teaching styles research papers

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• Published: 10 August 2020

ELT teachers’ epistemological beliefs and dominant teaching style: a mixed method research

• Neda Soleimani 1  

Asian-Pacific Journal of Second and Foreign Language Education volume  5 , Article number:  12 ( 2020 ) Cite this article

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Despite voluminous studies into teachers’ cognition and the role of teachers’ beliefs in their practices, not many studies have addressed the impact of teachers’ epistemological beliefs (EB, beliefs about the source of knowledge) on their teaching styles. In this study, a mixed design research method was adopted to show English language teachers’ epistemological beliefs, teaching styles and possible relationships between EB and teaching styles. To do this, a sample of 200 EFL teachers participated in the quantitative phase of the study to fill out the teaching style inventory (Grasha, Teaching with style, 2002) and epistemological beliefs scale (Chan and Elliott, Teaching and Teacher Education 20:817–831, 2004). In qualitative phase, 20 participants of this sample volunteered to attend the semi-structured interviews. Both quantitative and qualitative analyses indicated that facilitator style is the dominant style of teaching and learners should not assume teachers as mere source of knowledge and should use different resources to learn the language. Regression analyses and interview results showed that EFL teachers teaching style is informed by their EB. Implication and limitation of the research were discussed in detail in the paper.

Introduction

Epistemological beliefs (EBs hereafter) describing “individual representations (beliefs) about knowledge and knowing” (Mason & Bromme, 2010 , p. 1) guide teachers’ behaviors and attitudes towards issues in classroom context (Araga˜o, 2011 ). EB describes teachers’ beliefs about how knowledge is acquired. Teachers’ EB is assumed to play a decisive part regarding how a teacher interprets knowledge, justifies the structure and source of information, and more generally how learning process unfolds (Gholami & Husu, 2010 ).

Viewed as a significant component of teaching practicum in teacher education programs (Borg, 2006 ; Tang, 2007 ), teachers’ initial conceptualization of teaching, pedagogical decisions and practices and whatever happening in a classroom are filtered through teachers’ EB (Cheng, Chan, Tang, & Cheng, 2009 ). Put it more specifically, EB has found its status in education as a variable affecting many learning and teaching related issues including learning motivation and strategies (Duell & Schommer-Aikins, 2001 ), and instructional practices (Ng, Nicholas, & Williams, 2010 ). As a hallmark of instructional practices, teaching styles might be justified through teachers’ EB as well (Ng et al., 2010 ). Defined as a “predilection toward teaching behavior and the congruence between educators’ teaching behavior and teaching beliefs” (Heimlich & Norland, 1994 , p. 34), teaching style is a manifestation of teachers’ hidden assumptions and beliefs about what to do and what not to do in a classroom, tasks to be covered, materials to be selected and teacher-student interaction (Braten & Stromso, 2005 ; Buehl, 2003 ). A line of demarcation must be drawn between teaching style and strategies. As teaching strategies are referred to a generalized lesson plan and teachers’ behavior in terms of goals of instruction. Comparing to teaching styles which deal with theoretical mindsets that a teacher follows, strategies include practical items and performance a teacher does in classroom.

Cognizant of EB role in elucidating on teachers’ practices (Roth & Weinstock, 2013 ), a plethora of recent studies concentrated on how EB pinpoints the connection between beliefs and practices. Results of the earlier studies mainly highlighted the diversity of EB depending on teachers’ background, subject matter and contextual variables (Kienhues, Bromme, & Stahl, 2008 ). That said, studying the connection between EB in different disciplines (e.g. mathematics, psychology, etc.) and pedagogical aspects has been an interesting area of research (Hofer & Pintrich, 2002 ). However, most of extant studies have been quantitative in nature and empirical studies focusing on the possible connection between teachers’ EB and actual classroom practices and instructional preferences are missing (Sosu & Gray, 2012 ).

Despite the amplitude of the studies into EB of teachers in different subject matters, English teachers’ EB and the way through which EB could possibly find its pattern in and inform teaching styles seem to be far less investigated. Similar to other areas of teacher education, teaching styles might be rooted in how teachers perceive knowledge and define knowledge gaining resources. This under-investigated claim stimulated our study and motivated the researcher to do the current inquiry. Informed by Grasha's ( 2002 ) model of teaching style and Chan and Elliott's ( 2004 ) model of EB as the theoretical frameworks, this paper focused on EFL teachers’ EB, teaching styles and the likelihood of connection between these two attributes.

To operationalize teaching style, the researcher used the definition proposed by Grasha ( 2002 ) whereby teaching style referred to a set of teachers’ decisions regarding setting goals of teaching, managing classroom and learning tasks. This study used Chan and Elliott's ( 2004 ) model of EB to approach English teachers’ beliefs about knowledge and knowledge construction . To operationalize this variable and measure teachers’ EB, we used an EB questionnaire developed by the same authors (See the appendices for scales of measuring these variables).

Theoretical framework

Epistemological beliefs.

Bandura ( 1986 ) defined beliefs as a set of personal values filtering individuals’ behavior and decisions as well as knowledge acquisition. Personal beliefs are believed to underpin what teachers do and how they behave in classroom (Olafson & Schraw, 2006 ). Findings of the root of beliefs and the way that beliefs are created have been the topic of a plethora of studies into education.

Studying EBs has been recognized to be the first step toward triggering changes into teacher education programs and developing new insights into teaching profession (Lee & Schallert, 2016 ). Analyzing elementary school teachers’ beliefs about learning and teaching, Nespor ( 1987 ) suggested that teachers’ EBs either implicitly or explicitly influence learners’ beliefs about knowledge acquisition and curriculum development. A similar idea has also implied by Schraw and Olafson ( 2003 ) who labeled teachers’ beliefs as important predictors of pedagogical outcomes. More specified questions, however, are related to the aspects of teaching behaviors affected by EBs. According to Donmoyer ( 2001 ) EBs are determinant elements defining how a teacher reacts in a classroom. Likewise, Tsai ( 2002 ) asserted daily routines and teachers’ practices, deciding what to teach or the content of instruction mirror teachers’ EBs about learners’ role in knowledge construction and status of curriculum. Extending studies into EBs, Tsai and Kuo ( 2008 ) also proposed that classroom practices, classroom climate and management and instructional tasks are filtered by positivistic and or constructivist views. Similarly, Yang, Chang, and Hsu (2008) argued that teachers’ EB was an important sign of teachers’ values and how knowledge is shaped in learners. Extant studies showed that teachers holding more relativist and sophisticated EB tended to celebrate learners’ latitude in constructing knowledge and classroom performance. Whereas teachers holding naïve epistemological beliefs tend to be more teacher-centered in their decision-makings and practices in classroom (Chan, 2003 ).

Since the introduction of EB, many issues have been raised, added and modified by different scholars (Buehl, 2003 ; Gholami & Husu, 2010 ; Schommer-Aikins, Duell, & Hutter, 2005 ), yet the core meaning and main elements of epistemological beliefs are more or less the same (Hofer & Pintrich, 1997 ). Perry ( 1970 ) is one of the pioneering scholars whose study into EB paved the way and provided illuminating insights for the later scholars. He approached EB from a developmental perspective to show how a person progresses through different stages to come up with EB. Perry's ( 1970 ) study showed that primary and less experienced learners begin with dualist beliefs whereby knowledge is absolute and teachers are responsible for imparting the knowledge bases to the learners. In next level, a person develops the idea of multiplism and views knowledge from more than one source, though the idea of a special truth to be discovered is the matter of debate. Relativism was the third dimension added to this model later on showing that knowledge acquisition sources are relative. The final and more sophisticated stage of this model is commitment within relativism involving personal judgment and evaluations incorporated with the commitment to the beliefs (Brownlee, Purdie, & Boulton-Lewis, 2001 ; Hofer, 2001 ).

Schommer ( 1990 ) criticized Perry’s classification of EB for being developmental and unidimensional and revisited the model. She proposed a belief system containing five dimensions: certainty of knowledge (ranging from absolute to tentative), structure of knowledge (from simple to complex), source of knowledge (given by an authority or created by personal reasoning), control of knowledge (fixed to changing and dynamic ability to learn something), and speed of knowledge acquisition (quick to gradual knowledge acquisition). Schommer used a quantitative approach to check the relationships between academic cognition and performance and developed a 63-item scale to examine the belief system. Schommer ( 1990 ) and Schommer, Crouse, and Rhodes ( 1992 ) conducted series of factor analyses and found four factors: simple knowledge, certainty of knowledge, fixed ability and quick learning. The first two dimensions were related to the beliefs about the nature of knowing and the other ones were concerned about learning beliefs (Schommer-Aikins, 2004 ).

As some factors did not emerge in validation studies, Chan and Elliott ( 2002 , 2004 ) criticized Schumer’s research method and rectified the EB model. In this model EB was diagrammed in connection with teachers’ conceptions of teaching and learning (see Fig.  1 ). Four factors were extracted in Chan and Elliott ( 2004 ) study: Authority/Expert Knowledge, Certainty Knowledge, Learning Effort/Process and Innate/ Fixed Ability. According to this categorization, the first dimension shows the tendency to believe the presence of an authority to transfer the knowledge to the learners. The second one deals with the process through which knowledge is acquired: whether knowledge is something to be achieved by hard work and the third dimension deals with the point that whether knowledge is something fixed or changeable. Finally, the fourth category questions whether people perceive knowledge as an innate capacity or something to be constructed. In other words, from the perspective of innate/fixed ability believers’ knowledge is something that could not be changed because the ability to gain and reshape knowledge is very limited.

Path model highlighting the possible relation between teachers' EB and conception of teaching and learning (adapted from Chan & Elliott, 2004 )

• Teaching style

Teachers’ persistent patterns of behavior and actions representing minute variations are called teaching styles (Grasha, 2002 ). That said, teaching styles are personal predispositions and tendencies toward learning and pedagogy, which might be manifested differently in diverse contexts. To better conceptualize teaching style, Jarvis ( 2004 ) proposed the following definition:

A teacher's style is the totality of one’s philosophy, beliefs, values, and behaviors, and it incorporates the full implementation of this philosophy; it consists of substantiation and support of beliefs about values and attitudes toward elements of the student learning and teacher learning exchange (p. 40).

As teaching styles are amalgamations of teachers’ theoretical background and real pedagogical practices, multiple factors such as teachers’ personality, cultural and social contexts, philosophy and theoretical background of teaching, subject matter, etc. might change teaching styles (Korthagen, 2004 ). As such, there is no good or bad style of teaching and a teacher might practice varying degrees of different styles of teaching, yet “the order of each style in the cluster reflects the perceived importance of that style in the blend” (Grasha, 2002 , p. 153).

Depending on the mixture of these factors, teaching styles might be a reflection of teacher-centeredness or learner-centeredness. A teacher-centered style of teaching highlights teachers’ control over classroom and assumes a passive role for the learners and teachers following teacher-centered styles act like a sage on the stage (Jarvis, 2004 ). Whereas, learner-centered style of teaching focuses on active role of learners in the classroom and acknowledge significant contribution of learners to pedagogy and teaching career (Grasha, 2002 ; Jarvis, 2004 ; Springer, Stanne, & Donovan, 1999 ).

Language education studies have recommended diverse definitions and classifications for teaching styles. For instance, visual, auditory and kinesthetic teaching styles (Stensurd & Stensurd, 1983 ), and a six-point model to teaching style by Henson and Borthwick ( 1984 ) in which task oriented, cooperative planner, child centered, participant-centered learning-centered and emotionally exciting styles are explained. Grasha ( 2002 ) formulated one of the most commonly referred frameworks for teaching styles. This integrated model categorizes teaching styles into five classes: expert, formal authority, personal model, facilitator, and delegator. A formal authority teacher considers himself as a school member who is recognized by his knowledge. He is mainly involved with giving feedbacks to students and establishing rules and expectations. The personal model teacher conceives himself as a model for students and students have to emulate his approaches. The next one is a facilitator teacher who follows teacher-student interaction, tries to guide students by asking questions and making suggestions, and encourages students to make informed decisions. The delegator teacher is distinguished as a resourceful person who is available when needed. This teacher attempts at training autonomous learners. Teachers might perform all styles of teaching to varying degrees, yet one style is more reflected in teaching career. As Grasha ( 2002 ) put it, “think of each one as representing a different color on an artist’s palette. Thus the colors blend together in various ways with some combinations of styles or blends becoming dominant in teaching while others fall into the background” (p.140). In this study, we adopted Grasha's ( 2002 ) teaching style theory to focus on this variable as many studies have confirmed its validity (Dincol et al., 2011 ; LaBillois & Lagacé-Séguin, 2007 ).

