, 2023 ). The prolonged lockdowns and isolation during the pandemic have had a significant negative impact on students’ subjective well-being (Foa et al., 2020 ), and this could lead to negative emotions, thereby increasing academic procrastination behavior (Vlachopanou & Karagiannopoulou, 2022 ). Previous studies have shown that students with higher subjective well-being demonstrate stronger self-regulation and resilience, allowing them to manage their learning behavior and time more effectively, thereby reducing procrastination behavior (Bu, Wu, & Wang, 2021 ; Morosanova et al., 2021 ; Yadav et al., 2020 ). Recent research has also endorsed the negative correlation between university students’ subjective well-being and academic procrastination (García-Ros et al., 2023 ). The literature underscores the prediction of subjective well-being on academic procrastination by highlighting its role in improving self-regulation and resilience, which in turn significantly reduces academic procrastination. Therefore, this study proposes:

H1: Subjective well-being negatively predicts academic procrastination.

Literature suggests that smartphone addiction is one of the reasons for academic procrastination behavior (Albursan et al., 2022 ). As of the year 2021, China ranked first globally in terms of smartphone users with the number surpassing 910 million (Horani & Dong, 2023 ). Studies also indicate that during the COVID-19 lockdown, the usage time of smartphones significantly increased (Katsumata et al., 2022 ; Statista, 2021 ). A study conducted by Chen et al. ( 2017 ) reveal that the smartphone addiction rate among university student in China is 29.8%. It is expected that the percentage will continue to increase due to social isolation and increased screen time during the pandemic. Hence, this study posits smartphone addiction is one of the significant predictors of academic procrastination. Studies have also discovered that poor self-regulation is one of the contributing factors to procrastination behavior, with a significant correlation found between poor self-regulation ability and smartphone addiction (Ching & Tak, 2017 ; Zhang & Wu, 2020 ).

This is because smartphone addiction can disrupt cognitive processes and impair brain regions associated with self-regulation (Gao et al., 2020 ), leading to poor self-regulation in academic tasks and subsequent procrastination behavior (Liu et al., 2022 ). Additionally, students with smartphone addiction often experience sleep deprivation, which can contribute to a decline in cognitive function, making it more challenging to complete academic tasks on time and thus leading to academic procrastination (Cui et al., 2021 ; Hamvai et al., 2023 ). The findings from Akinci’s ( 2021 ) study also supported that smartphone addiction is an important positive predictor of academic procrastination, as students addicted to smartphones tend to neglect and procrastinate academic responsibilities. The literature highlights smartphone addiction as a key factor in academic procrastination during the pandemic. Smartphone addiction is related to sleep deprivation, poor self-regulation, impaired cognitive function, and ultimately predicting academic procrastination. Therefore, this study proposes:

H2: Smartphone addiction positively predicts academic procrastination.

It is common for students to experience negative emotions such as fear, anxiety, and stress during crises. Albujar Moreno, Castro Portillas ( 2020 ) found that individuals with higher levels of anxiety, tend to have lower self-regulation abilities and are more prone to procrastination behavior. Research indicates that negative emotions such as stress and anxiety can cause structural and functional changes in the brain by impairing memory and cognitive functions. This includes planning and goal-directed actions that could intensify procrastination tendencies (Limone et al., 2020 ; Podlesek et al., 2021 ). Being the country with the longest lockdown period, Chinese students encounter heavier academic tasks and learning pressures, making them more susceptible to negative emotions and predicting academic procrastination (Deng et al., 2022 ). Studies conducted on the detection of negative emotions among university students indicated a significant increase from 12.94% before the onset of the pandemic to 46.6% during the pandemic (Feng, 2018 ; MENG, 2020 ). The findings, revealing approximately a fourfold increase in negative emotions among students, indicate a potential connection between negative emotions and academic procrastination. Therefore, this study proposes:

H3: Negative emotions positively predict academic procrastination.

Self-esteem refers to a psychological state that evaluates one’s own worth and abilities, which can have a profound impact during public crises. Maxwell ( 1992 ) found that self-esteem is more vulnerable and significantly decreases during public crises. Academics propose that self-esteem is one of the important factors that predict academic procrastination. Shu et al. ( 2022 ) highlighted that during the COVID-19 pandemic, factors such as isolation, social restrictions, and future uncertainty affected university students’ self-esteem levels by inducing feelings of isolation, anxiety, and stress. Low self-esteem could intensify negative emotions and feelings of pressure by making students more inclined to adopting academic procrastination to escape from learning tasks and pressure (Batool, 2020 ). Similarly, a longitudinal study conducted by Yang et al. ( 2021 ) on university students reports that as self-esteem continues to decline, academic procrastination worsens progressively. The literature highlights a significant link between self-esteem and academic procrastination, emphasizing the importance of addressing self-esteem during crises to mitigate procrastination behavior. Therefore, this study proposes:

H4: Self-esteem negatively predicts academic procrastination.

Tian et al. ( 2023 ) define life autonomy is the ability of an individual to independently guide own life direction, make choices for oneself, and take responsibility for the consequences of those choices. Life autonomy encompasses several dimensions, including autonomy, self-management, and a sense of life responsibility. Greater life autonomy promotes intrinsic motivation and increases learning engagement, thus reducing academic procrastination (Guay, 2022 ; Sobia et al., 2021 ). In fact, many Chinese universities adjusted or delayed their schedules due to the pandemic, despite granting students with more flexible time. However, students with immature mindsets often struggle to engage in autonomous learning (Town et al., 2022 ). They tend to exhibit academic laziness, poor self-management skills, and procrastinate on academic tasks (Albursan et al., 2022 ; Shim et al., 2022 ). Additionally, previous studies on university students also suggest that the stronger their sense of responsibility, the less likely they are to engage in academic procrastination (Jiao & Guo, 2020 ). Evidence from the literature underscores the predictive effect of life autonomy on academic procrastination. Hence, drawing from the research on various dimensions of life autonomy and their correlation with academic procrastination, this study proposes:

H5: Life autonomy negatively predicts academic procrastination.

Procrastination can be conceptualized as an irrational tendency to delay required tasks or assignments (Klingsieck, 2013 ). Early research from Lillemo ( 2014 ) has revealed the negative correlation between pro-environmental behavior and procrastination tendencies. Studies highlight that pro-environment behavior is an important indicator for psychological regulation and adaptation from social changes and environmental crises (Colombo et al., 2023 ; Mi et al., 2021 ; Zebardast & Radaei, 2022 ). Literature implies that students who are more engaged in pro-environmental behavior also possess various qualities and traits that could negatively predict academic procrastination tendency. For example, individuals who engage in pro-environmental behavior often possess greater self-regulation, goal-setting, monitoring, and self-motivation qualities. These qualities can help individuals manage learning tasks and time more effectively, thereby negatively predicting procrastination (Colombo et al., 2023 ; Sawitri et al., 2015 ; Wang et al., 2021 ). Furthermore, implementing pro-environmental behavior requires individuals to have strong executive and organizational skills, which are opposed to academic procrastination behavior (Colombo et al., 2023 ; Gutiérrez-García et al., 2020 ). The literature review indicates that pro-environment behavior is important for psychological adaptation to environmental challenges, and suggests that individuals who exhibit pro-environmental behavior also often possess qualities and skills that mitigate academic procrastination. Therefore, this study proposes:

H6: Pro-environmental behavior negatively predicts academic procrastination.

Academic achievement is an important indicator of learning outcomes, and the academic environment has a crucial role to play. Academic achievement serves as a crucial predictor in understanding academic procrastination behavior, as it not only reflects students’ performance but also influences their psychological state and behavioral patterns (Samson, 2021 ). Recent studies suggest that the academic interruptions caused by COVID-19 have had a significant negative impact on academic achievement (Kuhfeld et al., 2020 ; Serrano Aguirre, 2020 ). Academics are interested in investigating the relationship between academic achievement and procrastination behavior, with findings supporting a negative correlation between achievement and academic procrastination (Karataş, 2015 ; Türel & Dokumacı, 2022 ). Kurtovic, Vrdoljak, and Idzanovic ( 2019 ) explain the relationship between academic achievement and procrastination by considering the level of self-efficacy. They argue that students with better academic achievement are likely to experience a greater sense of self-efficacy, negatively predicting academic procrastination. Conversely, low self-efficacy is often associated with poor academic achievement and is likely to increase procrastination behavior. The literature supports that academic achievement is a significant factor in procrastination behavior, as it reflects students’ performance and influences their psychological state and patterns of behavior. Therefore, this study proposes:

H7: Academic achievement negatively predicts academic procrastination.

During the COVID-19 pandemic, most countries in the world enforced lockdown and isolation measures, with China having the longest-lasting lockdown policies (Yu et al., 2022 ). Throughout this period, many Chinese universities conducted their courses and exams only through online platforms. The sudden transition from physical classes to online learning posed significant challenges in promoting and sustaining students’ sense of school belonging (Gopalan et al., 2022 ). Existing research has revealed that a sense of school belonging can have a significant impact on academic procrastination (Dong & Izadpanah, 2022 ; Tian et al., 2023 ). For example, Lim, Yoo, Rho, & Ryu ( 2022 ) found that students lacking a sense of school belonging are more likely to engage in dropout behaviors. Consequently, students may experience problems related to learning interruption and academic procrastination. This is especially true in pandemic situations, where prolonged isolation and lockdowns diminish the sense of school belonging, resulting in academic disengagement and procrastination (Morán-Soto et al., 2022 ). Overall, the literature suggests an existing relationship between a sense of school belonging and academic procrastination. Students lacking this sense of school belonging are more likely to exhibit higher levels of academic procrastination and engage in dropout behaviors, and the pandemic has further aggravated this phenomenon. Therefore, this study proposes:

H8: Sense of school belonging negatively predicts academic procrastination.