Review of literature

Teachers’ belief is generally assumed a universal construct on which classroom effectiveness and practical aspects of teaching hinge upon (Pajares, 1992 ; Richardson, 1996 ). In this sense, EB is considered a context free notion to be examined in different disciplines and from multiple perspectives of education (Sosu & Gray, 2012 ). Although epistemology has been touched upon from varying perspectives, researchers commonly focus on EB, “including beliefs about the definition of knowledge, how knowledge is constructed, how knowledge is evaluated, where knowledge resides, and how knowing occurs” (Hofer, 2001 ).

Owing to the solid theoretical frameworks proposed by Perry ( 1981 ), Schommer ( 1990 ) revised ideas and principles of EB (Chan & Elliott, 2004 ; Schommer-Aikins, 2004 ), substantial studies have focused on the role of EB in teaching and learning process. Teachers’ beliefs about knowledge acquisition affect teaching and teachers’ behaviors in diverse contexts (Haberman, 1995 , 1996 ). In a line of inquiry, Ravindran, Greene, and DeBacker ( 2005 ), utilized Schommer's ( 1990 ) model and examined 100 preservice teachers’ EB. They found that teachers’ different personal EB led to different teaching strategies. As an example, teachers who believed in authority as a source of knowledge followed a shallow processing strategy of teaching but those who thought that knowledge is acquired through reasoning adopted a deep processing strategy to teach.

In a number of studies conducted in Australia, (Brownlee et al. ( 2001 ) and Brownlee ( 2003 ) studied graduate teacher education students’ EB and found that they believed in multidimensional belief system as put forward by Schommer ( 1990 ). Those who hold that truth is constructed through ample of evidence also considered a significant role for an expert to facilitate knowledge acquisition.

Children’s learning, the process of choosing stories and teaching approaches are also filtered through teachers’ EB (Olafson & Schraw, 2006 ). Teachers holding sophisticated EB inclined to affirm learner-centered approaches to teaching and helped learners construct their knowledge. On the contrary, teachers who believed in naive belief system tended to practice transmission and teacher-centered styles of teaching (Sinatra & Kardash, 2004 ; Yadav & Koehler, 2007 ). Teachers holding sophisticated beliefs also have been found to use integrated strategies of teaching which create chances for group discussion and involve learners in problem solving tasks (Hashweh, 1996 ).

Pre-service teachers’ conceptions about teaching and learning could also be under the influence of EB. Earlier studies classified teachers into two categories: those who believe in traditional teaching and learning and constructivist learning and teaching (Brooks & Brooks, 1999 ; Cheng et al., 2009 ; Eren, 2010 ). Teaching didactically, traditional teachers presume that they are the source of information to be passed down to the learners. Chan ( 2003 ) asserted that pre-service teachers who believed in constructivist conceptions tend to consider knowledge as a tentative phenomenon and those with traditional conception think that knowledge is certain and fixed. Chan and Elliott ( 2004 ) in a complementary study found that pre-service teachers innate/fixed beliefs, authority/expert and certainty knowledge beliefs were positively connected to traditional conceptions and practices of teaching, yet learning effort beliefs were positively related to constructivist teaching practices. In congruence with findings of this study, Cheng et al. ( 2009 ) found that student teachers who believed in sophisticated EB performed learner-centered and constructivist teaching practices and acknowledged the role of active participation and critical thinking in learning. Using statistical analyses, Sosu and Gray ( 2012 ) tracked the epistemic beliefs in teachers’ behaviors and instructional preferences. Their regression analyses showed that teachers’ EB significantly predicted their instructional preferences and the teachers who believed in learning effort as the main channel of knowledge acquisition tended to practice student-centered instructional tasks and adapted teaching to the students’ needs.

In addition to being context free, teachers’ EB has been recognized to be highly domain specific (Buehl & Alexander, 2005 ; Hofer, 2001 ; Muis, Bendixen, & Haerle, 2006 ) and differ across individuals and subject matters (Hofer, 2000 ; Kaartinen-Koutaniemi & Lindblom-Ylänne, 2008 ). In a categorization by Schommer-Aikins, Duell, and Barker ( 2003 ) teachers and researchers of hard academic disciplines (e.g. physics, engineering and chemistry) view knowledge as having fixed patterns to be discovered. However, in soft science (e.g. education, language and literature), a lack of structured paradigm is recognized and knowledge acquisition manners fuel academic and scholarly debates. This argument implies that depending on the subject matter teachers organize instructional activities and respond to pedagogical needs differently (Kuhn & Weinstock, 2002 ).

Similar to teachers of other disciplines, much of EFL teachers’ EB are embodied in their classroom discourse and behaviors. EFL pre-service teachers’ EB determines their cognitive and metacognitive strategies (Cotterall, 1999 ; Horwitz, 1999 ). EFL contexts are distinguished for unique cultural background that might ignite specific EB (Clarebout, Elen, Luyten, & Bamps, 2001 ). Flores ( 2001 ) and Arkoudis ( 2003 ) also argued that language teachers’ EB are formulated in particular socio-economic contexts leading to expectedly different teaching behaviors. In Saudi, Abd Alsmaie and Ammar ( 2011 ) investigated the relationships between EFL pre-service teachers’ EB and their learning strategies, teaching practices and foreign language classroom anxiety. One hundred fourteen pre-service EFL teachers participated in that quantitative study and filled EFL EB questionnaire, EFL learning strategies questionnaire and EFL teaching practices questionnaire. Results of their study showed that Saudi Arabian EFL pre-service teachers showed tendency towards traditional, memorization and rehearsal practices of teaching rather than higher order and elaboration tasks of teaching. In the context of Iran, Ketabi, Zabihi, and Ghadiri ( 2012 ) studied Pre-service English teachers’ epistemological beliefs and their conceptions of teaching. To collect data, they handed EB scale of teaching and learning conception questionnaire to 92 Iranian pre-service EFL teachers. They found that EFL pre-service teachers endorsed innate/fixed ability and therefore traditional conception of teaching. Additionally, significant correlations were observed between teaching conceptions and teachers’ EB.

Janfeshan ( 2017 ) studied Iranian EFL teachers’ beliefs about teaching grammar. In a mixed research, the researcher found that teachers emphasized teaching grammar as an important aspect of communication. Teachers also asserted that examination-oriented culture of education in Iran and the necessity of preparing learners for university-entrance examination force teachers to spend more time on grammar in language teaching.

In a more recent study, Mardali, Siyyari, and Lu ( 2019 ) found that teacher’ beliefs about teaching vocabulary in which EFL teachers believed that their major role is explaining about vocabularies and use direct method of teaching.

In bringing together, the discussion started in review of literature, teachers’ EB are acknowledged as essential elements introducing permanent changes in instructional plans and practices (Chan, 2003 ).

Teachers’ beliefs and its anchors in teacher’s practices have been a hot topic for most of the studies in recent decades (Borg, 2006 ). Most prominent assertions of these studies are manifestation of EB as a system of core and peripheral beliefs, the impact of contextual variable mediating teachers’ EB and the tension that might appear as the result of conflict between beliefs and practices (Borg, 2006 ). Yet, research giving credence to these claims has its own limitations as most of empirical evidence for the nature of beliefs comes from studies about pre-service teachers and graduate teachers teaching hard science and EB in language education, an ill-defined soft domain of knowledge, have been the topic of scant attention. Furthermore, EB has been claimed to be a contextually dependent issue, which restricts the generalizability of earlier studies and brings up the necessity of doing research to enlighten the nature of English language teachers’ EB system. To obviate this necessity, in this study we deal with predominantly espoused types of EB, how system of EB finds its path into the language teachers’ teaching practices and inform dominant teaching styles in private language learning centers. In particular, this research sought to answer the following questions:

What do predominantly EFL teachers believe about the resource of knowledge and knowledge acquisition?

What teaching styles do EFL teachers predominantly follow?

In what sense are EFL teachers’ teaching styles informed by their beliefs about knowledge acquisition resources?

Methodology

Context and participants.

To answer the research questions, both quantitative and qualitative methods of research were used to compensate for the weaknesses of either of methods and gain a more reliable and valid picture of issues being examined (Riazi & Candlin, 2014 ). A mixture of data gathered through questionnaire and semi-structured interview enables the researchers to delve into the participants’ mind patterns, thoughts and insights. As for the quantitative phase of this study, through convenient sampling, two cities (Yasuj and Gachsaran, located in southwest of Iran) were chosen. According to Ary, Jacobs, Sorensen, and Razavieh ( 2010 ), cluster sampling is a type of probability sampling in which units of people who are naturally together and share common features are selected. Relying on a list of the Provincial Office of Education, 70 institutes exist in Yasuj and Gachsaran from which 35 clusters were randomly selected. The selected clusters in this study comprised 200 EFL teachers who filled out the questionnaires. 45% of the participants were female and 55% of them were male. About 65% of the samples held B.A. in teaching English as a foreign language and 35% of the samples obtained M.A. in the same major. All of the participants were originally from Iran and their native language was Farsi. Twenty participants of this sample were selected through systematic random sampling for the follow–up semi-structured interview sessions.

Instruments

The teaching style inventory (TSI) developed by Grasha ( 2002 ) was administered to survey the participants’ teaching styles (see the appendix ). TSI is a forty-item questionnaire based on a five-point Likert scale which is rated from 1 (strongly disagree) to 5 (strongly agree) and categorized teaching styles into five main classes: (a) expert, (b) formal authority, (c) personal model, (d) facilitator, and (e) delegator. Each subscale has eight items addressing teachers’ teaching options and preferences. The overall Cronbach alpha estimated for five subscales of TSI was .75.

Epistemological belief scale (EBS) developed by Chan and Elliott ( 2004 ) was used to collect data about EFL teachers’ epistemological beliefs (see the appendix ). In comparison to the previous scales of EB, this questionnaire is more user-friendly and contains less number of items, yet the scale is comprehensive and valid. EBS includes 30 items based on a five-point Likert scale whereby “1” means “strongly disagree” and “5” means “strongly agree”. This belief scale aimed at measuring innate/fixed ability (13 items), learning effort/process (6 items), authority/expert knowledge (6 items), and certainty knowledge (5 items). Innate/fixed ability assesses teachers’ beliefs about whether people’s capabilities are exposed to change. Learning effort/process deals with teachers’ beliefs about work and hard efforts to learn something. Authority/expert measures teachers’ beliefs in regard with the way that individuals learn something and if knowledge is obtainable from authorities or personal judgment and justifications. Certainty knowledge evaluates teachers’ beliefs concerning the consistency of knowledge. The Cronbach alpha for EBS was .82. The questionnaires were handed to the EFL teachers in person and to ensure their full cooperation, a small gift was offered to each of them.

Capturing belief system is generally difficult as most of teachers are not aware of what they believe (Kagan, 1992 ). For this reason, describing beliefs might be more reliable when quantitative methods are coupled with qualitative research methods whereby richer insights are promised (McCrum, 2013 ). That said, we conducted 20-min semi-structured interviews in Farsi to approach an enhanced understanding of EFL teachers’ EB, and how EB inform teaching styles (the interview questions are included in appendix ).

Data collection and analysis

We informed the participants about the purpose of the study and explained the meaning of EB and teaching style to the participants before distributing the scales. Then semi-structured interviews were conducted. To do so, the researcher asked for participants’ permission to record their voice (see Appendix for the interview questions). Qualitative data collection was pursued until data saturation point that was the 15th interview. Following that, using code and theme analyses (Charmaz, 2006 ), we transcribed the tape-recorded interviews and extracted the main themes and codes. After data collection, we used systematic qualitative analyses approach (Strauss & Corbin, 1998 ) to transcribe, break the codes and extract the main themes and subthemes of the data. To ensure the reliability and validity of the research, we made our determined effort to focus on “indigenous concepts” (Patton, 1990 ) and removed them from data analyses. To this end, we asked two coders to check for redundancy and transparency of the major themes extracted from the interviews and field notes. Statistical Package of Social Science (SPSS) was used to perform quantitative data analyses. Correlational and regression analyses were implemented to check the results of quantitative phase of the study.

Results of quantitative analysis

Descriptive statistics were used to find the prevalent epistemological beliefs that EFL teachers mainly hold and discover EFL teachers’ dominant teaching style. Results of the study showed that EFL teachers’ dominant teaching styles were facilitator style (mean = 35, SD = 4.5), expert style (Mean = 33.27, SD = 3.7), formal authority (mean = 32.24, SD = 3.5), delegator (mean = 31.44, SD = 3.4), and personal model (mean = 29.20, SD = 3.2). Descriptive statistics of EFL teachers’ epistemological beliefs were represented in the following table. As seen in this table, EFL teachers’ predominant epistemological belief is learning process (see the following Tables 1 and 2 ).

To examine the relationships among EFL teachers’ epistemological beliefs and dominant teaching style, Pearson correlational analyses were conducted. Results of these analyses illustrated that there existed significant relationships between these variables (Table 3 ).

According to this table, significant positive relationship at 0.01 existed between EFL teachers’ dominant teaching style (facilitator) and epistemological beliefs (learning process) (r = .685, p  < 0.01) meaning that the more EFL teachers believe that knowledge is acquired through effort, the more facilitating role they play in their teaching styles.

To examine whether EFL teachers’ EB could determine their dominant teaching style, linear regression analysis was carried out. The independent or predictor variable was EFL teachers’ EB and dependent variable was EFL teachers’ dominant teaching style. Enter method was used to do regression analysis. The following table shows the model summary (Table 4 ).

According to this table, R-value is the correlation between entered variables and R square is the degree of variance that independent variable can account for dependent variable. R square in this table shows that 46% of the variance in dependent variable could be accounted for by independent variable (EFL teachers’ epistemological belief). ANOVA table demonstrates the significance of regression model (Table 5 ).

According to ANOVA, F (1,103) equals18.089 and sig ( p value) is .000, p  < 0.05 which shows that the independent variable could significantly explain the amount of variance in the dependent variable. The following table shows multiple regression equations coefficients. Based on Beta value, EFL teachers’ epistemological belief (learning process) contributes to 65% of facilitator style of teaching at the level of 0.05 and it is considered as a significant predictor of variable (p  < 0.05) (Table 6 ).

Results of qualitative analysis

Semi-structured corroborated the quantitative results showing the traces of teachers’ EB in teaching style. Participants of this study were required to elaborate on the ways that their beliefs about sources of knowledge would inform their dominant teaching style. They believed that their material selection, relationship with students, and overall teaching styles are mirrors reflecting EB. Results of semi-structured interviews showed that the ability to learn is not an innate but a changeable asset and most of learners can enhance and improve their capacities to learn a language. The teachers believed that language knowledge is acquired through persistent practice and diligence. This idea is well reflected in the following interview excerpt: “ language learning in Iran is highly dependent on the amount of extracurricular activities and out-of-class efforts a person might put into the task. So, I believe that students have very limited chance of learning the language in the class and if they really want to master the skills, they have to take every learning chance everywhere ”. They asserted that knowledge acquisition resources are not restricted to books and experts of the field. Therefore, they try to use multiple media to teach and help learners build knowledge base. In support of this point, one of the teachers commented, “Language knowledge acquisition is possible through effort, though some learners are genetically endowed with a great talent to learn a language and this, undoubtedly, helps them outperform and get greater achievements. Therefore, when I teach, I focus on my students’ abilities and encourage them to do their best” .

Appreciating the learners’ active participation in constructing the knowledge base, the participants of this study emphasized their role in providing chances for learners to have their shares. One of the teachers explicated, “I think a teacher is supposed to be flexible when he/she teaches. For example, I try to be friendly and have a warm connection with my learners. This way I can see what they need and how they learn and help them with their learning challenges”. In a similar vein, another teacher remarked “the ability to learn needs to be reinforced. I am here to give them food of thought and help my learners in their learning process. In fact, learning process takes time and perseverance and knowledge is not an innate asset” . As for source of knowledge, the teachers believed that multiple sources might come at play when a person acquires knowledge. Moving from the very simple and basic bases of knowledge to sophisticated and more comprehensive body of knowledge is possible when more than one source is exploited. Participants of this study confirmed the role of textbooks as a primary source of knowledge, yet they claimed that building a sound body of knowledge requires using more resources and media:

“TV shows, short stories, games and audio podcasts are just some examples of available sources that can assist learners and help them construct sophisticated knowledge base. It is like learning alphabets, first starting with the books and content of the books and then reading some other magazines and journals. So these secondary sources are very helpful in expanding vocabulary and linguistic information” .

Giving credence to the role of critical thinking and enabling the learners to produce novel structures was another theme extracted from the interviews. Highlighting the status of uncertainty and doubt as the first step of learning, another teacher stated that, “We are here to share some basic structures and vocabulary and help learners create the language knowledge but they must work for their own understanding and learn to justify and argue” . To offer such an experience, teachers of this study asserted that they develop group discussion and peer activities to assist the learners in their language productions.

Discussion and conclusion

This study followed threefold research objectives: EFL teachers’ dominant teaching styles and EB, the connection between these two variables and how EFL teachers’ dominant teaching style might be informed by EB. This study showed that EFL teachers predominantly practice facilitator style of teaching. Both quantitative and qualitative results indicated that EFL teachers inclined to student-centered and constructive practices of teaching.

To interpret the findings of this study it must be noted that the data were collected from private language learning centers in which communications and improving speaking and listening skills are largely emphasized. In such a context, EFL teachers mainly believed learning is malleable and learning effort is the major source of knowledge acquisition. Learners were believed to have shared responsibility in teaching and actively contribute to the pedagogy. In the face of sticking to learning process as the predominant source of knowledge acquisition, ELT teachers seemed to choose facilitator role to direct this process. A Facilitator teacher makes effort to give positive feedbacks and increase motivation of learners (Grasha, 2002 ). This finding reflects the idea of knowledge transformation rather than transmission propagated by post method proponents. Post method era in language education features and celebrates learners’ voice and sensitizing practitioners to learners’ needs and their cultural backgrounds. New streams of teaching practices were much earlier quoted in Kumaravadivelu' ( 1994 ) post method condition emphasizing a reconsideration of traditional conception of teaching. In post method era, students are not considered as mere consumer or passive recipient of the knowledge but they must be actively engaged in the process of learning and knowledge construction. In one of his widely cited articles, Kumaravadivelu ( 1994 ) sets an agenda for English language teachers mapping some macro strategies whereby maximizing learning opportunities, promoting learner autonomy, contextualizing linguistic input and facilitating negotiated interaction were specifically highlighted. Findings of this study suggested that the teachers started reconsideration of teaching concept and made primary steps to move from didactic approaches to teaching to constructivist conception and practicing student-centered teaching styles and used learner-centered tasks such as group discussion, role-play activities and pair talks demanding the learners’ active involvement with learning are manifestation of this set of EB. Results of this study stressed the teachers’ insistence on improving critical thinking abilities and the capability of argumentation, interactive real world communication, and reasoning among learners which resonate teachers’ approach to learner-centered pedagogy. This finding verifies Iranmehr and Davari ( 2017 ) study emphasizing teachers’ attempts in private language learning centers to enhance the learners’ communicative skills. In addition, this piece of finding is in congruence with Moodie ( 2016 ) that indicated the curricular reforms and new orientations of teaching preached in second language education stressing the role of communicative language, establishing a warm teacher-student interaction and EFL teachers’ adaptive pedagogies. On the contrary, Wong and Chai’s ( 2010 ) study showed that teachers embracing traditional EB and sticking to the role of authority knowledge in teaching would view teaching as full transmission of knowledge and carry out teacher-centered styles of teaching.

Unlike this study, Janfeshan ( 2017 ) and Mardali et al. ( 2019 ) found that Iranian EFL teachers stressed the role of teachers in classroom rather than highlighting learners’ voice. Their study indicated consistency between beliefs, practices, and using teacher-centered pedagogy. They argued that exam culture and traditional method of teaching are two main reasons for over-emphasized status of teachers in education in Iran. Differences between findings of this study and earlier ones, might be justified through teachers’ experiences and institutional conventions which might affect teachers’ beliefs and understanding.

Regarding the connection between ELT teachers’ dominant EB and teaching style, correlational and regression analyses indicated that these two attributes were significantly related to each other and EB significantly accounted for the changes in teaching styles. This piece of finding supports Chan and Elliott’s ( 2004 ) study showing positive correlation between teaching concept and practices. They found that teachers holding traditional EB practiced traditional and teacher-centered instruction whereas those adhering to learning process as the foremost source of knowledge acquisition implemented constructivist-teaching practices. This idea was also evident in Cheng et al. ( 2009 ) investigation suggesting that teachers’ sophisticated EB resulted in designing activities that motivate critical thinking and learners’ engagement. Moreover, Sosu and Gray ( 2012 ) examining the connection between teachers’ EB and teaching preferences found that EB significantly predicted teachers’ instructional preferences. They also found that teachers’ instructional practices and EB change from naïve to sophisticated as they face challenges in their profession.

Overall, although most of the studies reviewed earlier focused on pre-service teachers and non-ELT teachers, our findings presented supportive pieces of evidence for the influence of epistemological beliefs on teaching practices and styles. Cultural background, curricular and contextual variable might introduce some changes in teachers’ EB (Windschitl, 2002 ). On the same page, Moodie ( 2016 ) found that that ELT teachers participating in this study have initiated to reform their beliefs with respect to sources of knowledge acquisition and made big leaps to upgrade their teaching career and update their teaching to meet the standards of the latest curricular changes in language education. This might be of much interest when changes in teacher education are tracked and challenges and mismatches between teachers’ beliefs and practices pose obstacles (Lim & Chai, 2008 ).

As an instance of studies into language teacher education, this study might carry some implications. Tracing ELT teachers’ EB and focusing on its connection with different aspects of teaching might justify the reasons behind teachers’ decisions and actions. Doing so might provide insights for teacher educators to reflect upon instructional choices made by teachers (Chan & Elliott, 2004 ; Hofer, 2004 ). Armed with such an understanding, teacher education programs might be able to capitalize the efficient beliefs and practices. Further, as the status of reflectivity is a consistently pronounced element in effective teaching, findings of this study might accentuate the significance of contemplation of what teachers do and how it is connected with higher levels of thinking and belief system. The development, potential strengths and drawbacks of instructional practices, and efficacy of curricular revolutions might be accentuated through studies into teachers’ EB and teaching related factors. As reviewed in the related studies, very few studies have focused on ELT teachers’ EB and its connection with teaching practices. Thus, findings of this study portraying only limited aspect of ELT teachers’ EB and teaching style may contribute to filling the gap in the current literature of ELT teacher education studies. As asserted earlier, drawing a clear-cut border between different types of EB and teaching style might not be an easy task due to the flexibility and variable nature of beliefs and style. However, this study did not deal with this dynamic system and a static picture of the research problems. Future studies may address how teachers’ EB and teaching styles might transform as they grow professionally and gain more experiences and redefine their values. This type of research might be best implemented through longitudinal qualitative research designs and methods of data collection (e.g. self-reflective journals and self-reported practices).

This study provided a static and small scale picture of language teachers in south west of Iran which means the generalizability of findings must be performed cautiously. To present a deeper understanding of the issues, we suggest that future studies use a qualitative instrument (field note, observation or journal writing) to record more detailed information on how ELT teachers teach and gain a moment-by-moment report of teaching practices. While developments of teachers’ EB and teaching styles changes as a result of professional development would yield illuminating insights into teacher education programs, this study did not deal with the question as to how teacher training might shape and change teachers’ EB and teaching styles.

This study dealt with a general overview of EFL teachers’ EB and their teaching style and, due to feasibility issues, failed to approach teachers’ EB about teaching different language skills (e.g. speaking, grammar, vocabulary, etc.). The question as to how ELT teachers’ EB regarding different sub-components of language affect their teaching style could be a worthy line of further research. To do so, items of EB scale should be adapted to pinpoint teachers’ beliefs about sources and quality of gaining grammatical, lexical and vocabulary knowledge to name a few.