To examine the above hypothesis, this study employed a decision tree model to predict academic procrastination behavior among college students. The strength of this method lies in decision trees’ capability to manage large amounts of data and identify various predictive factors and their interactions (Charbuty & Abdulazeez, 2021 ), which is important for understanding complex academic procrastination behaviors (Yang et al., 2020 ). Moreover, the decision tree prediction process is easy to understand and can be visualized by showing every decision step from the root node to the leaf nodes (Streeb et al., 2022 ). This approach enables intuitive display of the relationship patterns between predictors and the outcome variable (Yang et al., 2020 ). This study intended to utilize a decision tree model to clearly demonstrate the factors and pathways influencing academic procrastination, thereby providing insights for developing strategies and interventions to reduce academic procrastination. In short, the decision tree is an appropriate approach to adopt in this study as it is an intuitive, easy-to-understand, and effective method for prediction that enables the identification of key influencing factors. Hypothetical predictors of academic procrastination in this study are shown in Fig. 1 .

The left panel lists hypothesized predictors of academic procrastination. The arrow symbolizes the predictive relationship. The right panel represents the predicted variable, academic procrastination, which is categorized into high and low level.

Research Method

Participants and procedures.

The location of this study was the Guangxi Zhuang Autonomous Region of China, chosen for its proactive educational policies and practices during the COVID-19 pandemic. For instance, the region implemented strict academic measures by issuing dropout notices to students with severe academic procrastination or overdue completion. This study adopted a three-stage random sampling method to ensure the representativeness of the data collected from September 7th to October 15th, 2022. At the initial stage, because of the stringent lockdown policies and school management regulations during the pandemic, a simple random sampling method was employed to select 3 universities out of 83 in the region by utilizing a random number generator (Calculator.net, 2022 ). However, only one university was authorized to conduct the survey. It is noteworthy that due to the pandemic, the freshmen had not started their courses, and most of the seniors were engaged in off-campus internships. Therefore, this study focused its sampling on sophomore students, covering 46 different majors, with a total of 15,000 students. Subsequently, this study utilized the Online Random Number Generator website (Calculator.net, 2022 ), randomly selected 800 sophomore students. After data collection was completed, 24 questionnaires were deemed invalid due to poor response quality (such as arbitrary ticking, consecutive repetitive answers, etc.). Ultimately, 776 valid questionnaires were obtained, including 219 males (28.2%) and 557 females (71.8%), with ages ranging from 19 to 25 years.

This study utilized an online questionnaire to collect data. Because of lockdown policies during the COVID-19 pandemic, researchers were not allowed to enter the campus. Therefore, a trained research assistant, who also serves as a university counselor of the selected university, assisted the researchers in data collection. The questionnaire was expected to take approximately 20 minutes to complete. Prior to the survey, the research assistant provided detailed information about the purpose of the study, confidentiality, and the right to withdraw to ensure voluntariness.

Additionally, participants were required to read and sign an informed consent form, affirming their understanding and agreement to participate under the terms outlined. Afterward, the trained research assistants displayed QR codes for the questionnaire to students. Those who agreed to participate in the study were required to scan the QR codes, access the survey page, answer the questions, and then submit upon completion. Additionally, the trained research assistant was prepared to assist and answer any questions from participants if there was any confusion related to the questionnaire during the process.

The online questionnaire employed in this study consists of two parts: demographic information and scales, a total of 131 items. The demographic information includes gender, age, and academic achievement. The scales section comprises seven different scales to assess the predictors and academic procrastination in this study: subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, and sense of school belonging.

Subjective well-being scale

Subjective Well-Being (SWB), as coined by Diener ( 1984 ), refers to a personal overall assessment of quality of life that encompasses two dimensions: affective and cognitive. Xing ( 2002 ) modified this scale into a Chinese version, including 20 items. It is one of the most recognized and commonly used scales to measure well-being in China. This study adopted the Chinese version of SWB, which comprises 20 items similar to the original scale. The scale utilizing a 5-point Likert scale to measure well-being ranging from “1” as strongly disagree; “2” as disagree; “3” as somewhat disagree; “4” as agree; and “5” as strongly agree. In this study, the Cronbach’s alpha coefficient for the Subjective Well-Being Scale was 0.860.

Smartphone addiction scale

The Short version of the Smartphone Addiction Scale (SAS-SV) was developed by Kwon, Kim, Cho, and Yang ( 2013 ). It consists of 10 items to assess the degree of smartphone addiction. The items describe daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationships, overuse, and tolerance. This scale is rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of smartphone addiction. The Cronbach’s alpha coefficient for SAS-SV found in this study was 0.840.

Depression, anxiety, and stress scale

The Depression, Anxiety, and Stress Scale (DASS-42) was developed by Lovibond, Lovibond ( 1995 ) based on the three-factor model of depression, anxiety, and stress. Then, DASS-42 was later revised into a shorter version known as DASS-21 by Antony et al., ( 1998 ) to measure the levels of depression, anxiety, and stress. This scale is simple, easy to use, novel unique, and fast to operate. It has been translated into many languages for research and application in countries around the world. This scale consists of 21 items and rated on 5-point Likert scale ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of depression, anxiety, and stress. In this study, the Cronbach’s alpha coefficient for the scale was 0.965.

Rosenberg self-esteem scale

This study adopted the Rosenberg Self-Esteem Scale (RSE), which was developed by Rosenberg ( 1965 ) to evaluate general feelings of self-worth and self-acceptance. The scale consists of 10 items, and respondents report whether the items accurately describe themselves. This scale has been widely used, it is concise and easy to score and can help participants directly assess their own positive or negative feelings. All items are rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. In this study, the Cronbach’s alpha coefficient for the self-esteem scale was 0.714.

Life autonomy scale

This study adapted Life Autonomy Scale from Pan and Xie ( 2010 ) to measure students’ life autonomy. The original scale consists of 70 items, including six sub-scales: ideal, life autonomy, existence, love and care, life experience, and attitude toward death. To assess the degree of life autonomy of participants, this study only selected the sub-scale ‘life autonomy,’ which includes 12 items. All items were rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Items 7 to 12 employ reverse scoring, a higher score indicates a lower life autonomy, while a lower score may suggest a higher life autonomy. In this study, the Cronbach’s alpha coefficient of the scale was 0.946.

Pro-environmental behavior scale

The Pro-environmental Behavior Scale utilized in this study was developed by Liu and Wu ( 2013 ). This scale consists of 11 items across public and private dimensions. Six items pertain to behaviors in the public domain, while five items concern behaviors in the private domain. In the public domain, behaviors mainly involve participation in environmental conservation activities within public organizations, such as donating to environmental NGOs (Non-Governmental Organizations) or conservation societies. Conversely, private domain behaviors refer to environmentally friendly actions in individuals’ daily lives, such as purchasing eco-friendly products. This scale was rated on a 5-point Likert scale, ranging from “1” for strongly agree to “5” for strongly disagree. Higher scores indicate higher levels of pro-environmental behavior. The scale in this study yielded a Cronbach’s alpha coefficient of 0.953.

Psychological sense of school membership scale

This study employed the Psychological Sense of School Membership (PSSM) scale to assess the sense of school belonging of respondents. The original scale was developed by Goodenow ( 3.0.CO;2-X " href="/articles/s41599-024-03300-1#ref-CR39" id="ref-link-section-d139715408e993">1993 ) and consists of 18 items. It has been widely translated into multiple languages, including a Chinese version. This study adopted the Chinese version of PSSM, revised by Pan et al. ( 2011 ), which also comprises 18 items and assesses a student’s commitment to the school in terms of attachment, behavioral attitudes, and identification with the school. This scale is rated on a 5-point Likert scale ranging from “1” for strongly agree to “5” for strongly disagree. The scale used in this study yielded a Cronbach’s alpha coefficient of 0.838.

Academic procrastination scale

This study adopted the Academic Procrastination Scale developed by Tuckman ( 1991 ) to measure academic procrastination behavior. According to Tuckman, one of the strengths of this scale is its flexibility for allowing respondents to report their own behavior, and its specific usefulness in identifying academic procrastination behavior. Its rating scale was also converted from the original four points to five points before administration to maintain consistency with the other scales. The scale consists of 16 items and is rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of academics. In this study, the reported Cronbach’s alpha coefficient of the scale was 0.920.

Statistical analyses

This study employed SPSS 26.0 for descriptive statistical analysis and Modeler 18.0 for decision tree model analysis. Descriptive statistics were used to analyze frequency distributions and trend changes in the observed data. Subsequently, the decision tree model was constructed using the C5.0 algorithm, an extension of the ID3 and the C4.5 algorithms proposed by Quinlan ( 1987 , 1996 ) and Witten, Frank, and Hall( 2005 ). This algorithm is not only suitable for large datasets but also offers faster computational speed and stronger predictive capabilities (Xiong, 2011 ). It was utilized to investigate which variables can predict the occurrence of academic procrastination.

Data coding

This study categorizes samples into two levels of academic procrastination: high and low. The questionnaire utilizes a Likert 5-point scale for scoring, with the study selecting 60% as the cutoff point. Consequently, scores of 3 or below are coded as 0, while scores above 3 are coded as 1. Based on this principle, the present study encodes the key variables predicting academic procrastination. (see Table 1 ).