Moreover, teachers’ EB, thinking and cognition are shaped within and through the cultural background (Hofer, 2004 ), and new teaching and learning experiences. In this study we only examined ELT teachers’ EB and teaching style regardless of their cultural variables. Therefore, the question as to what cultural background are ELT teachers exposed to and how these cultural features interplay between the beliefs and performance of ELT teachers are still open to research. Finally, as Chai, Teo, and Lee ( 2009 ) posited the role of school in developing the EB system and teaching and learning decisions, our study extends the questions surrounding the role of teacher education in providing a well-defined framework for tracing ELT teachers’ EB development from naive to sophisticated and grasping the factors affecting this path.

Availability of data and materials

Data sharing is not applicable to this study because confidentiality of the data was assured when collecting the data.

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Epistemological Beliefs (Chan & Elliott, 2004 )

There isn’t much you can do to make yourself smarter as your ability is fixed at birth.

Our abilities to learn are fixed at birth.

One’s innate ability limits what one can do.

Some people are born good learners; others are just stuck with limited abilities.

Some children are born incapable of learning well in certain subjects.

The ability to learn is innate/inborn.

Students who begin school with “average” ability remain ‘average’ throughout school.

The really smart students don’t have to work hard to do well in school.

If people can’t understand something right away, they should keep on trying.

Knowing how to learn is more important than the acquired facts.

One learns little if one does not work hard.

Understanding course materials and thinking process are more important than acquiring knowledge/facts.

Everyone needs to learn how to learn.

People will learn better if they focus more on the process of understanding rather than the facts to be acquired

Learning something really well takes a long time or much effort.

How much you get from your learning depends mostly on your effort.

Getting ahead takes a lot of work.

If one tries hard enough, then one will understand the course material.

Wisdom is not knowing the answers, but knowing how to find the answers.

Sometimes I don’t believe the facts in textbooks written by authorities.

Even advice from experts should often be questioned.

I often wonder how much experts really know.

I am very aware that teachers/lecturers know a lot more than I do and so I agree with what they say is important is important rather than rely on my own judgment.

I still believe in what the experts say even though it differs from what I know.

I have no doubts in whatever the experts say.

Scientists will ultimately get to the truth if they keep searching for it.

If scientists try hard enough, they can find the truth to almost anything.

Anyone can figure out difficult concepts if one works hard enough.

I believe there should exist a teaching method applicable to all learning situations.

Scientific knowledge is certain and does not change.

Teaching Style Inventory (TSI)

Respond to each of the items below in terms of how you teach General English. Try to answer as honestly and as objectively as you can. Resist the temptation to respond as you believe you should or ought to think or behave, or in terms of what you believe is the expected or proper thing to do.

Interview questions

What do you think about the sources of knowledge acquisition?

What do you think about your role in building the learners’ knowledge?

What do you think about the learners’ status in your teaching?

To what extent do you take care of your learners’ needs in your teaching career? How?

Do you ever see any connection between your current style of teaching and your beliefs about knowledge acquisition? if yes, how is your teaching style informed by your knowledge acquisition beliefs?

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Soleimani, N. ELT teachers’ epistemological beliefs and dominant teaching style: a mixed method research. Asian. J. Second. Foreign. Lang. Educ. 5 , 12 (2020). https://doi.org/10.1186/s40862-020-00094-y

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Learning Styles: A Review of Theory, Application, and Best Practices

Much pedagogical research has focused on the concept of “learning styles.” Several authors have proposed that the ability to typify student learning styles can augment the educational experience. As such, instructors might tailor their teaching style so that it is more congruent with a given student's or class of students' learning style. Others have argued that a learning/teaching style mismatch encourages and challenges students to expand their academic capabilities. Best practice might involve offering courses that employ a variety of teaching styles. Several scales are available for the standardization of learning styles. These scales employ a variety of learning style descriptors and are sometimes criticized as being measures of personality rather than learning style. Learning styles may become an increasingly relevant pedagogic concept as classes increase in size and diversity. This review will describe various learning style instruments as well as their potential use and limitations. Also discussed is the use of learning style theory in various concentrations including pharmacy.

INTRODUCTION

The diversity of students engaged in higher education continues to expand. Students come to colleges with varied ethnic and cultural backgrounds, from a multitude of training programs and institutions, and with differing learning styles. 1 Coupled with this increase in diversification has been a growth in distance education programs and expansions in the types of instructional media used to deliver information. 2 , 3 These changes and advances in technology have led many educators to reconsider traditional, uniform instruction methods and stress the importance of considering student learning styles in the design and delivery of course content. 4 - 5 Mismatches between an instructor's style of teaching and a student's method of learning have been cited as potential learning obstacles within the classroom and as a reason for using a variety of teaching modalities to deliver instruction. 6 - 8 The concept of using a menu of teaching modalities is based on the premise that at least some content will be presented in a manner suited to every type of learner within a given classroom or course. Some research has focused on profiling learning types so that instructors have a better understanding of the cohort of students they are educating. 7 - 8 This information can be used to guide the selection of instruction modalities employed in the classroom. Limited research has also focused on describing and characterizing composite learning styles and patterns for students in various concentrations of study (eg, medicine, engineering). 5 , 6 , 9 This review will describe the potential utility and limitations in assessing learning styles.

LEARNING STYLES

A benchmark definition of “learning styles” is “characteristic cognitive, effective, and psychosocial behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment. 10 Learning styles are considered by many to be one factor of success in higher education. Confounding research and, in many instances, application of learning style theory has begat the myriad of methods used to categorize learning styles. No single commonly accepted method currently exists, but alternatively several potential scales and classifications are in use. Most of these scales and classifications are more similar than dissimilar and focus on environmental preferences, sensory modalities, personality types, and/or cognitive styles. 11 Lack of a conceptual framework for both learning style theory and measurement is a common and central criticism in this area. In 2004 the United Kingdom Learning and Skills Research Center commissioned a report intended to systematically examine existing learning style models and instruments. In the commission report, Coffield et al identified several inconsistencies in learning style models and instruments and cautioned educators with regards to their use. 12 The authors also outlined a suggested research agenda for this area.

Alternatively, many researchers have argued that knowledge of learning styles can be of use to both educators and students. Faculty members with knowledge of learning styles can tailor pedagogy so that it best coincides with learning styles exhibited by the majority of students. 4 Alternatively, students with knowledge of their own preferences are empowered to use various techniques to enhance learning, which in turn may impact overall educational satisfaction. This ability is particularly critical and useful when an instructor's teaching style does not match a student's learning style. Compounding the issue of learning styles in the classroom has been the movement in many collegiate environments to distance and/or asynchronous education. 2 , 3 This shift in educational modality is inconsistent with the learning models with which most older students and adult learners are accustomed from their primary and high school education. 3 , 13 , 14 Alternatively, environmental influences and more widespread availability of technological advances (eg, personal digital assistants, digital video, the World Wide Web, wireless Internet) may make younger generations of students more comfortable with distance learning. 15 - 17

LEARNING STYLES INSTRUMENTS

As previously stated, several models and measures of learning styles have been described in the literature. Kolb proposed a model involving a 4-stage cyclic structure that begins with a concrete experience, which lends to a reflective observation and subsequently an abstract conceptualization that allows for active experimentation. 18 Kolb's model is associated with the Learning Style Inventory instrument (LSI). The LSI focuses on learner's preferences in terms of concrete versus abstract, and action versus reflection. Learners are subsequently described as divergers, convergers, assimilators, or accommodators.

Honey and Mumford developed an alternative instrument known as the Learning Style Questionnaire (LSQ). 6 Presumably, the LSQ has improved validity and predictive accuracy compared to the LSI. The LSQ describes 4 distinct types of learners: activists (learn primarily by experience), reflectors (learn from reflective observation), theorists (learn from exploring associations and interrelationships), and pragmatics (learn from doing or trying things with practical outcomes). The LSQ has been more widely used and studied in management and business settings and its applicability to academia has been questioned. 6 An alternative to the LSQ, the Canfield Learning Style Inventory (CLSI) describes learning styles along 4 dimensions. 19 These dimensions include conditions for learning, area of interest, mode of learning, and conditions for performance. Analogous to the LSQ, applicability of the CLSI to academic settings has been questioned. Additionally, some confusion surrounding scoring and interpretation of certain result values also exists.

Felder and Silverman introduced a learning style assessment instrument that was specifically designed for classroom use and was first applied in the context of engineering education. 20 The instrument consists of 44 short items with a choice between 2 responses to each sentence. Learners are categorized in 4 dichotomous areas: preference in terms of type and mode of information perception (sensory or intuitive; visual or verbal), approaches to organizing and processing information (active or reflective), and the rate at which students progress towards understanding (sequential or global). The instrument associated with the model is known as the Index of Learning Survey (ILS). 21 The ILS is based on a 44-item questionnaire and outputs a preference profile for a student or an entire class. The preference profile is based on the 4 previously defined learning dimensions. The ILS has several advantages over other instruments including conciseness and ease of administration (in both a written and computerized format). 20 , 21 No published data exist with regards to the use of the ILS in populations of pharmacy students or pharmacists. Cook described a study designed to examine the reliability of the ILS for determining learning styles among a population of internal medicine residents. 20 The researchers administered the ILS twice and the Learning Style Type Indicator (LSTI) once to 138 residents (86 men, 52 women). The LSTI has been previously compared to the ILS by several investigators. 8 , 19 Cook found that the Cronbach's alpha scores for the ILS and LSTI ranged from 0.19 to 0.69. They preliminarily concluded that the ILS scores were reliable and valid among this cohort of residents, particularly within the active-reflective and sensing-intuitive domains. In a separate study, Cook et al attempted to evaluate convergence and discrimination among the ILS, LSI, and another computer-based instrument known as the Cognitive Styles Analysis (CSA). 11 The cohort studied consisted of family medicine and internal medicine residents as well as first- and third-year medical students. Eighty-nine participants completed all 3 instruments, and responses were analyzed using calculated Pearson's r and Cronbach's α. The authors found that the ILS active-reflective and sensing-intuitive scores as well as the LSI active-reflective scores were valid in determining learning styles. However, the ILS sequential-global domain failed to correlate well with other instruments and may be flawed, at least in this given population. The authors advised the use of caution when interpreting scores without a strong knowledge of construct definitions and empirical evidence.

Several other instruments designed to measure personality indexes or psychological types may overlap and describe learning styles in nonspecific fashions. One example of such an indicator is the Myers-Briggs Index. 6 While some relation between personality indexes and learning styles may exist, the use of instruments intended to describe personality to characterize learning style has been criticized by several authors. Therefore, the use of these markers to measure learning styles is not recommended. 6 The concept of emotional intelligence is another popular way to characterize intellect and learning capacity but similarly should not be misconstrued as an effective means of describing learning styles. 23

Several authors have proposed correlations between culture and learning styles. 6 , 24 This is predicated on the concept that culture influences environmental perceptions which, in turn, to some degree determine the way in which information is processed and organized. The storage, processing, and assimilation methods for information contribute to how new knowledge is learned. Culture also plays a role in conditioning and reinforcing learning styles and partially explains why teaching methods used in certain parts of the world may be ineffective or less effective when blindly transplanted to another locale. 6 , 24 Teachers should be aware of this phenomenon and the influence it has on the variety of learning styles that are present in classrooms. This is especially true in classrooms that have a large contingency of international students. Such classrooms are becoming increasingly common as more and more schools expand their internationalization efforts. 25

The technological age may also be influencing the learning styles of younger students and emerging generations of learners. The Millennial Generation has been described as more technologically advanced than their Generation X counterparts, with higher expectations for the use of computer-aided media in the classroom. 15 , 16 , 26 Younger students are accustomed to enhanced visual images associated with various computer- and television-based games and game systems. 16 , 26 Additionally, video technology is increasingly becoming “transportable” in the way of mobile computing, MP3 devices, personal digital video players, and other technologies. 26 All of these advances have made visual images more pervasive and common within industrialized nations.

APPLYING LEARNING STYLES TO THE CLASSROOM

As class sizes increase, so do the types and numbers of student learning styles. Also, as previously mentioned, internationalization and changes in the media culture may affect the spectrum of classroom learning styles as well. 24 , 25 Given the variability in learning styles that may exist in a classroom, some authors suggested that students should adapt their learning styles to coincide with a given instruction style. 6 , 27 This allows instructors to dictate the methods used to instruct in the classroom. This approach also allows instructors to “teach from their strengths,” with little consideration to other external factors such as learning style of students. While convenient, this unilateral approach has been criticized for placing all of the responsibility for aligning teaching and learning on the student. When the majority of information is presented in formats that are misaligned with learning styles, students may spend more time manipulating material than they do in comprehending and applying the information. Additionally, a unilaterally designed classroom may reinforce a “do nothing” approach among faculty members. 6 Alternatively, a teaching style-learning style mismatch might challenge students to adjust, grow intellectually, and learn in more integrated ways. However, it may be difficult to predict which students have the baseline capacity to adjust, particularly when significant gaps in knowledge of a given subject already exist or when the learner is a novice to the topic being instructed. 6 , 27 This might be especially challenging within professional curricula where course load expectations are significant.

Best practice most likely involves a teaching paradigm which addresses and accommodates multiple dimensions of learning styles that build self-efficacy. 27 Instructing in a way that encompasses multiple learning styles gives the teacher an opportunity to reach a greater extent of a given class, while also challenging students to expand their range of learning styles and aptitudes at a slower pace. This may avoid lost learning opportunities and circumvent unnecessary frustration from both the teacher and student. For many instructors, multi-style teaching is their inherent approach to learning, while other instructors more commonly employ unilateral styles. Learning might be better facilitated if instructors were cognizant of both their teaching styles and the learning styles of their students. An understanding and appreciation of a given individual's teaching style requires self-reflection and introspection and should be a component of a well-maintained teaching portfolio. Major changes or modifications to teaching styles might not be necessary in order to effectively create a classroom atmosphere that addresses multiple learning styles or targets individual ones. However, faculty members should be cautious to not over ambitiously, arbitrarily, or frivolously design courses and activities with an array of teaching modalities that are not carefully connected, orchestrated, and delivered.

Novice learners will likely be more successful when classrooms, either by design or by chance, are tailored to their learning style. However, the ultimate goal is to instill within students the skills to recognize and react to various styles so that learning is maximized no matter what the environment. 28 This is an essential skill for an independent learner and for students in any career path.

Particular consideration of learning styles might be given to asynchronous courses and other courses where a significant portion of time is spent online. 29 As technology advances and classroom sizes in many institutions become increasingly large, asynchronous instruction is becoming more pervasive. In many instances, students who have grown accustomed to technological advances may prefer asynchronous courses. Online platforms may inherently affect learning on a single dimension (visual or auditory). Most researchers who have compared the learning styles of students enrolled in online versus traditional courses have found no correlations between the learning styles and learning outcomes of cohorts enrolled in either course type. Johnson et al compared learning style profiles to student satisfaction with either online or face-to-face study groups. 30 Forty-eight college students participated in the analysis. Learning styles were measured using the ILS. Students were surveyed with regard to their satisfaction with various study group formats. These results were then correlated to actual performance on course examinations. Active and visual learners demonstrated a significant preference for face-to-face study groups. Alternatively, students who were reflective learners demonstrated a preference for online groups. Likely due to the small sample size, none of these differences achieved statistical significance. The authors suggest that these results are evidence for courses employing hybrid teaching styles that reach as many different students as possible. Cook et al studied 121 internal medicine residents and also found no association (p > 0.05) between ILS-measured learning styles and preferences for learning formats (eg, Web-based versus paper-based learning modules). 31 Scores on assessment questions related to learning modules administered to the residents were also not statistically correlated with learning styles.

Cook et al examined the effectiveness of adapting Web-based learning modules to a given learner's style. 32 The investigators created 2 versions of a Web-based instructional module on complementary and alternative medications. One version of the modules directed the learner to “active” questions that provided learners immediate and comprehensive feedback, while the other version involved “reflective” questions that directed learners back to the case content for answers. Eighty-nine residents were randomly matched or mismatched based on their active-reflective learning styles (as determined by ILS) to either the “active” or “reflective” test version. Posttest scores for either question type among mismatched subjects did not differ significantly ( p = 0.97), suggesting no interaction between learning styles and question types. The authors concluded from this small study that learning styles had no influence on learning outcomes. The study was limited in its lack of assessment of baseline knowledge, motivation, or other characteristics. Also, the difficulty of the assessment may not have been sufficient enough to distinguish a difference and/or “mismatched” learners may have automatically adapted to the information they received regardless of type.

STUDIES OF PHARMACY STUDENTS

There are no published studies that have systematically examined the learning styles of pharmacy students. Pungente et al collected some learning styles data as part of a study designed to evaluate how first-year pharmacy students' learning styles influenced preferences toward different activities associated with problem-based learning (PBL). 33 One hundred sixteen first-year students completed Kolb's LSI. Learning styles were then matched to responses from a survey designed to assess student preferences towards various aspects of PBL. The majority of students were classified by the LSI as being accommodators (36.2%), with a fairly even distribution of styles among remaining students (19.8% assimilators, 22.4% convergers, 21.6% divergers). There was a proportional distribution of learning styles among a convenience sample of pharmacy students. Divergers were the least satisfied with the PBL method of instruction, while convergers demonstrated the strongest preference for this method of learning. The investigators proposed that the next step might be to correlate learning styles and PBL preferences with actual academic success.

Limited research correlating learning styles to learning outcomes has hampered the application of learning style theory to actual classroom settings. Complicating research is the plethora of different learning style measurement instruments available. Despite these obstacles, efforts to better define and utilize learning style theory is an area of growing research. A better knowledge and understanding of learning styles may become increasingly critical as classroom sizes increase and as technological advances continue to mold the types of students entering higher education. While research in this area continues to grow, faculty members should make concentrated efforts to teach in a multi-style fashion that both reaches the greatest extent of students in a given class and challenges all students to grow as learners.

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Open Access

Peer-reviewed

Research Article

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Linguistics, School of International Studies, Zhejiang University, Hangzhou City, Zhejiang Province, China, Center for College Foreign Language Teaching, Zhejiang University, Hangzhou City, Zhejiang Province, China, Institute of Asian Civilizations, Zhejiang University, Hangzhou City, Zhejiang Province, China

Roles Formal analysis, Project administration, Writing – review & editing

Affiliation Department of Linguistics, School of International Studies, Zhejiang University, Hangzhou City, Zhejiang Province, China

Roles Formal analysis, Writing – original draft

Roles Writing – review & editing

• Jie Hu, 
• Yi Peng, 
• Xueliang Chen, 

• Published: May 20, 2021
• https://doi.org/10.1371/journal.pone.0251545
• Peer Review
• Reader Comments

Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students’ discipline-specific learning styles in a college blended learning setting.

Citation: Hu J, Peng Y, Chen X, Yu H (2021) Differentiating the learning styles of college students in different disciplines in a college English blended learning setting. PLoS ONE 16(5): e0251545. https://doi.org/10.1371/journal.pone.0251545

Editor: Haoran Xie, Lingnan University, HONG KONG

Received: May 15, 2020; Accepted: April 29, 2021; Published: May 20, 2021

Copyright: © 2021 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

Funding: This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 [grant number 20NDJC01Z] with the recipient Jie Hu, Second Batch of 2019 Industry-University Collaborative Education Project of Chinese Ministry of Education [grant number 201902016038] with the recipient Jie Hu, SUPERB College English Action Plan with the recipient Jie Hu, and the Fundamental Research Funds for the Central Universities of Zhejiang University with the recipient Jie Hu.

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

Introduction

Research background.

Learning style, as an integral and vital part of a student’s learning process, has been constantly discussed in the field of education and pedagogy. Originally developed from the field of psychology, psychological classification, and cognitive research several decades ago [ 1 ], the term “learning style” is generally defined as the learner’s innate and individualized preference for ways of participation in learning practice [ 2 ]. Theoretically, learning style provides a window into students’ learning processes [ 3 , 4 ], predicts students’ learning outcomes [ 5 , 6 ], and plays a critical role in designing individualized instruction [ 7 ]. Knowing a student’s learning style and personalizing instruction to students’ learning style could enhance their satisfaction [ 8 ], improve their academic performance [ 9 ], and even reduce the time necessary to learn [ 10 ].

Researchers in recent years have explored students’ learning styles from various perspectives [ 11 – 13 ]. However, knowledge of the learning styles of students from different disciplines in blended learning environments is limited. In an effort to address this gap, this study aims to achieve two major objectives. First, it investigates how disciplinary background impacts students’ learning styles in a blended learning environment based on data collected in a compulsory college English course. Students across 46 disciplines were enrolled in this course, providing numerous disciplinary factor resources for investigating learning styles. Second, it introduces a novel machine learning method named the SVM to the field of education to identify an optimal set of factors that can simultaneously differentiate students of different academic disciplines. Based on data for students from 46 disciplines, this research delves into the effects of a massive quantity of variables related to students’ learning styles with the help of a powerful machine learning algorithm. Considering the convergence of a wide range of academic disciplines and the detection of latent interactions between a large number of variables, this study aims to provide a clear picture of the relationship between disciplinary factors and students’ learning styles in a blended learning setting.

Literature review

Theories of learning styles..

Learning style is broadly defined as the inherent preferences of individuals as to how they engage in the learning process [ 2 ], and the “cognitive, affective and physiological traits” of students have received special attention [ 14 ]. To date, there has been a proliferation of learning style definitions proposed to explain people’s learning preferences, each focusing on different aspects. Efforts to dissect learning style have been contested, with some highlighting the dynamic process of the learner’s interaction with the learning environment [ 14 ] and others underlining the individualized ways of information processing [ 15 ]. One vivid explication involved the metaphor of an onion, pointing out the multilayer nature of learning styles. It was proposed that the outermost layer of the learning style could change in accordance with the external environment, while the inner layer is relatively stable [ 16 , 17 ]. In addition, a strong concern in this field during the last three decades has led to a proliferation of models that are germane to learning styles, including the Kolb model [ 18 ], the Myers-Briggs Type Indicator model [ 19 ] and the Felder-Silverman learning style model (FSLSM) [ 20 ]. These learning style models have provided useful analytical lenses for analyzing students’ learning styles. The Kolb model focuses on learners’ thinking processes and identifies four types of learning, namely, diverging, assimilating, converging, and accommodating [ 18 ]. The Myers-Briggs Type Indicator model classifies learners into extraversion and introversion types, with the former preferring to learn from interpersonal communication and the latter inclining to benefit from personal experience [ 19 ]. As the most popular available model, the FSLSM identifies eight categories of learners according to the four dimensions of perception, input, processing and understanding [ 20 ]. In contrast to other learning style models that divided students into only a few groups, the FSLSM describes students’ learning styles in a more detailed manner. The four paired dimensions delicately distinguish students’ engagement in the learning process, providing a solid basis for a steady and reliable learning style analysis [ 21 ]. In addition, it has been argued that the FSLSM is the most appropriate model for a technology-enhanced learning environment because it involves important theories of cognitive learning behaviors [ 22 , 23 ]. Therefore, a large number of scholars have based their investigations of students’ learning styles in the e-learning/computer-aided learning environment on FSLSM [ 24 – 28 ].

Learning styles and FSLSM.

Different students receive, process, and respond to information with different learning styles. A theoretical model of learning style can be used to categorize people according to their idiosyncratic learning styles. In this study, the FSLSM was adopted as a theoretical framework to address the collective impacts of differences in students’ learning styles across different disciplines (see Fig 1 ).

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This model specifies the four dimensions of the construct of learning style: visual/verbal, sensing/intuitive, active/reflective, and sequential/global. These four dimensions correspond to four psychological processes: input, perception, processing, and understanding.

https://doi.org/10.1371/journal.pone.0251545.g001

The FSLSM includes learning styles scattered among four dimensions.

Visual learners process information best when it is presented as graphs, pictures, etc., while verbal learners prefer spoken cues and remember best what they hear. Sensory learners like working with facts, data, and experimentation, while intuitive learners prefer abstract principles and theories. Active learners like to try things and learn through experimentation, while reflective learners prefer to think things through before taking action. Sequential learners absorb knowledge in a linear fashion and make progress step by step, while global learners tend to grasp the big picture before filling in all the details.

Learning styles and academic disciplines.