The construction of decision tree

The construction of the decision tree model requires classifying samples based on the information entropy of the input dataset. Information entropy reflects the complexity within the samples. The greater the impurity within the samples (degree of impurity of a dataset), the larger the value of the information entropy, defined based on Mitchell ( 1997 ) as:

D is the training dataset with sample size m, and P k is the probability of each class of samples.

The Gain Ratio is used to measure the difference in information entropy of datasets under different classification methods. If this study chooses variable C to divide the dataset D into n subsets, then based on Quinlan ( 1996 ), the Gain Ratio is defined as:

The C5.0 algorithm selects the attribute with the maximum Gain Ratio as the splitting point, establishing several branches based on the values of this attribute, and obtaining some subsets. This selection process is repeated until the final subsets contain only data of the same category, to perform the induction classification of data (Che et al., 2011 ).

Pruning of the decision tree

Based on the decision tree model constructed from the training samples, the dataset is recursively traversed to each leaf node. Specifically, the C5.0 algorithm employs post-pruning to systematically prune the leaf nodes, layer by layer. The mean square error of the dataset nodes is calculated. If the mean square error decreases after pruning, the node is removed; otherwise, it is retained (Quinlan, 1998 ).

Evaluation of the decision tree

In this study, as recommended by Gholamy, Kreinovich, and Kosheleva ( 2018 ), 70% of the sample data ( n = 544) is selected as the training data, while 30% of the sample data ( n = 232) is used as the testing data. The testing data reflects the extent to which the model constructed from the training data is suitable to new data. Accuracy, precision, and recall are indicators used to evaluate the quality of the model (Han et al., 2019 ). Accuracy refers to the proportion of correctly classified samples out of all samples. Precision refers to the proportion of true positive samples among the predicted positive samples. Recall refers to the proportion of true positive samples correctly predicted out of all actual positive samples. Specifically, recall is calculated as TP (true positive) divided by TP (true positive) plus FN (false negative), and accuracy is calculated as TP (true positive) divided by TP (true positive) plus FP (false positive).

Descriptive statistics

The descriptive statistics are summarized in Table 2 . The forecast target, students’ academic procrastination behavior, shows a good status. The mean value of academic procrastination was 2.593 (with a standard deviation of 0.635), which is lower than 60% of the full score. This means that no more than half of the students were in a state of high academic procrastination. We then encoded each variable by assigning a value of 1 to cases with scores above 60% of the full score and a value of 0 to all other cases.

Prediction analysis of academic procrastination

As presented in Fig. 2 below, the predictive factors of academic procrastination include subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environment behavior, academic achievement, and sense of school belonging. Additionally, respondents with low academic procrastination accounted for 79.963%. The root node is the topmost node in a decision tree model, and the branches below this top node represent the outcomes of decisions (see Fig. 2 ). The closer a predictive variable is to the root node, the higher its importance, indicating the degree of importance of the predictive variable.

The gray rectangle represents a node. The value inside a node indicates the quantity and distribution of samples. Blue and red squares represent the volume and proportion of samples within the node. The value ‘n’ denotes the number of samples in the node. The ‘%‘ value indicates the percentage of samples in the node relative to the total number of samples. ‘Total’ represents the cumulative total number of samples in the node.

The importance of predictor variables can be inferred from Fig. 3 . Subjective well-being is the most important predictor variable to academic procrastination, followed by smartphone addiction and negative emotions in second and third place. Self-esteem, autonomy, and prosocial behavior rank fourth, fifth, and sixth in importance, while academic achievement and school belonging have the least importance.

The horizontal axis represents the importance of the predictive impact, while the vertical axis lists the predictors. The blue bars indicate the importance of each predictor on academic procrastination, with longer bars representing more important prediction.

Model evaluation

Tables 3 and 4 respectively present the confusion matrix and classification accuracy of the research model. The accuracy of training samples in the model is 87.50%, and the accuracy of test samples is 85.78%. As reported in Table 5 , for the test samples, the model’s precision rate for predicting low academic procrastination is 90.24%, with a recall rate of 94.12%.

This study utilized a decision tree model and the C5.0 algorithm to construct an eight-factor model for predicting academic procrastination. Additionally, the study ranked the importance of predictors to academic procrastination based on their contribution. The discussions of the findings of this study are based on the order of importance of predictive factors for academic procrastination, which are subjective well-being, smartphone addiction, negative emotion, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging.

The findings of this study suggesting the three most important predictors of academic procrastination were subjective well-being, smartphone addiction, and negative emotion. The reason may lie in the fact that students with higher levels of subjective well-being are more likely to possess conscientious and open positive personality traits (Abdullahi et al., 2020 ). These students also possess a heightened sense of time value and time monitoring, thereby avoiding academic procrastination behaviors in comparison to their counterparts who have lower levels of subjective well-being (Berber Çelik & Odaci, 2022 ). Moreover, higher levels of subjective well-being can assist students in adapting throughout the learning process and tackling academic tasks (Ran et al., 2023 ). Conversely, lower levels of subjective well-being tend to exhibit negative personality traits such as Neuroticism, which increases the likelihood of academic procrastination (Abdullahi et al., 2020 ).

Students with smartphone addiction often exhibit a lack of attention and self-control (Geng et al., 2021 ). This addiction can easily lead to a tendency to use smartphones to escape study pressure, thus increasing the likelihood of academic procrastination behaviors (Troll et al., 2021 ). This phenomenon is particularly pronounced in China, as the country has the highest number of smartphone users in the world. Smartphone addiction intensified even further as students became increasingly dependent on smartphones for online learning during pandemic (Albursan et al., 2022 ). However, heavy screen time can also easily lead to the misuse and overuse of smartphones, such as spending excessive time on social media, entertainment, or for gaming purposes. Smartphone addiction can deteriorate learning efficiency and self-control, while increasing procrastination behaviors (Troll et al., 2021 ).

Research indicates that negative emotions can predict procrastination behavior. Neuroimaging studies show that negative emotions activate specific brain regions in the anterior insula and amygdala. These regions are associated with procrastination behaviors (Barrett & Satpute, 2013 ; Seeley et al., 2007 ). Evidence suggests that the stronger the negative emotions, the more likely procrastination behaviors are to manifest (Wang et al., 2022 ). A study conducted by Rahimi and Vallerand ( 2021 ) during COVID-19 also reveals that negative emotions such as fear, anxiety, and depression are associated with academic procrastination. These findings from past literature could explain how prolonged lockdowns and high academic pressure are likely to diminish students’ goal-setting, self-monitoring, and self-regulation, leading to a significant increase in negative emotions among college students. This, in turn, further contributes to the occurrence of academic procrastination behaviors.

Khurshid, Batool ( 2018 ) considered academic procrastination as the product of low self-esteem, and they argued that self-esteem can negatively predict academic procrastination. In times of crisis, college students with higher self-esteem often find it easier to immerse themselves in academic life and are more likely to overcome academic procrastination (Yang et al., 2021 ). Additionally, Brando-Garrido et al. ( 2020 ) pointed out that self-esteem not only affects academic confidence and motivation but also indirectly influences academic procrastination behavior through various aspects, including emotional regulation and proficiency in goal establishment.

Past literature suggests life autonomy can negatively predict academic procrastination, students with higher life autonomy are more self-directed and have stronger self-control, thus less likely to engage in academic procrastination behavior (Codina et al., 2018 ). This is because students with higher life autonomy are also likely to possess greater self-regulation and take proactive action to avoid the occurrence of academic procrastination behavior. This viewpoint has received support from neuroscience studies, where non-invasive technologies such as Transcranial Direct Current Stimulation (TDCS) and Transcranial Magnetic Stimulation (TMS) were used to stimulate the dorsolateral prefrontal cortex (DLPFC) to enhance participants’ self-control and thereby reduce procrastination behavior (Feng, Wang, & Su, 2021 ).

Ateş ( 2020 ) proposes that pro-environmental behavior is an important predictor for academic procrastination given pro-environmental behavior often requires strong planning skills. Empirical evidence suggests that those who actively engage in pro-environmental behavior also likely demonstrate stronger proficiency in goal-setting and execution of plans, thereby mitigating procrastination behavior (Sawitri et al., 2015 ; Yuriev et al., 2020 ). Additionally, pro-environmental behavior also reflects personal self-regulation and self-discipline, both of which are crucial for reducing procrastination behavior (Akinci, 2021 ; Colombo et al., 2023 ). The findings of this study suggest that even in times of crisis, understanding and promoting pro-environmental behavior is important not only for promoting environmental protection but also for improving academic performance and reducing procrastination behavior.

In comparison to the other predictors mentioned above, the findings of this study indicate that the impact of academic achievement and a sense of school belonging on academic procrastination are relatively small. Although previous studies supported the association between these two predictors and academic procrastination (Lim et al., 2022 ; Morán-Soto et al., 2022 ), during times of crisis, students may prioritize health, family, and economic situation (Tadesse & Muluye, 2020 ; Verma & Prakash, 2020 ). Under such circumstances, academic achievement may no longer be the top priority for students, but rather addressing the various issues brought about by the crisis (Brion & Kiral, 2021 ; Hartshorn & Benjamin, 2020 ).

Lastly, the weakest predictor for academic procrastination found in this study is the sense of school belonging. One possible explanation could be the impact of long-term online learning during the epidemic, which weakens the connection between students’ sense of school belonging, and their tendency to procrastinate academically. Literature suggests that a sense of school belonging often has a role to play in academic procrastination through other mediators. For example, Dong and Izadpanah ( 2022 ) argue that the sense of school belonging can indirectly affect academic procrastination by influencing self-efficacy and emotions. Furthermore, a sense of school belonging can also reduce the degree of academic procrastination by alleviating psychological pressures such as anxiety and tension (Abdollahi et al., 2020 ). Despite academic achievement and a sense of school belonging are not the most important predictors for academic procrastination in this study, they are still important for students’ overall academic performance and psychological well-being during public crises. Therefore, these predictors should not be disregarded.