Learning styles vary depending on a series of factors, including but not limited to age [ 29 ], gender [ 30 ], personality [ 2 , 31 ], learning environment [ 32 ] and learning experience [ 33 ]. In the higher education context, the academic discipline seems to be an important variable that influences students’ distinctive learning styles, which echoes a multitude of investigations [ 29 , 34 – 41 ]. One notable study explored the learning styles of students from 4 clusters of disciplines in an academic English language course and proposed that the academic discipline is a significant predictor of students’ learning styles, with students from the soft-pure, soft-applied, hard-pure and hard-applied disciplines each favoring different learning modes [ 42 ]. In particular, researchers used the Inventory of Learning Styles (ILS) questionnaire and found prominent disparities in learning styles between students from four different disciplinary backgrounds in the special educational field of vocational training [ 43 ]. These studies have found significant differences between the learning styles of students from different academic disciplines, thus supporting the concept that learning style could be domain dependent.

Learning styles in an online/blended learning environment.

Individuals’ learning styles reflect their adaptive orientation to learning and are not fixed personality traits. Consequently, learning styles can vary among diverse contexts, and related research in different contexts is vital to understanding learning styles in greater depth. Web-based technologies eliminate barriers of space and time and have become integrated in individuals’ daily lives and learning habits. Online and blended learning have begun to pervade virtually every aspect of the education landscape [ 40 ], and this warrants close attention. In addition to a series of studies that reflected upon the application of information and communication technology in the learning process [ 44 , 45 ], recent studies have found a mixed picture of whether students in a web-based/blended learning environment have a typical preference for learning.

Online learning makes it possible for students to set their goals and develop an individualized study plan, equipping them with more learning autonomy [ 46 ]. Generally, students with a more independent learning style, greater self-regulating behavior and stronger self-efficacy are found to be more successful in an online environment [ 47 ]. For now, researchers have made substantial contributions to the identification and prediction of learning styles in an online learning environment [ 27 , 48 – 51 ]. For instance, an inspiring study focused on the manifestation of college students’ learning styles in a purely computer-based learning environment to evaluate the different learning styles of web-learners in the online courses, indicating that students’ learning styles were significantly related to online participation [ 49 ]. Students’ learning styles in interactive E-learning have also been meticulously investigated, from which online tutorials have been found to be contributive to students’ academic performance regardless of their learning styles [ 51 ].

As a flexible learning method, blended courses have combined the advantages of both online learning and traditional teaching methods [ 52 ]. Researchers have investigated students’ learning styles within this context and have identified a series of prominent factors, including perceived satisfaction and technology acceptance [ 53 ], the dynamics of the online/face-to-face environment [ 54 ], and curriculum design [ 55 ]. Based on the Visual, Aural, Reading or Write and Kinesthetic model, a comprehensive study scrutinized the learning styles of K12 students in a blended learning environment, elucidating the effect of the relationship between personality, learning style and satisfaction on educational outcomes [ 56 ]. A recent study underscored the negative effects of kinesthetic learning style, whereas the positive effects of visual or auditory learning styles on students’ academic performance, were also marked in the context of blended learning [ 57 ].

Considering that academic disciplines and learning environment are generally regarded as essential predictors of students’ learning styles, some studies have also concentrated on the effects of academic discipline in a blended learning environment. Focusing on college students’ learning styles in a computer-based learning environment, an inspiring study evaluated the different learning styles of web learners, namely, visual, sensing, global and sequential learners, in online courses. According to the analysis, compared with students from other colleges, liberal arts students, are more susceptible to the uneasiness that may result from remote teaching because of their learning styles [ 11 ]. A similar effort was made with the help of the CMS tool usage logs and course evaluations to explore the learning styles of disciplinary quadrants in the online learning environment. The results indicated that there were noticeable differences in tool preferences between students from different domains [ 12 ]. In comparison, within the context of blended learning, a comprehensive study employed chi-square statistics on the basis of the Community of Inquiry (CoI) presences framework, arguing that soft-applied discipline learners in the blended learning environment prefer the kinesthetic learning style, while no correlations between the learning style of soft-pure and hard-pure discipline students and the CoI presences were identified. However, it is noted that students’ blended learning experience depends heavily on academic discipline, especially for students in hard-pure disciplines [ 13 ].

Research gaps and research questions

Overall, the research seems to be gaining traction, and new perspectives are continually introduced. The recent literature on learning styles mostly focuses on the exploration of the disciplinary effects on the variation in learning styles, and some of these studies were conducted within the blended environment. However, most of the studies focused only on several discrete disciplines or included only a small group of student samples [ 34 – 41 ]. Data in these studies were gathered through specialized courses such as academic English language [ 42 ] rather than the compulsory courses available to students from all disciplines. Even though certain investigations indeed boasted a large number of samples [ 49 ], the role of teaching was emphasized rather than students’ learning style. In addition, what is often overlooked is that a large number of variables related to learning styles could distinguish students from different academic disciplines in a blended learning environment, whereas a more comprehensive analysis that takes into consideration the effects of a great quantity of variables related to learning styles has remained absent. Therefore, one goal of the present study is to fill this gap and shed light on this topic.

Another issue addressed in this study is the selection of an optimal measurement that can effectively identify and differentiate individual learning styles [ 58 ]. The effective identification and differentiation of individual learning styles can not only help students develop greater awareness of their learning but also provide teachers with the necessary input to design tailor-made instructions in pedagogical practice. Currently, there are two general approaches to identify learning styles: a literature-based approach and a data-driven approach. The literature-based approach tends to borrow established rules from the existing literature, while the data-driven approach tends to construct statistical models using algorithms from fields such as machine learning, artificial intelligence, and data mining [ 59 ]. Research related to learning styles has been performed using predominantly traditional instruments, such as descriptive statistics, Spearman’s rank correlation, coefficient R [ 39 ], multivariate analysis of variance [ 56 ] and analysis of variance (ANOVA) [ 38 , 43 , 49 , 57 ]. Admittedly, these instruments have been applied and validated in numerous studies, in different disciplines, and across multiple timescales. Nevertheless, some of the studies using these statistical tools did not identify significant results [ 36 , 53 , 54 ] or reached only loose conclusions [ 60 ]; this might be because of the inability of these methods to probe into the synergistic effects of variables. However, the limited functions of comparison, correlation, prediction, etc. are being complemented by a new generation of technological innovations that promise more varied approaches to addressing social and scientific issues. Machine learning is one such approach that has received much attention both in academia and beyond. As a subset of artificial intelligence, machine learning deals with algorithms and statistical models on computer systems, performing tasks based on patterns and inference instead of explicit instruction. As such, it can deal with high volumes of data at the same time, perform tasks automatically and independently, and continuously improve its performance based on past experience [ 54 ]. Similar machine learning approaches have been proposed and tested by different scholars to identify students’ learning styles, with varying results regarding the classification of learning styles. For instance, a study that examined the precision levels of four computational intelligence approaches, i.e., artificial neural network, genetic algorithm, ant colony system and particle swarm optimization, found that the average precision of learning style differentiation ranged between 66% and 77% [ 61 ]. Another study that classified learning styles through SVM reported accuracy levels ranging from 53% to 84% [ 62 ]. A comparison of the prediction performance of SVM and artificial neural networks found that SVM has higher prediction accuracy than the latter [ 63 ]. This was further supported by another study, which yielded a similar result between SVM and the particle swarm optimization algorithm [ 64 ]. Moreover, when complemented by a genetic algorithm [ 65 ] and ant colony system [ 66 ], SVM has also shown improved results. These findings across different fields point to the reliability of SVM as an effective statistical tool for identification and differentiation analysis.

Therefore, a comprehensive investigation across the four general disciplines in Biglan’s taxonomy using a strong machine learning approach is needed. Given the existence of the research gaps discussed above, this exploratory study seeks to address the following questions:

• Can students’ learning styles be applied to differentiate various academic disciplines in the blended learning setting? If so, what are the differentiability levels among different academic disciplines based on students’ learning styles?
• What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?
• What are the collective impacts of optimal feature sets?

Materials and methods

This study adopted a quantitative approach for the analysis. First, a modified and translated version of the original ILS questionnaire was administered to collect scores for students’ learning styles. Then, two alternate data analyses were performed separately. One analysis involved a traditional ANOVA, which tested the main effect of discipline on students’ learning styles in each ILS dimension. The other analysis involved the support vector machine (SVM) technique to test its performance in classifying students’ learning styles in the blended learning course among 46 specializations. Then, SVM-based recursive feature elimination (SVM-RFE) was employed to specify the impact of students’ disciplinary backgrounds on their learning styles in blended learning. By referencing the 44 questions (operationalized as features in this study) in the ILS questionnaire, SVM-RFE could rank these features based on their relative importance in differentiating different disciplines and identify the key features that collectively differentiate the students’ learning style. These steps are intended to not only identify students’ learning style differences but also explain such differences in relation to their academic disciplinary backgrounds.

Participants

The participants included 790 sophomores taking the blended English language course from 46 majors at Z University. Sophomore students were selected for this study for two reasons. First, sophomores are one of the only two groups of students (the other group being college freshmen) who take a compulsory English language course, namely, the College English language course. Second, of these two groups of students, sophomores have received academic discipline-related education, while their freshmen counterparts have not had disciplinary training during the first year of college. In the College English language course, online activities, representing 55% of the whole course, include e-course teaching designed by qualified course teachers or professors, courseware usage for online tutorials, forum discussion and essay writing, and two online quizzes. Offline activities, which represent 45% of the whole course, include role-playing, ice-breaker activities, group presentations, an oral examination, and a final examination. Therefore, the effects of the academic discipline on sophomores’ learning styles might be sufficiently salient to warrant a comparison in a blended learning setting [ 67 ]. Among the participants, 420 were male, and 370 were female. Most participants were aged 18 to 19 years and had taken English language courses for at least 6 years. Based on Biglan’s typology of disciplinary fields, the students’ specializations were classified into the four broad disciplines of hard-applied (HA, 289/37.00%), hard-pure (HP, 150/19.00%), soft-applied (SA, 162/20.00%), and soft-pure (SP, 189/24.00%).

Biglan’s classification scheme of academic disciplines (hard (H) vs. soft (S) disciplines and pure (P) vs. applied (A) disciplines) has been credited as the most cited organizational system of academic disciplines in tertiary education [ 68 – 70 ]. Many studies have also provided evidence supporting the validity of this classification [ 69 ]. Over the years, research has indicated that Biglan’s typology is correlated with differences in many other properties and serves as an appropriate mechanism to organize discipline-specific knowledge or epistemologies [ 38 ] and design and deliver courses for students with different learning style preferences [ 41 ]. Therefore, this classification provides a convenient framework to explore differences across disciplinary boundaries. In general, HA disciplines include engineering, HP disciplines include the so-called natural sciences, SA disciplines include the social sciences, and SP disciplines include the humanities [ 41 , 68 , 71 ].

In learning style research, it is difficult to select an instrument to measure the subjects’ learning styles [ 72 ]. The criteria used for the selection of a learning style instrument in this study include the following: 1) successful use of the instrument in previous studies, 2) demonstrated validity and reliability, 3) a match between the purpose of the instrument and the aim of this study and 4) open access to the questionnaire.

The Felder and Soloman’s ILS questionnaire, which was built based on the FSLSM, was adopted in the present study to investigate students’ learning styles across different disciplines. First, the FSLSM is recognized as the most commonly used model for measuring individual learning styles on a general scale [ 73 ] in higher education [ 74 ] and has remained popular for many years across different disciplines in university settings and beyond. In the age of personalized instruction, this model has breathed new life into areas such as blended learning [ 75 ], online distance learning [ 76 ], courseware design [ 56 ], and intelligent tutoring systems [ 77 , 78 ]. Second, the FSLSM is based on previous learning style models; the FSLSM integrates all their advantages and is, thus, more comprehensive in delineating students’ learning styles [ 79 , 80 ]. Third, the FSLSM has a good predictive ability with independent testing sets (i.e., unknown learning style objects) [ 17 ], which has been repeatedly proven to be a more accurate, reliable, and valid model than most other models for predicting students’ learning performance [ 10 , 80 ]. Fourth, the ILS is a free instrument that can be openly accessed online (URL: https://www.webtools.ncsu.edu/learningstyles/ ) and has been widely used in the research context [ 81 , 82 ].