This study employs a decision tree model to predict academic procrastination. The results show that the model has an accuracy of 85.78%, indicating its effectiveness in predicting academic procrastination. The findings of this study suggest that subjective well-being, smartphone addiction, and negative emotions are core predictors of academic procrastination. In particular, subjective well-being, the most significant predictor, underscores the crucial role of psychological well-being and life satisfaction in academic behavior. Moreover, smartphone addiction and negative emotions reveal the significant impact of modern technology dependence and emotional health on academic procrastination. It is worth noting that factors such as self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging, although having a relatively small predictive effect on academic procrastination in this study, should not be disregarded. Instead, they remain important considerations for understanding the complexity of academic procrastination behavior. These factors provide insights for mitigating academic procrastination by promoting personal well-being, enhancing self-regulation, and strengthening school belonging. The findings of this study not only offer new perspectives on understanding and predicting academic procrastination but also provide empirical evidence for developing effective interventions to address it. In conclusion, this study makes a significant contribution to the understanding of academic procrastination behavior, both in times of crisis and beyond, and offers practical guidance for effectively tackling academic procrastination among college education students.

Implication

This study has enriched the applicability of the Biopsychosocial Model in the context of public crises by suggesting that the predictors associated with academic procrastination are indeed interrelated with students’ biological, psychological, and social aspects. Simultaneously, this study could provide a series of practical implications for educational stakeholders. The findings of the study suggest that students bear the primary responsibility for combating academic procrastination, but maintaining personal well-being is essential for improving self-regulation, which is necessary for effectively addressing procrastination. This would enable students to better utilize campus resources, such as engaging in group studies and utilizing counseling services to mitigate tendencies toward academic procrastination. Additionally, it is important for lecturers to provide academic and emotional support to enhance students’ self-esteem and autonomy as they are significant factors in addressing academic procrastination behavior. Lastly, the findings of the study could offer insights to university management on optimizing student support services such as wellness centers, counseling services, and academic advising centers. This optimization would aim to consistently ensure student well-being and enhance the sense of school belonging, especially during times of crisis to reduce academic procrastination behavior among students.

Limitation and future research

This study has certain limitations. Adopting a cross-sectional design may restrict the depth of understanding over time. Moreover, the respondents all come from a university in the Guangxi Zhuang Autonomous Region, which could potentially limit the generalizability of the research results. Future research could focus on enhancing understanding by observing the interactions between the predictors and changes in academic procrastination over time. Additionally, recruiting participants from different regions or institutions could improve the generalizability of the findings. Researcher is also encouraged to investigate other potential variables that are not included in this study to expand the understanding of academic procrastination behavior and its determinants. This could be beneficial in the endeavor to reduce academic procrastination behavior beyond the pandemic.

Data availability

The datasets generated during and analyzed during the current study are not publicly available due to institutional confidentiality regulations but are available from the corresponding author on reasonable request.

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Song, P., Liu, X., Cai, X. et al. Predictive analysis of college students’ academic procrastination behavior based on a decision tree model. Humanit Soc Sci Commun 11 , 869 (2024). https://doi.org/10.1057/s41599-024-03300-1

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PMC10297372

Academic Procrastination in Children and Adolescents: A Scoping Review

Marcela paz gonzález-brignardello.

1 Department of Personality Psychology, Psychological Assessment and Treatment, Faculty of Psychology, Universidad Nacional de Educación a Distancia, UNED, 28040 Madrid, Spain; se.denu.isp@zelaznogpm (M.P.G.-B.); se.denu.isp@arivle-zehcnasa (A.S.-E.P.)

Angeles Sánchez-Elvira Paniagua

M. Ángeles lópez-gonzález.

2 Psychology Department, Faculty of Health Sciences, Universidad Rey Juan Carlos, 28922 Madrid, Spain

Associated Data

Data can be accessed via contacting the authors upon approval.

Academic procrastination is a persistent behavior in students’ academic development consisting of postponing or delaying the completion of necessary tasks and having a deadline for completion, which is associated with detriment in performance, school dropout, and loss of student well-being. The largest body of existing knowledge on this behavior comes from studies conducted with university students, although it is necessary to deepen the findings obtained at lower educational levels. The aim of this work has been to carry out a scoping review of the empirical publications focused on academic procrastination in children and adolescents. The inclusion and exclusion criteria are detailed following the general guidelines of the Joanna Briggs Institute. However, some modifications are incorporated in the flowchart to guide the review sequence. The search was conducted in eleven thematic (ERIC, MedLine, Psychology and Behavioral Sciences Collection, PsycINFO, PubPsych, and Teacher Reference Center) and multidisciplinary databases (Academic Search Ultimate, E-Journals, ProQuest, Scopus, and Web of Science) to identify relevant publications up to 2022, including grey literature. Out of the initial 1185 records screened, a total of 79 records were selected. The search results included a total of 79 records. The most used assessment instruments, the most studied variables, and the type of design and sources of information used in the selected studies are detailed. Cultural aspects that open new lines of future research are identified.

1. Introduction

Procrastination is a very common and pervasive behavior in different areas of human activity. It involves the intentional delay of actions and behaviors that have a time limit within which they should be completed [ 1 , 2 ]. There is a tendency to assume that everyone procrastinates or delays some necessary activity (e.g., medical appointments, exercise, paying fines, going to bed, among many other possible activities). However, to the extent that this behavior occurs on a frequent basis, it has consequences for those who engage in it, and may even affect others if it occurs, for example, in work or collaborative learning environments. Contrary to popular belief, people who engage in this behavior have a desire to complete tasks or actions, but have difficulty in translating these intentions into implementation actions, initiation, and completion [ 3 ].

Procrastination is a complex, poorly understood behavior that involves different cognitive, emotional, and behavioral components [ 4 , 5 , 6 ]. It has been understood as a failure of self-regulation [ 4 ], an avoidance behavior toward unpleasant tasks [ 7 ], due to fear of failure [ 8 ], fear of success [ 9 ], or an expression of poor action control [ 9 ], and it has been consistently associated with low self-efficacy, e.g., [ 10 ].

Academic procrastination, a type of domain-specific behavior, refers to the tendency of students to delay or postpone completing academic tasks, such as studying for an exam, doing homework, or writing an essay, even though they know they should perform these actions and have a specific deadline for completion. Academic procrastination has garnered significant attention from researchers, primarily due to two factors: (a) Its high prevalence among university students and, on the other hand, (b) the ease of access to samples of students. Approximately 80% of college students are estimated to procrastinate, making it one of the most prevalent issues among post-secondary students, with estimates ranging from 10% to 70% [ 11 , 12 ]. Contrary to the previous statement, little research has been conducted to understand the characteristics of procrastination in younger age groups, e.g., [ 13 ]. However, researchers became interested in studying the behavior of children and adolescents during the prolonged periods of confinement due to the COVID-19 pandemic. Research in relation to procrastination focused primarily on the significant increase in the use of electronic devices and social media, as well as the procrastination of academic activities that were mandatory online during that period, e.g., [ 14 , 15 , 16 ].

Academic procrastination leads to a decline in students’ well-being. It has been associated with poor academic performance [ 17 ], emotional distress (stress, anxiety, and depression) [ 18 , 19 , 20 ], and physical health deterioration, e.g., [ 21 , 22 ].

One of the many unanswered questions regarding academic procrastination pertains to its development in students. Is academic procrastination a behavioral pattern that develops at an early age, or does it emerge as a reaction or response as students face the transition to university level? If the behavior does develop at an early age, what is its prevalence in primary and secondary education, what role do parents and the education system play in the development of the problem, and are there interventions to change children’s procrastinating behavior? If so, what factors are involved in promoting change?

The aim of this work has been to carry out a panoramic review of the empirical publications focused on academic procrastination in children and adolescents. We propose a scoping review that will also allow us: (a) To identify the production and evolution of publications on academic procrastination in primary and secondary education; (b) to specify the methodological basic characteristics of the studies; (c) to conduct a content analysis to categorize the correlates investigated in relation to academic procrastination in this age group and to determine the types of interventions reported (see Figure 1 ). The ultimate goal, which is the essence of a systematic scoping review, is to detect gaps in research to contribute to future lines of inquiry.

An external file that holds a picture, illustration, etc.
Object name is children-10-01016-g001.jpg

Flowchart of the specific objectives.

Following the methodology implemented in previous studies [ 23 , 24 ], this review includes several control mechanisms designed to reduce any bias that may exist a priori, as suggested by the PRISMA-ScR [ 25 ] (see Appendix A ) and the JBI Evidence Synthesis Manual [ 26 ]. The study was registered in the international registry for overview reviews and the protocol is available from OSF if required ( https://doi.org/10.17605/OSF.IO/KCXJ9 , accessed on 11 May 2023). Therefore, we developed a protocol that has allowed for uniform criteria to be applied to each of the registries, from the initial search for papers to the inclusion of the final papers.

2.1. Inclusion and Exclusion Criteria

Based on the research question and following an adaptation of the PICO strategy, a protocol called “d-Cocospe” (documents, concept, context, studies, participants, and evaluation) was designed following the indications of various authors, e.g., [ 27 ] (see Table 1 ).

Inclusion and exclusion criteria.