The modified and translated version of the original ILS questionnaire includes 44 questions in total, and 11 questions correspond to each dimension of the Felder-Silverman model as follows: questions 1–11 correspond to dimension 1 (active vs. reflective), questions 12–22 correspond to dimension 2 (sensing vs. intuitive), questions 23–33 correspond to dimension 3 (visual vs. verbal), and questions correspond 34–44 to dimension 4 (sequential vs. global). Each question is followed by five choices on a five-point Likert scale ranging from “strongly agree with A (1)”, “agree with A (2)”, “neutral (3)”, “agree with B (4)” and “strongly agree with B (5)”. Option A and option B represent the two choices offered in the original ILS questionnaire.

Ethics statements

The free questionnaires were administered in a single session by specialized staff who collaborated on the investigation. The participants completed all questionnaires individually. The study procedures were in accordance with the ethical standards of the Helsinki Declaration and were approved by the Ethics Committee of the School of International Studies, Zhejiang University. All participants signed written informed consent to authorize their participation in this research. After completion of the informed consent form, each participant was provided a gift (a pen) in gratitude for their contribution and participation.

Data collection procedure

Before the questionnaires were distributed, the researchers involved in this study contacted faculty members from various departments and requested their help. After permission was given, the printed questionnaires were administered to students under the supervision of their teachers at the end of their English language course. The students were informed of the purpose and importance of the study and asked to carefully complete the questionnaires. The students were also assured that their personal information would be used for research purposes only. All students provided written informed consent (see S2 File ). After the questionnaires were completed and returned, they were thoroughly examined by the researchers such that problematic questionnaires could be identified and excluded from further analysis. All questionnaires eligible for the data analysis had to meet the following two standards: first, all questions must be answered, and second, the answered questions must reflect a reasonable logic. Regarding the few missing values, the median number of a given individual’s responses on 11 questions per dimension included in the ILS questionnaire was used to fill the void in each case. In statistics, using the median number to impute missing values is common and acceptable because missing values represent only a small minority of the entire dataset and are assumed to not have a large impact on the final results [ 83 , 84 ].

In total, 850 questionnaires were administered to the students, and 823 of these questionnaires were retrieved. Of the retrieved questionnaires, the remaining 790 questionnaires were identified as appropriate for further use. After data screening, these questionnaires were organized, and their respective results were translated into an Excel format.

Data analysis method

During the data analysis, as a library of the SVM, the free package LIBSVM ( https://www.csie.ntu.edu.tw/~cjlin/libsvm/ ) was first applied as an alternative method of data analysis. Then, a traditional ANOVA was performed to examine whether there was a main effect of academic discipline on Chinese students’ learning styles. ANOVA could be performed using SPSS, a strong data analysis software that supports a series of statistical analyses. In regard to the examination of the effect of a single or few independent variables, SPSS ANOVA can produce satisfactory results. However, SVM, a classic data mining algorithm, outperforms ANOVA for dataset in which a large number of variables with multidimensions are intertwined and their combined/collective effects influence the classification results. In this study, the research objective was to efficiently differentiate and detect the key features among the 44 factors. Alone, a single factor or few factors might not be significant enough to discriminate the learning styles among the different disciplines. Selected by the SVM, the effects of multiple features may collectively enhance the classification performance. Therefore, the reason for selecting SVM over ANOVA is that in the latter case, the responses on all questions in a single dimension are summed instead of treated as individual scores; thus, the by-item variation is concealed. In addition, the SVM is especially suitable for statistical analysis with high-dimensional factors (usually > 10; 44-dimensional factors were included in this study) and can detect the effects collectively imposed by a feature set [ 85 ].

Originally proposed in 1992 [ 86 ], the SVM is a supervised learning model related to machine learning algorithms that can be used for classification, data analysis, pattern recognition, and regression analysis. The SVM is an efficient classification model that optimally divides data into two categories and is ranked among the top methods in statistical theory due to its originality and practicality [ 85 ]. Due to its robustness, accurate classification, and prediction performance [ 87 – 89 ], the SVM has high reproducibility [ 90 , 91 ]. Due to the lack of visualization of the computing process of the SVM, the SVM has been described as a “black box” method [ 92 ]; however, future studies in the emerging field of explainable artificial intelligence can help solve this problem and convert this approach to a “glass box” method [ 67 ]. This algorithm has proven to have a solid theoretical foundation and excellent empirical application in the social sciences, including education [ 93 ] and natural language processing [ 94 ]. The mechanism underlying the SVM is also presented in Fig 2 .

Hyperplanes 1 and 2 are two regression lines that divide the data into two groups. Hyperplane 1 is considered the best fitting line because it maximizes the distance between the two groups.

https://doi.org/10.1371/journal.pone.0251545.g002

The SVM contains the following two modules: one module is a general-purpose machine learning method, and the other module is a domain-specific kernel function. The SVM training algorithm is used to build a training model that is then used to predict the category to which a new sample instance belongs [ 95 ]. When a set of training samples is given, each sample is given the label of one of two categories. To evaluate the performance of SVM models, a confusion matrix, which is a table describing the performance of a classifier on a set of test data for which the true values are known, is used (see Table 1 ).

https://doi.org/10.1371/journal.pone.0251545.t001

ACC represents the proportion of true results, including both positive and negative results, in the selected population;

SPE represents the proportion of actual negatives that are correctly identified as such;

SEN represents the proportion of actual positives that are correctly identified as such;

AUC is a ranking-based measure of classification performance that can distinguish a randomly chosen positive example from a randomly chosen negative example; and

F-measure is the harmonic mean of precision (another performance indicator) and recall.

The ACC is a good metric frequently applied to indicate the measurement of classification performance, but the combination of the SPE, SEN, AUC, F-measure and ACC may be a measure of enhanced performance assessment and was frequently applied in current studies [ 96 ]. In particular, the AUC is a good metric frequently applied to validate the measurement of the general performance of models [ 97 ]. The advantage of this measure is that it is invariant to relative class distributions and class-specific error costs [ 98 , 99 ]. Moreover, to some extent, the AUC is statistically consistent and more discriminating than the ACC with balanced and imbalanced real-world data sets [ 100 ], which is especially suitable for unequal samples, such as the HA-HP model in this study. After all data preparations were completed, the data used for the comparisons were extracted separately. First, the processed data of the training set were run by using optimized parameters. Second, the constructed model was used to predict the test set, and the five indicators of the fivefold cross-validation and fivefold average were obtained. Cross-validation is a general validation procedure used to assess how well the results of a statistical analysis generalize to an independent data set, which is used to evaluate the stability of the statistical model. K-fold cross-validation is commonly used to search for the best hyperparameters of SVM to achieve the highest accuracy performance [ 101 ]. In particular, fivefold, tenfold, and leave-one-out cross-validation are typically used versions of k-fold cross-validation [ 102 , 103 ]. Fivefold cross-validation was selected because fivefold validation can generally achieve a good prediction performance [ 103 , 104 ] and has been commonly used as a popular rule of thumb supported by empirical evidence [ 105 ]. In this study, five folds (groups) of subsets were randomly divided from the entire set by the SVM, and four folds (training sample) of these subsets were randomly selected to develop a prediction model, while the remaining one fold (test sample) was used for validation. The above functions were all implemented with Python Programming Language version 3.7.0 (URL: https://www.python.org/ ).

Then, SVM-RFE, which is an embedded feature selection strategy that was first applied to identify differentially expressed genes between patients and healthy individuals [ 106 ], was adopted. SVM-RFE has proven to be more robust to data overfitting than other feature selection techniques and has shown its power in many fields [ 107 ]. This approach works by removing one feature each time with the smallest weight iteratively to a feature rank until a group of highly weighted features were selected. After this feature selection procedure, several SVM models were again constructed based on these selected features. The performance of the new models is compared to that of the original models with all features included. The experimental process is provided in Fig 3 for the ease of reference.

https://doi.org/10.1371/journal.pone.0251545.g003

The classification results produced by SVM and the ranking of the top 20 features produced by SVM-RFE were listed in Table 2 . Twenty variables have been selected in this study for two reasons: a data-based reason and a literature-based reason. First, it is clear that models composed of 20 features generally have a better performance than the original models. The performance of models with more than 20 is negatively influenced. Second, SVM-based studies in the social sciences have identified 20 to 30 features as a good number for an optimal feature set [ 108 ], and 20 features were selected for inclusion in the optimal feature set [ 95 ]. Therefore, in this study, the top 20 features were selected for subsequent analysis, as proposed in previous analyses that yielded accepted measurement rates. These 20 features retained most of the useful information from all 44 factors but with fewer feature numbers, which showed satisfactory representation [ 96 ].

https://doi.org/10.1371/journal.pone.0251545.t002

Results of RQ (1) What are the differentiability levels among different academic disciplines based on students’ learning styles?

To further measure the performance of the differentiability among students’ disciplines, the collected data were examined with the SVM algorithm. As shown in Table 2 , the five performance indicators, namely, the ACC, SPE, SEN, AUC and F-measure, were utilized to measure the SVM models. Regarding the two general performance indicators, i.e., the ACC value and AUC value, the HA-HP, HA-SA, and HA-SP-based models yielded a classification capacity of approximately 70.00%, indicating that the students in these disciplines showed a relatively large difference. In contrast, the models based on the H-S, A-P, HP-SA, HP-SP, and SA-SP disciplines only showed a moderate classification capacity (above 55.00%). This finding suggests that these five SVM models were not as effective as the other three models in differentiating students among these disciplines based on their learning styles. The highest ACC and AUC values were obtained in the model based on the HA-HP disciplines, while the lowest values were obtained in the model based on the HP-SA disciplines. As shown in Table 2 , the AUCs of the different models ranged from 57.76% (HP-SA) to 73.97% (HA-HP).

To compare the results of the SVM model with another statistical analysis, an ANOVA was applied. Prior to the main analysis, the students’ responses in each ILS dimension were summed to obtain a composite score. All assumptions of ANOVA were checked, and no serious violations were observed. Then, an ANOVA was performed with academic discipline as the independent variable and the students’ learning styles as the dependent variable. The results of the ANOVA showed that there was no statistically significant difference in the group means of the students’ learning styles in Dimension 1, F(3, 786) = 2.56, p = .054, Dimension 2, F(3, 786) = 0.422, p = .74, or Dimension 3, F(3, 786) = 0.90, p = .443. However, in Dimension 4, a statistically significant difference was found in the group means of the students’ learning styles, F (3, 786) = 0.90, p = .005. As the samples in the four groups were unbalanced, post hoc comparisons using Scheffé’s method were performed, demonstrating that the means of the students’ learning styles significantly differed only between the HA (M = 31.04, SD = 4.986) and SP (M = 29.55, SD = 5.492) disciplines, 95.00% CI for MD [0.19, 2.78], p = .016, whereas the other disciplinary models showed no significant differences. When compared with the results obtained from the SVM models, the three models (HA-HP, HA-SA, and HA-SP models) presented satisfactory differentiability capability of approximately 70.00% based on the five indicators.

In the case of a significant result, it was difficult to determine which questions were representative of the significant difference. With a nonsignificant result, it was possible that certain questions might be relevant in differentiating the participants. However, this problem was circumvented in the SVM, where each individual question was treated as a variable and a value was assigned to indicate its relative importance in the questionnaire. Using SVM also circumvented the inherent problems with traditional significance testing, especially the reliance on p-values, which might become biased in the case of multiple comparisons [ 109 ].

Results of RQ (2) What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?

To examine whether the model performance improved as a result of this feature selection procedure, the 20 selected features were submitted to another round of SVM analysis. The same five performance indicators were used to measure the model performance (see Table 2 ). By comparing the performance of the SVM model and that of the SVM-RFE model presented in Table 2 , except for the HA-SP model, all other models presented a similar or improved performance after the feature selection process. In particular, the improvement in the HA-HP and HP-SA models was quite remarkable. For instance, in the HA-HP model, the ACC value increased from 69.32% in the SVM model to 82.59% in the SVM-RFE model, and the AUC score substantially increased from 73.97% in the SVM model to 89.13% in the SVM-RFE model. This finding suggests that the feature selection process refined the model’s classification accuracy and that the 20 features selected, out of all 44 factors, carry substantive information that might be informative for exploring disciplinary differences. Although results for the indicators of the 20 selected features were not very high, all five indicators above 65.00% showed that the model was still representative because only 20 of 44 factors could present the classification capability. Considering that there was a significant reduction in the number of questions used for the model construction in SVM-RFE (compared with those used for the SVM model), the newly identified top 20 features by SVM-RFE were effective enough to preserve the differential ability of all 44 questions. Thus, these newly identified top 20 factors could be recognized as key differential features for distinguishing two distinct disciplines.