Criteria	Inclusion	Exclusion
Documents (d)	Journal articles, books, book chapters, and doctoral theses	Magazine articles, editorials, conferences, etc.
Concept (Co)	Academic procrastination	The rest
Context (Co)	Academic context	Other contexts (e.g., general, work or health procrastination)
Studies (s)	Empirical studies	Theoretical reviews and case studies
Participants (p)	Students under 18 years	University students or community samples
Evaluation (e)	Behavioral or reported procrastination measures (i.e., self-reports or hetero reports)	Single-item instruments or unstructured instruments

2.1.1. Documents

Both periodical (journal articles) and non-periodical (books, book chapters, and doctoral theses) publications were included. Magazine articles, editorials, conferences, and other similar types of documents were excluded.

2.1.2. Concept

The focus of the study was on publications in which academic procrastination was assessed.

2.1.3. Context

The study focused specifically on publications that assessed academic procrastination within an academic context. As a result, publications on general procrastination or on procrastination in other contexts (such as work, health, or leisure activities) were excluded.

2.1.4. Studies

Empirical studies were included and theoretical studies, literature reviews, and case studies were not considered.

2.1.5. Participants

The study subjects were exclusively students under 18 years and publications that included university students, community samples or those that did not specify the age or educational level of the participants were discarded.

2.1.6. Evaluation

Empirical studies in which the sample size and mode of assessment of procrastination was explicitly stated were included, if procrastination was assessed by behavioral or reported procrastination measures (i.e., self-reports or hetero reports). On the other hand, publications where procrastination was assessed by single-item instruments or unstructured instruments were excluded.

2.2. Search Strategy

The search equation was developed by the authors (MPGB, ASEP, and MALG) through initial exploratory searches in collaboration with expert documentalists from the Universidad Nacional de Educación a Distancia (UNED).

The final search was constructed by applying Boolean operators (AND & OR) and truncation (* and inverted commas) in TI, AB, KW: (“academic procrast*” OR “student procrast*”) AND (child* OR adolesc* OR young OR teen* OR school OR infan* OR boy* OR girl* OR junior OR kid)] including publications up to December 2022. In the ProQuest database, NOFT was used: [(“academic procrast*” OR “student procrast*”) AND (child* OR adolesc* OR young OR teen* OR school OR infan* OR boy* OR girl* OR junior OR kid)]. For WoS, the search was executed in TOPIC: [(“academic procrast*” OR “student procrast*”) AND (child* OR adolesc* OR young OR teen* OR school OR infan* OR boy* OR girl* OR junior OR kid)] including publications up to December 2022.

2.3. Sources of Information

The documentary search was carried out in July 2022. Subsequently, in March 2023, the references were updated, and the records found up to December 2022 were included to replicate the search and incorporate new records.

2.3.1. Formal Strategies

The records were obtained through different sources of information. First, 11 automated databases were consulted: (a) Thematic databases in the areas of Psychology and Education: ERIC, MedLine, Psychology and Behavioral Sciences Collection, PsycINFO, PubPsych, and Teacher Reference Center, and (b) multidisciplinary: Academic Search Ultimate, E-Journals, ProQuest, Scopus, and Web of Science. The search was conducted without language restrictions.

Second, the search was complemented by consulting documents located in different international repositories, such as Redined, Cogprints, Zenodo, BASE, NDLTD Networked Digital Library of Theses and Dissertations, OAIster, and arxiv. Bibliographic references were also analyzed to identify possible publications.

2.3.2. Informal Strategies

Academic social networks (e.g., ResearchGate, academia.edu, Dimensions, etc.) were also explored to locate publications by relevant researchers in the field.

2.3.3. Retrospective Strategies

The search was complemented by analysis of systematic reviews and meta-analyses to retrieve potentially relevant articles.

The searches were conducted without applying language restrictions to control for possible linguistic bias. In this regard, online translation (DeepL and Google Translator) of the original documents was used when necessary (e.g., texts in Turkish, Farsi, Chinese, Indonesian, etc.).

2.4. Coding and Identification of Records and Data Extraction

All records obtained from each database were exported to separate libraries in the EndNote 20.5 software package. EndNote 20.5 allowed for the files to be merged into a single library, which facilitated the removal of duplicate items. The records were then exported to a shared spreadsheet in Google Drive, in order that the authors could work collaboratively. After several online meetings, a protocol was established to create the analysis fields for processing each record (data charting). Then, a series of bibliometric data were included: (a) Year of publication; (b) authorship; (c) title; (d) journal or book name; (e) DOI; and (f) abstract. In addition, several enriched fields were added in accordance with the d-Cocospe format: (a) Document typology: Journal articles, books, book chapters, theses, etc.; (b) Concept: It was specified whether the document dealt with procrastination. If this concept was not attended, the topic of analysis was specified; (c) Context: In each record it was indicated whether procrastination was confined to the academic field, otherwise, the specific context was indicated; (d) Study: The type of study was indicated, specifying whether it was an empirical study, theoretical study, review or case study; (e) Participants: The following information was collected: (i) Number of participants in the study; (ii) type of participants: Students, general population, young volunteers, etc.; (iii) education level: Primary education (children aged 6–7 to 11–12) or secondary education (students aged 12 to 17–18); (iv) educational grade or rank; and (v) geographical origin of the sample; (f) Evaluation: Information on the assessment instrument used to measure academic procrastination was included; (g) Type of design: Experimental, quasi-experimental, ex post facto or observational; (h) Type of intervention: Content of the interventions, type of format (individual vs. group), intervention setting, structure of each session, and record of measures taken; (i) Analyzed variables: The variables analyzed were categorized according to different domains: Sociodemographic domain, learning domain, health domain, relational domain, and intrapersonal domain; (j) Outcomes; and (k) Conclusions in relation to academic procrastination.

During the coding and data extraction process, regular meetings were held to discuss inconsistencies, doubts, and disagreements. Each reference was independently analyzed by two researchers (MPGB and MALG), and both relevant and non-relevant records were recorded, in order that all the items were coded after reading the title, abstract, and full text.

The next phase of the review consisted of leading two types of analysis: (a) Thematic content analysis of the variables, and (b) classification of experimental interventions based on the analysis of the variables under study.

To carry out the thematic content analysis, a sequential procedure was followed: (a) Generation of a list of variables measured in the documents’ titles, abstracts, and full texts; (b) configuration of initial categories (a posteriori) through inductive analysis; (c) creation of a category tree, grouping them into conceptual families; and (d) re-labeling and reducing categories to a more inclusive level.

The intervention classification was carried out by categorizing the fields where the intervention was directed. Then, for each study, the type of technique or program implemented was collected. The type of design, variables, and most relevant results obtained were recorded.

Finally, we present the results using summary tables and figures created with Microsoft Excel (version 16.7), MapChart, MIRO (online whiteboard), and Infogram, along with a narrative description of the main findings.

3.1. Production and Evolution of Publications

The search strategies allowed us to identify 1185 documents, of which 79 met the criteria for inclusion, representing 6.67% of the initial records. Figure 2 shows the entire process carried out from the selection of formal and informal strategies used, as well as the number of records and the reasons for inclusion and exclusion of each document in each phase of the screening process.

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Flowchart of the review process. The literature search has been conducted using formal strategies (automated databases and references from selected documents), informal strategies (academic social networks) and retrospective strategies [ 4 , 28 , 29 , 30 , 31 ].

On the other hand, Figure 3 shows the diachronic evolution of publications on academic procrastination in students under 18 years of age. The first selected work was published in 1995, although the evolution of publications has been irregular until the last 15 years, with the peak of records in 2021 with 15 documents.

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Diachronic evolution of the literature on academic procrastination in children and adolescents. In this graphic, it have been indicated the most cited refs. [ 32 , 33 , 34 ].

A comparison is made between the initially retrieved publications and the documents finally included. Similarly, the three publications with the highest number of citations in the Web of Science database in 2023 are labeled and the only experimental studies conducted to date are marked.

3.2. Characteristics of the Studies

Using the prototypical structure of the “Methods” section of any scientific publication, the characteristics of the studies are presented below: Participants, assessment, and type of design (see Appendix B ).

3.2.1. Participants

In terms of sample size, a total of 34,563 participants were counted, with an average per study equal to 437.5 students (min. = 5 and max. = 1509); out of these, 8.86% were primary school students (participants in seven studies) and the rest, 91.14%, were secondary school students. In total, five of the studies analyzed had all-girl samples, e.g., [ 35 , 36 , 37 , 38 , 39 ] and one of them was aimed at studying academic procrastination in girls with learning disabilities, e.g., [ 38 ] (see Figure 4 for details).

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Infographic: Summary of study methodology.

Regarding the geographical distribution of the samples, Figure 5 specifies the number of publications in each of the continents and countries. Moreover, it lists the countries in which experimental and quasi-experimental studies have been carried out.

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Geographical distribution of samples in studies on academic procrastination in children and adolescents. Note: It should be highlighted that the number of publications does not correspond to the final count due to the presence of a cross-cultural study which includes two different countries.

3.2.2. Instruments for Assessing Academic Procrastination

In Table 2 , the 26 different assessment tools used in the retrieved documents are shown. In general, these are specific questionnaires and scales directly applied to children and adolescents (self-reports), although the participation of parents and teachers (hetero reports) is also reported.

Recount of instruments used.

Instrument	Count
Procrastination Assessment Scale-Student—PASS (Solomon and Rothblum, 1984 [ ])	23
Academic Procrastination Scale—AP-S (Çakıcı, 2003 [ ])	10
Tuckman Procrastination Scale—TPS (Tuckman, 1991 [ ])	8
Aitken Procrastination Inventory—API (Aiken, 1982 [ ])	5
General Procrastination Scale—GPS (Lay, 1986 [ ])	5
Escala de Procrastinación Académica—EPA (Busko, 1998 [ ])	4
Academic Procrastination Questionnaire (Huang, 2009 [ ])	2
Scale developed by authors (Dietz et al., 2007 [ ])	3
Academic Procrastination Scale—APS-S (McCloskey, 2011 [ ])	2
Academic Procrastination Inventory for Middle School Students—API-MSS (Zuo, 2020 [ ])	1
Academic Procrastination Questionnaire (Ran, H., 2010 [ ])	1
Academic Procrastination Scale—MSLQ (Lay and Silverman, 1996 [ ])	1
Academic Procrastination Scale—APS (Lay, 1986 [ ])	1
Academic Procrastination Student Form—APS (Milgram and Amir, 1998 [ ])	1
Academic Procrastination Survey (Savari, K., 2011 [ ])	1
Cuestionario de Procrastinación en el Estudio (CPE; Rosário et al., [ ])	3
Irrational Procrastination Scale—IPS (Steel, 2010 [ ])	1
Melbourne Decision Making Questionnaire (five items procrastination) (Mann et al., 1997 [ ])	1
Scale developed by authors (Depreeuw and Lens, 1998 [ ])	2
Scale developed by authors (Santyasa et al., 2020 [ ])	1
Scale developed by authors (Shih, 2016 [ ])
Scale developed by authors (Ocak and Karatas, 2019 [ ])	1
Academic Procrastination Scale—DPS (Ferrari et al., 1995 [ ])	1
Total	79

The authors indicate that the instruments used were adapted to the language of the sample; however, they are not questionnaires created specifically for children and adolescents, but mostly validated scales in university students that are also applied in individuals under 18 years old.

The most used instrument is the Procrastination Assessment Scale-Student/PASS, but typically only the first part of the scale, which is designed to measure the frequency of procrastination, is utilized.

3.2.3. Methodology of the Studies and Type of Design

In 97.47% of the studies, a quantitative methodology was employed, while 2.53% utilized qualitative or mixed method approaches. Regarding the design of quantitative studies, 86.07% were ex post facto designs (with an average sample size of 458.64 students), encompassing correlational or group comparison analyses. Additionally, 11.39% of the studies were experimental and quasi-experimental designs, including six randomized controlled trials (RCTs), two randomized non-controlled trials (RNCTs), and one pre-experimental study, with an average sample size of 197.5 students (see Figure 4 ). Within the ex post facto studies, correlation and regression analyses were predominantly observed, with a recent emergence of analyses based on structural equation modeling (SEM) in recent years.

Regarding studies that incorporate qualitative methodology, they have focused on children’s opinions about behaviors related to procrastination. In one of them [ 59 ], children were asked about the reasons for procrastination, strategies to avoid it, and suggestions to reduce this behavior. In this sense, boys mentioned playing computer games as a substitute behavior for studying, while girls preferred activities, such as reading books. The problematic use of mobile phones and the internet has also been studied (e.g., [ 59 ]). In this case, students considered access to information as a positive aspect of using the internet, and time loss as a negative aspect.

3.3. Content Analysis

3.3.1. investigated correlates.

The thematic content analysis reveals four main dimensions: Parental and teacher variables (teacher and family), personal variables (sociodemographic, personality, motivation, emotional/affective), mental and well-being (psychopathology, alternative or competitive behaviors, and well-being and quality of life), and variables of the learning cycle (self-regulated learning strategies, performance, and school).

In Figure 6 , the set of variables analyzed in the documents (i.e., those that have been measured) is detailed. The most studied variables correspond to dimensions related to the learning cycle, and self-regulated learning occupied the focus of most of this dimension. Thirty-four studies focused on variables related to this process, specifically self-regulated learning, e.g., [ 60 , 61 , 62 ], time management, e.g., [ 57 , 63 , 64 ], and inattention or management of distractions, e.g., [ 62 , 65 ]. Regarding previous learning experience and performance [ 33 ], we analyzed the role of experience in receiving low grades on exams and assignments and its relationship with academic procrastination. All studies that included performance and academic achievement variables, a total of 22, e.g., [ 52 , 66 , 67 ] were included in this category.

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Categories and analyzed variables.

On the other hand, studies that relate personality variables to academic procrastination are also among the most numerous, with a total of 31, highlighting self-efficacy or self-efficacy beliefs, e.g., [ 62 , 68 , 69 , 70 , 71 , 72 ], perfectionism, e.g., [ 57 , 68 , 73 , 74 , 75 ], and self-esteem, e.g., [ 33 , 67 ]. Other less common constructs are resilience, locus of control, and persistence.

The relationship between emotional and affective variables with academic procrastination was studied in 11 publications, with test anxiety being the most frequent variable, e.g., [ 75 ]; but not only related to evaluation, but also general academic anxiety, anxiety in the study process or for specific subjects, e.g., [ 32 , 76 ].

A specific area of study in this population of children and adolescents is parental support and the development of autonomy; a total of 18 publications analyzed variables related to the quality of parental relationships, parenting style, and parental involvement in education, e.g., [ 72 , 76 , 77 , 78 , 79 ], attention to the promotion of autonomy or excessive parental control, e.g., [ 78 , 79 ].

Alternative behaviors to studying, such as mobile phone use or internet browsing, have also been of interest to researchers, with nine publications dedicated to exploring the relationship between academic procrastination and these types of behaviors, e.g., [ 14 , 16 , 80 , 81 ].

Among the retrieved documents, there are also publications that study academic procrastination in the context of psychopathology, such as three studies that analyze procrastination behavior associated with addictive behaviors related to the internet, use of social networks, and electronic devices, e.g., [ 82 , 83 , 84 ], social anxiety disorder [ 80 , 82 ], and obsessive-compulsive symptoms [ 70 ].

3.3.2. Types of Interventions

Regarding the retrieved experimental studies and considering the intervention area, a total of nine studies were selected. These studies can be classified, according to their practical perspective, as follows: Healthcare (1); educational (2); psychoeducational (4); psychotherapeutic (1); and mixed (1).

From a broad socio-sanitary perspective, with an emphasis on child health within social, family, and educational context, Kocoglu and Emiroğlu [ 85 ] studied the impact of a school nursing service on the academic performance of 4th-grade students. The authors reported an increase in academic performance, while decreasing absenteeism and academic procrastination behaviors.

On the other hand, from an educational framework, Santyasa et al. [ 56 ] compared two teaching methods in two groups of students: A project-based learning (PjBL) model and a direct instruction (DI) model. They found that the project-based method improved academic performance, but this occurred primarily in the group of students with low levels of academic procrastination. From the same perspective, Dong and Izadpanah [ 35 ] compared the effect of corrective feedback after formative feedback in an experimental group with control group (women), obtaining improvement in academic resilience, educational belonging, and reduction in procrastination compared to the control group.

From a psycho-educational framework, Ghadampour [ 86 ] trained a group of female students in learning strategies (cognitive and metacognitive): The experimental group showed a reduction in procrastination and an increase in self-efficacy that was maintained during the follow-up phase, with significantly differential effects compared to the control group without intervention. Additionally, Motie [ 87 ] reported a study based on training in self-regulated learning strategies (boys) in an experimental group design with a control group, which resulted in a decrease in procrastination behavior in the experimental group. Moreover, from the psycho-educational framework, Jaradat [ 88 ] reported a study based on three groups: One assigned to cognitive therapy, study skills counseling, and a control group (waiting list). The two experimental groups improved satisfaction with studying in the post-test. The study skills counseling group showed a greater reduction in academic procrastination than the CT group, which decreased exam anxiety but not procrastination. Finally, Yildiz and Iskender [ 89 ] implemented a psycho-educational program aimed at secure attachment style in a control group (13 years old) that decreased intolerance of uncertainty and academic procrastination in the experimental group, compared to the control group without intervention, with effects that persisted after the program ended.

From a clinical psychological perspective, Lubis and Djuwita [ 37 ] intervened online and in a group format, through a CBT program aimed at academic procrastination, with five girls during the COVID-19 pandemic, with a pre-post and follow-up design. The authors reported that the intervention decreased levels of procrastination.

From a mixed perspective, Kheirkhah [ 36 ] trained girls in self-efficacy and learning strategies during eight sessions. The intervention significantly decreased procrastination compared to the control group without intervention.

4. Discussion

The aim of this work has been to carry out a scoping review of the empirical publications focused on academic procrastination in children and adolescents.

Regarding the first objective, which aims to identify the production and evolution of publications on academic procrastination in primary and secondary education, the results indicate a lack of significant interest from researchers in this topic.

In contrast to academic procrastination in university students [ 17 , 29 , 90 , 91 , 92 ], there has been a clear increase in the number of publications since 2020. In fact, 45.5% of the retrieved records are dated after this year. It is worth noting that 2020 was the year of the pandemic, with widespread confinement and the consequent implementation of online education systems at all levels [ 93 ], as well as a significant rise in the use of social media. Therefore, it is highly likely that this newfound interest has been influenced by the circumstances that occurred. Only time will tell if this trend continues in the coming years.

Regarding the second objective of identifying the common methodological characteristics of the studies, the most relevant data showed that academic procrastination has been predominantly studied in secondary education (92%). In terms of sample sizes, it is noteworthy that 90% of the studies were conducted with very large (>1000) or large (>350 students) samples. It is striking that only 11.4% of the studies were experimental in nature; the majority of the research has focused on exploring relationships between variables. Hopefully, in the future, there will be initiatives aimed at detecting and specifically intervening in this age group.

A high proportion of the retrieved studies came from Eastern countries, with a very limited presence of studies from Europe and America. These findings are similar to those reported by Lu et al. [ 40 ], in their meta-analysis on sociodemographic characteristics and procrastination, the authors found a large sample of studies with Chinese participants. On the other hand, Mann [ 94 ], in an exploratory study on cultural differences in decision-making in six cultures (USA, Australia, New Zealand, Japan, Hong Kong, and Taiwan), found that Asian students scored higher in academic procrastination compared to Western students, which could justify the differential interest shown by Eastern researchers compared to Western ones. This imbalance is even more pronounced if we consider that there is an Anglo-Saxon bias in the location of international literature in this review.

Within the methodological aspects, the analysis of the measurement instruments used to assess academic procrastination in students under 18 years revealed that many of the scales commonly used have not been specifically created for this age group [ 40 ]. This can represent a methodological limitation and a challenge when interpreting results. This highlights the need to reflect on more appropriate evaluation methods for children. For example, the development of observational scales could be a promising approach.

Recording the third objective, which aimed to conduct a content analysis to categorize the correlates investigated in relation to academic procrastination and determine the types of interventions reported, four topics were identified. The content analysis yielded four topics, including constructs related to self-regulated learning [ 95 , 96 ] and a range of personality, motivational, e.g., [ 97 ], and emotional variables [ 98 , 99 ], which were expected to be present. Additionally, a third topic emerged concerning the relationships established with significant individuals and the influence of specific variables, such as parenting style, excessive demands, and overprotection on the development of autonomy and self-control in children. Furthermore, an existing but less developed topic pertains to the relationship between procrastination and physical and mental health variables. While this is an emerging area, likely linked to post-pandemic studies, it requires greater attention. We cannot forget that this relationship has been well-studied in academic procrastination in adults [ 22 , 82 ].

The intervention studies reviewed, despite being scarce, show great methodological diversity in terms of sample size and characteristics (e.g., exclusively girls), making it impossible to draw conclusions on effectiveness. This, however, goes beyond the scope of this review. Nevertheless, given the insufficient number of studies found and the methodological problems detected, we can conclude that we are not yet close to conducting a meta-analysis focused exclusively on this age group and being able to answer specific questions.

Building on the previous discussion, we cannot only consider methodological issues related to assessing procrastination in children or the variables that explain the complexity of the construct, but also broader questions such as: What are the limits of the concept of procrastination in childhood? What is the alternative or competing behavior to completing necessary and obligatory tasks in children? Can we say that a child who is playing rather than doing their homework is procrastinating? On the other hand, should we ask ourselves if an inactive child who is not playing is also procrastinating? We believe that one of the important tasks of childhood is the development of play, pleasure, and a sense of fun, and we wonder when the “obligation of tasks” begins to give way to procrastination. The role of school in this regard seems to be very relevant, and it is logical to think that they have the possibility to detect and intervene early.

Is there procrastination in a childhood free of mandatory tasks? We can evoke hundreds of images of children demanding constant activity and insatiable curiosity. At what point, and due to what factors, does learning—or its tasks—become “procrastinatable”?

The analyzed studies do not address these issues. They also do not study, longitudinally, the transition through the stages or levels of study and the adaptation to the different changes and growing demands for autonomy, and how they impact procrastination behavior. In studies with university students, the relationship between academic procrastination and the change in the educational system has been discussed: The autonomy granted by higher education can be a stressor or a promoter of excessive flexibility, which in turn, may favor the emergence of procrastination behavior. In this regard, special attention has been paid to integration during the first year, due to the high dropout rates observed in this course and its relationship with academic self-efficacy, e.g., [ 97 ], use of self-regulation strategies, academic engagement, e.g., [ 100 ], and academic burnout, e.g., [ 101 ]. It is valid to question whether throughout primary and secondary education, the processes of change and transition between these systems present, in a similar way to what has been studied in university samples, critical moments related to the loss of self-regulatory processes and the consequent emergence of greater procrastination. Therefore, longitudinal and long-term studies are necessary.

This scoping review has inherent limitations associated with its scope and breadth, as it aims to provide a comprehensive overview of the literature rather than conduct a detailed assessment of individual study biases. Therefore, the assessment of individual study bias has not been conducted in this review. It is important to acknowledge these limitations when interpreting the findings and to consider them in future research.

5. Conclusions

In this study, we have found that there is a large proportion of publications reporting on the relationship between academic procrastination and personality, motivational, and self-regulation variables (both of learning and emotion) in childhood and adolescence. However, our analysis of the classification categories has allowed us to identify gaps in the literature, such as the need to define and operationalize the construct of procrastination in this age group and to develop appropriate assessment tools and techniques. Overall, it is not clear how prevalent the problem of academic procrastination is in this age group, while it is well-known to be a major problem in higher education, with broad consequences for psychological emotional and physical health, academic performance, and well-being.

Based on the studies analyzed here, the authors argue for the need to clarify, through different methodological strategies, the role of promoting autonomy, developing self-regulation and self-control in childhood, and how they relate to parenting styles, educational models, and teaching strategies. Emphasizing the possibility of early detection and intervention methods may pave the way for reducing the high prevalence of procrastination in university students.

The main conclusions are as follows:

Addressing academic procrastination in children and adolescents should consider both individual and contextual factors, as well as appropriate interventions;
There is a need for the development of more appropriate assessment tools to measure academic procrastination in children and adolescents, considering their specific developmental characteristics;
The prevalence of academic procrastination in this population is still understudied, highlighting a research gap that requires further attention;
In summary, further research and interventions are necessary to improve the understanding and management of academic procrastination in children and adolescents.

In order to enhance knowledge in this field, it is suggested to develop methodological improvements (such as the use of appropriate instruments, operationalization of the target behaviors under study, and high-quality research reporting) and promote the implementation of policies that provide specific financial support to foster collaboration between institutions and different knowledge areas.

Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist [ 25 ].

Section	Item	PRISMA-ScR Checklist Item	Reported on Page # (Number)
Title
Title	1	Identify the report as a scoping review.	1
Abstract
Structured summary	2	Provide a structured summary including, as applicable: Background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review question(s) and objective(s).	1
Introduction
Rationale	3	Describe the rationale for the review in the context of what is already known. Explain why the review question(s)/objective(s) lend themselves to a scoping review approach.	1–2
Objectives	4	Provide an explicit statement of the question(s) and objective(s) being addressed with reference to their key elements (e.g., population or participants, concepts, and context), or other relevant key elements used to conceptualize the review question(s) and/or objective(s)).	2
Methods
Protocol, and registration	5	Indicate if a review protocol exists, if and where it can be accessed (e.g., web address), and, if available, provide registration information including registration number.	3
Eligibility criteria	6	Specify the characteristics of the sources of evidence (e.g., years considered, language, publication status) used as criteria for eligibility, and provide a rationale.	3, 6
Information sources	7	Describe all information sources (e.g., databases with dates of coverage, contact with authors to identify additional sources) in the search, as well as the date the most recent search was executed.	4
Search	8	Present the full electronic search strategy for at least one database, including any limits used, in order that it could be repeated.	4
Selection of sources of evidence	9	State the process for selecting sources of evidence (i.e., screening, eligibility) included.	5
Data charting process	10	Describe the methods of charting data from the included sources of evidence (e.g., piloted forms, forms that have been tested by the team before their use, whether data charting was carried out independently, in duplicate) and any processes for obtaining and confirming data from investigators.	5
Data items	11	List and define all variables for which data were sought and any assumptions and simplifications made.	5
Critical appraisal of individual sources of evidence	12	If performed, provide a rationale for conducting a critical appraisal of included sources of evidence, describe the methods used, and how this information was used in any data synthesis (if appropriate).	N/A
Summary measures	13	Not applicable for scoping reviews.
Synthesis of results	14	Describe the methods of handling and summarizing the data that were charted.	5
Risk of bias across studies	15	Not applicable for scoping reviews.
Additional analyses	16	Not applicable for scoping reviews.
Results
Selection of sources of evidence	17	Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.	6
Characteristics of sources of evidence	18	For each source of evidence, present characteristics for which data were charted and provide the citations.	6, 7, 8, 10
Critical appraisal within sources of evidence	19	If performed, present data on critical appraisal of included sources of evidence (see item 12).
Results of individual sources of evidence	20	For each included source of evidence, present the relevant data that were charted that relate to the review question(s) and objective(s).
Synthesis of results	21	Summarize and/or present the charting results as they relate to the review question(s) and objective(s).	5, 6, 7, 8, 9
Risk of bias across studies	22	Not applicable for scoping reviews.
Additional analyses	23	Not applicable for scoping reviews.
Discussion
Summary of evidence	24	Summarize the main results (including an overview of concepts, themes, and types of evidence available), explain how they relate to the review question(s) and objectives, and consider the relevance to key groups.	11–12
Limitations	25	Discuss the limitations of the scoping review process.	13
Conclusions	26	Provide a general interpretation of the results with respect to the review question(s) and objective(s), as well as potential implications and/or next steps.	13
Funding
Funding	27	Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.	14

Summary of Scoping Review.

Bibliometric Information			Sample			Design
Author	Year	Doc. Type	Edu Level	N	Country	Design
Milgram et al. [ ]	1995	Article	SS	195	ISR	EPF
Owens and Newbegin [ ]	1997	Article	PS	418	AUS	EPF
Milgram and Toubiana [ ]	1999	Article	SS	245	ISR	EPF
Owens and Newbegin [ ]	2000	Article	SS	380	AUS	EPF
Davis [ ]	2001	Thesis	SS	284	USA	EPF
Nadeau et al. [ ]	2003	Article	SS	100	CAN	EPF
Jaradat [ ]	2004	Thesis	SS	729	JOR	RCT
Dietz and et al. [ ]	2007	Article	SS	704	DEU	EPF
Rosário et al. [ ]	2008	Article	SS	533/796	PRT	EPF
Klassen and Kuzucu [ ]	2009	Article	SS	508	TUR	EPF
Klassen et al. [ ]	2009	Article	SS	612	CAN, SGP	EPF
Uzun Özer [ ]	2009	Article	SS	223	TUR	EPF
Rosário et al. [ ]	2009	Article	SS	580/809	PRT	EPF
Al-Attiyah [ ]	2010	Article	PS	538	QAT	EPF
Kalafat et al. [ ]	2010	Article	SS	285	TUR	EPF
Kuhnle et al. [ ]	2011	Article	SS	348	DEU	EPF
Uzun Özer and Ferrari [ ]	2011	Article	SS	214	TUR	EPF
Liu and Lu [ ]	2011	Article	SS	712	CHN	EPF
Hofer et al. [ ]	2012	Article	SS	697	DEU	EPF
Motie et al. [ ]	2012	Article	SS	250	IRN	EPF
Mih [ ]	2013	Article	SS	189	ROU	EPF
Motie et al. [ ]	2013	Article	SS	66	IRN	RCT
Bong et al. [ ]	2014	Article	SS	306	KOR	EPF
Katz et al. [ ]	2014	Article	PS	171	ISR	EPF
Tang et al. [ ]	2014	Article	SS	460	CHN	EPF
Hong et al. [ ]	2015	Article	SS	597	TWN	EPF
Jahromi et al. [ ]	2015	Article	SS	268	IRN	EPF
Gazidari et al. [ ]	2016	Article	SS	311	IRN	EPF
Ng [ ]	2016	Article	SS	442	SGP	EPF
Shih [ ]	2016	Article	SS	405	TWN	EPF
Zohreyi et al. [ ]	2016	Article	SS	210	IRN	EPF
Chen and Han [ ]	2017	Article	SS	577	CHN	EPF
Günlü and Ceyhan [ ]	2017	Article	SS	1088	TUR	EPF
Kocoglu and Emiroglu [ ]	2017	Article	PS	31	TUR	RNCT
Shih [ ]	2017	Article	SS	405	TWN	EPF
Yang et al. [ ]	2017	Article	SS	360	CHN	EPF
Borekci and Uyangor [ ]	2018	Article	SS	496	TUR	EPF
Jiang et al. [ ]	2018	Article	SS	848	KOR	EPF
Korkmaz et al. [ ]	2018	Article	SS	167	TUR	EPF
Mostafa [ ]	2018	Article	SS	100	EGY	EPF
Ziegler and Opdenakker [ ]	2018	Article	SS	566	NLD	EPF
Ghadampour and Beiranvand [ ]	2019	Article	SS	30	IRN	RCT
Macias and Nevarez [ ]	2019	Article	SS	300	MEX	EPF
Ahmadi et al. [ ]	2020	Article	SS	149	IRN	EPF
Sula Atas and Kumcagiz [ ]	2020	Article	SS	602	TUR	EPF
Durak [ ]	2020	Article	SS	451	TUR	EPF
Eissa and Khalifa [ ]	2020	Article	SS	228	EGY	EPF
Gündüz [ ]	2020	Article	SS	358	TUR	EPF
Lenggono and Tentama [ ]	2020	Article	SS	60	IDN	EPF
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Note: Edu Level (Educational Level): PS (Primary School) and SS (Secondary School). Country: AUS (Australia), CAN (Canada), CHN (China), DEU (Germany), EGY (Egypt), IDN (Indonesia), IRN (Iran), ISR (Israel), JOR (Jordan), KOR (South Korea), MEX (Mexico), MOZ (Mozambique), NLD (Netherlands), PER (Peru), PRT (Portugal), QAT (Qatar), ROU (Romania), SAU (Saudi Arabia), SGP (Singapore), TUR (Turkey), TWN (Taiwan), and USA (United States). Design: EPF (Ex Post Facto), PE (Pre-Experimental study), RCT (Randomized Controlled Trial), and RNCT (Randomized non-Controlled Trial).

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.P.G.-B., A.S.-E.P. and M.Á.L.-G.; methodology, M.P.G.-B. and M.Á.L.-G.; software, M.Á.L.-G.; formal analysis, M.P.G.-B. and M.Á.L.-G.; investigation, M.P.G.-B. and M.Á.L.-G.; resources, M.P.G.-B., A.S.-E.P. and M.Á.L.-G.; data curation, M.P.G.-B. and M.Á.L.-G.; writing—original draft preparation, M.P.G.-B. and M.Á.L.-G.; writing—review and editing, M.P.G.-B., A.S.-E.P. and M.Á.L.-G.; visualization, M.P.G.-B., A.S.-E.P. and M.Á.L.-G.; supervision, M.P.G.-B. and M.Á.L.-G. All authors have read and agreed to the published version of the manuscript.

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Ethics in Patients’ Health Literacy: a scoping review and a critical discussion

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Melina Evripidou, Areti Efthymiou, Venetia Velonaki, Athina Kalokairinou, Evridiki Papastavrou, Ethics in Patients’ Health Literacy: a scoping review and a critical discussion, Health Promotion International , Volume 39, Issue 4, August 2024, daae100, https://doi.org/10.1093/heapro/daae100

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A growing body of literature has acknowledged that a high number of populations with low Health Literacy (HL) is related to poor health outcomes, inequities in healthcare and high economic costs. Those findings have formulated the research questions of this review: (i) what ethical issues arise within the context of patients’ HL and (ii) What is the relationship between HL and quality of life? This review followed the guidelines of Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) and it was conducted in five databases: PubMed, CINAHL, MEDLINE, Scopus and Science Direct between June 2022 and December 2023. Out of the 3164 titles retrieved, 285 abstracts were eligible to proceed. Following a thorough examination of the full text of 61 papers, 45 sources were identified that met the inclusion criteria. The data analysis process was guided by the research questions, employing a thematic approach. Four themes were identified: the use of language and patient understanding, human rights, the principlism approach (justice, beneficence, non-maleficence and autonomy) and quality of life. The first theme mainly focused on the relation of HL with the notion of consent forms and national action plans. Human rights in relation to HL were discussed as a minor issue. The bioethical framework by Beauchamp and Childress (Principles of Biomedical Ethics, 6th edn. Oxford University Press, New York, NY, 2009), was addressed by several studies, with a particular focus on justice and the loss of autonomy. Quality of life indicated a positive correlation with HL by most of the authors, while few studies revealed a moderate correlation.

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Exploring 40 years on affective correlates to procrastination: a literature review of situational and dispositional types

Published: 14 January 2022
Volume 41 , pages 1097–1111, ( 2022 )

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Reza Feyzi Behnagh ORCID: orcid.org/0000-0002-4109-3501 1 &
Joseph R. Ferrari 2

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The relationship between different emotions with situational (e.g., academic) and dispositional (chronic) procrastination was examined extensively in the literature since the early days of procrastination research. A review of empirical studies over the past 40 years might shed light on the role of emotions in procrastination in different contexts with different populations. The current paper reviewed 83 studies (from 1977 to 2021) exploring the relationship between 9 different emotions and situational and dispositional procrastination. The emotions examined, listed in the order of the extent of focus of scholarly research are: anxiety, fear, shame, guilt, regret, boredom, frustration, anger, and revenge. Findings highlight the important role of emotions as motives, antecedents, correlates, or consequences of situational and dispositional procrastination. Based on the findings, a lack of a comprehensive theory summarizing dispositional and situational procrastination is pointed out and avenues for future research are outlined and recommended.

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Does Rumination Mediate the Unique Effects of Shame and Guilt on Procrastination?

Understanding academic procrastination: A Longitudinal analysis of procrastination and emotions in undergraduate and graduate students

Procrastination and depression from a cognitive perspective: an exploration of the associations among procrastinatory automatic thoughts, rumination, and mindfulness, explore related subjects.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Feyzi Behnagh, R., Ferrari, J.R. Exploring 40 years on affective correlates to procrastination: a literature review of situational and dispositional types. Curr Psychol 41 , 1097–1111 (2022). https://doi.org/10.1007/s12144-021-02653-z

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Characteristics of Academic Procrastination

How Is Academic Procrastination Measured?

Detrimental Effects of Academic Procrastination

Social and Contextual Factors Facilitating Procrastination

Large Degree of Freedom in the Study Situation

Relation to the Academic Environment

Long Deadlines

Task Aversiveness

Temptations and Distractions

Limited Information for Proper Self-Monitoring

Low Focus on Study Skills Training

Lack of Self-Efficacy-Building Opportunities

Inefficient Group Work

Influence of Peers

Do the Factors Point to Common Problem Areas?

Given the Large Number of Factors Discussed, Are Some Particularly Important?

Does It Make Sense to Implement Changes in One or Few Factors, Leaving Out Others?

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Predictive analysis of college students’ academic procrastination behavior based on a decision tree model

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Methods for measuring career readiness of high school students: based on multidimensional item response theory and text mining

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Literature review

The predictor of academic procrastination

Research Method

Subjective well-being scale

Smartphone addiction scale

Depression, anxiety, and stress scale

Rosenberg self-esteem scale

Life autonomy scale

Pro-environmental behavior scale

Psychological sense of school membership scale

Academic procrastination scale

Statistical analyses

Data coding

The construction of decision tree

Pruning of the decision tree

Evaluation of the decision tree

Descriptive statistics

Prediction analysis of academic procrastination

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Academic Procrastination in Children and Adolescents: A Scoping Review