To identify these top 20 features in eight models (see Table 2 ), SVM-RFE was applied to rank order all 44 features contained in the ILS questionnaire. To facilitate a detailed understanding of what these features represent, the questions related to the top 20 features in the HA-HP model are listed in Table 3 for ease of reference.

https://doi.org/10.1371/journal.pone.0251545.t003

Results of RQ (3) What are the collective impacts of optimal feature sets?

The collective impacts of optimal feature sets could be interpreted from four aspects, namely, the complexities of students’ learning styles, the appropriate choice of SVM, the ranking of SVM-RFE and multiple detailed comparisons between students from different disciplines. First, the FSLSM considers the fact that students’ learning styles are shaped by a series of factors during the growth process, which intertwine and interact with each other. Considering the complex dynamics of the learning style, selecting an approach that could detect the combined effects of a group of variables is needed. Second, recent years have witnessed the emergence of data mining approaches to explore students learning styles [ 28 , 48 – 50 , 110 ]. Specifically, as one of the top machine learning algorithms, the SVM excels in identifying the combined effects of high-order factors [ 87 ]. In this study, the SVM has proven to perform well in classifying students’ learning styles across different disciplines, with every indicator being acceptable. Third, the combination of SVM with RFE could enable the simultaneous discovery of multiple features that collectively determine classification. Notably, although SVM-FRE could rank the importance of the features, they should be regarded as an entire optimal feature set. In other words, the combination of these 20 features, rather than a single factor, could differentiate students’ learning styles across different academic disciplines. Last but not least, the multiple comparisons between different SVM models of discipline provide the most effective learning style factors, giving researchers clues to the nuanced differences between students’ learning styles. It can be seen that students from different academic disciplines understand, see and reflect things from individualized perspectives. The 20 most effective factors for all models scattered within 1 to 44, verifying students’ different learning styles in 4 dimensions. Therefore, the FSLSM provides a useful and effective tool for evaluating students’ learning styles from a rather comprehensive point of view.

The following discussions address the three research questions explored in the current study.

Levels of differentiability among various academic disciplines based on students’ learning styles with SVM

The results suggest that SVM is an effective approach for classification in the blended learning context in which students with diverse disciplinary backgrounds can be distinguished from each other according to their learning styles. All performance indicators presented in Tables 2 and 3 remain above the baseline of 50.00%, suggesting that between each two disciplines, students’ learning style differences can be identified. To some extent, these differences can be identified with a relatively satisfactory classification capability (e.g., 69.32% of the ACC and 73.97% of the AUC in the HA-HP model shown in Table 2 ). Further support for the SVM algorithm is obtained from the SVM-RFE constructed to assess the rank of the factors’ classification capacity, and all values also remained above the baseline value, while some values reached a relatively high classification capability (e.g., 82.59% of the ACC and 89.13% of the AUC in the HA-HP model shown in Table 2 ). While the results obtained mostly show a moderate ACC and AUC, they still provide some validity evidence supporting the role of SVM as an effective binary classifier in the educational context. However, while these differences are noteworthy, the similarities among students in different disciplines also deserve attention. The results reported above indicate that in some disciplines, the classification capacity is not relatively high; this was the case for the model based on the SA-SP disciplines.

Regarding low differentiability, one explanation might be the indistinct classification of some emerging “soft disciplines.” It was noted that psychology, for example, could be identified as “a discipline that can be considered predominantly ‘soft’ and slightly ‘purer’ than ‘applied’ in nature” [ 111 ] (p. 43–53), which could have blurred the line between the SA and SP disciplines. As there is now no impassable gulf separating the SA and SP disciplines, their disciplinary differences may have diminished in the common practice of lecturing in classrooms. Another reason comes from the different cultivation models of “soft disciplines” and “hard disciplines” for sample students. In their high school, sample students are generally divided into liberal art students and science students and are then trained in different environments of knowledge impartation. The two-year unrelenting and intensive training makes it possible for liberal art students to develop a similar thinking and cognitive pattern that is persistent. After the college entrance examination, most liberal art students select SA or SP majors. However, a year or more of study in university does not exert strong effects on their learning styles, which explains why a multitude of researchers have traditionally investigated the SA and SP disciplines together, calling them simply “social science” or “soft disciplines” compared with “natural science” or “hard disciplines”. There have been numerous contributions pointing out similarities in the learning styles of students from “soft disciplines” [ 37 , 112 – 114 ]. However, students majoring in natural science exhibit considerable differences in learning styles, demonstrating that the talent cultivation model of “hard disciplines” in universities is to some extent more influential on students’ learning styles than that of the “soft disciplines”. Further compelling interpretations of this phenomenon await only the development of a sufficient level of accumulated knowledge among scholars in this area.

In general, these results are consistent with those reported in many previous studies based on the Felder-Silverman model. These studies tested the precision of different computational approaches in identifying and differentiating the learning styles of students. For example, by means of a Bayesian network (BN), an investigation obtained an overall precision of 58.00% in the active/reflective dimension, 77.00% in the sensing/intuitive dimension and 63.00% in the sequential/global dimension (the visual/verbal dimension was not considered) [ 81 ]. With the help of the keyword attributes of learning objects selected by students, a precision of 70.00% in the active/reflective dimension, 73.30% in the sensing/intuitive dimension, 73.30% in the sequential/global dimension and 53.30% in the visual/verbal dimension was obtained [ 115 ].

These results add to a growing body of evidence expanding the scope of the application of the SVM algorithm. Currently, the applications of the SVM algorithm still reside largely in engineering or other hard disciplines despite some tentative trials in the humanities and social sciences [ 26 ]. In addition, as cross-disciplines increase in current higher education, it is essential to match the tailored learning styles of students and researchers studying interdisciplinary subjects, such as the HA, HP, SA and SP disciplines. Therefore, the current study is the first to incorporate such a machine learning algorithm into interdisciplinary blended learning and has broader relevance to further learning style-related theoretical or empirical investigations.

Verification of the features included in the optimal feature sets

Features included in the optimal feature sets provided mixed findings compared with previous studies. Some of the 20 identified features are verified and consistent with previous studies. A close examination of the individual questions included in the feature sets can offer some useful insights into the underlying psychological processes. For example, in six of the eight models constructed, Question 1 (“I understand something better after I try it out/think it through”) appears as the feature with the number 1 ranking, highlighting the great importance attached to this question. This question mainly reflects the dichotomy between experimentation and introspection. A possible revelation is that students across disciplines dramatically differ in how they process tasks, with the possible exception of the SA-SP disciplines. This difference has been supported by many previous studies. For example, it was found that technical students tended to be more tactile than those in the social sciences [ 116 ], and engineering students (known as HA in this study) were more inclined toward concrete and pragmatic learning styles [ 117 ]. Similarly, it was explored that engineering students prefer “a logical learning style over visual, verbal, aural, physical or solitary learning styles” [ 37 ] (p. 122), while social sciences (known as SA in this study) students prefer a social learning style to a logical learning style. Although these studies differ in their focus to a certain degree, they provide an approximate idea of the potential differences among students in their relative disciplines. In general, students in the applied disciplines show a tendency to experiment with tasks, while those in the pure disciplines are more inclined towards introspective practices, such as an obsession with theories. For instance, in Biglan’s taxonomy of academic disciplines, students in HP disciplines prefer abstract rules and theories, while students in SA disciplines favor application [ 67 ]. Additionally, Question 10 (“I find it easier to learn facts/to learn concepts”) is similar to Question 1, as both questions indicate a certain level of abstraction or concreteness. The difference between facts and concepts is closely related to the classification difference between declarative knowledge and procedural knowledge in cognitive psychology [ 35 , 38 ]. Declarative knowledge is static and similar to facts, while procedural knowledge is more dynamic and primarily concerned with operational steps. Students’ preferences for facts or concepts closely correspond to this psychological distinction.

In addition, Questions 2, 4, 7, and 9 also occur frequently in the 20 features selected for the different models. Question 2 (“I would rather be considered realistic/innovative”) concerns taking chances. This question reflects a difference in perspective, i.e., whether the focus should be on obtaining pragmatic results or seeking original solutions. This difference cannot be easily connected to the disciplinary factor. Instead, there are numerous factors, e.g., genetic, social and psychological factors, that may play a strong role in defining this trait. The academic discipline only serves to strengthen or diminish this difference. For instance, decades of research in psychology have shown that males are more inclined towards risk taking than females [ 118 – 121 ]. A careful examination of the current academic landscape reveals a gender difference; more females choose soft disciplines than males, and more males choose hard disciplines than females. This situation builds a disciplinary wall classifying students into specific categories, potentially strengthening the disciplinary effect. For example, Question 9 (“In a study group working on difficult material, I am more likely to jump in and contribute ideas/sit back and listen”) emphasizes the distinction between active participation and introspective thinking, reflecting an underlying psychological propensity in blended learning. Within this context, the significance of this question could also be explained by the psychological evaluation of “loss and gain”, as students’ different learning styles are associated with expected reward values and their internal motivational drives, which are determined by their personality traits [ 122 ]. When faced with the risk of “losing face”, whether students will express their ideas in front of a group of people depends largely on their risk and stress management capabilities and the presence of an appropriate motivation system.

The other two questions also convey similar messages regarding personality differences. Question 4 concerns how individuals perceive the world, while Question 7 concerns the preferred modality of information processing. Evidence of disciplinary differences in these respects was also reported [ 35 , 123 – 125 ]. The other questions, such as Questions 21, 27, and 39, show different aspects of potential personality differences and are mostly consistent with the previous discussion. This might also be a vivid reflection of the multi-faceted effects of blended learning, which may differ in their consonance with the features of each discipline. First, teachers from different domains use technology in different ways, and student from different disciplines may view blended learning differently. For instance, the characteristics of soft-applied fields entail specialized customization in blended courses, further broadening the gulf between different subjects [ 126 ]. Second, although blended learning is generally recognized as a stimulus to students’ innovation [ 127 ], some students who are used to an instructivist approach in which the educator acts as a ‘sage on the stage’ will find it difficult to adapt to a social constructivist approach in which the educator serves as a ‘guide on the side’ [ 128 ]. This difficulty might not only negatively affect students’ academic performance but also latently magnify the effects of different academic disciplines.

Interpretation of the collective impact of optimal feature sets

In each SVM model based on a two-discipline model, the 20 key features (collectively known as an optimal feature set) selected exert a concerted effect on students’ learning styles across different disciplines (see Table 2 ). A broad examination of the distribution of collective impact of each feature set with 20 features in the eight discipline models suggests that it is especially imperative considering the emerging cross-disciplines in academia. Current higher education often involves courses with crossed disciplines and students with diverse disciplinary backgrounds. In addition, with the rise of technology-enhanced learning, the design of personalized tutoring systems requires more nuanced information related to student attributes to provide greater adaptability [ 59 ]. By identifying these optimal feature sets, such information becomes accessible. Therefore, understanding such interdisciplinary factors and designing tailor-made instructions are essential for promoting learning success [ 9 ]. For example, in an English language classroom in which the students are a blend of HP and SP disciplines, instructors might consider integrating a guiding framework at the beginning of the course and stepwise guidelines during the process such that the needs of both groups are met. With the knowledge that visual style is dominant across disciplines, instructors might include more graphic presentations (e.g., Question 11) in language classrooms rather than continue to use slides or boards filled with words. Furthermore, to achieve effective communication with students and deliver effective teaching, instructors may target these students’ combined learning styles. While some methods are already practiced in real life, this study acts as a further reminder of the rationale underlying these practices and thus increases the confidence of both learners and teachers regarding these practices. Therefore, the practical implications of this study mainly concern classroom teachers and educational researchers, who may draw some inspiration for interdisciplinary curriculum design and the tailored application of learning styles to the instructional process.

Conclusions

This study investigated learning style differences among students with diverse disciplinary backgrounds in a blended English language course based on the Felder-Silverman model. By introducing a novel machine learning algorithm, namely, SVM, for the data analysis, the following conclusions can be reached. First, the multiple performance indicators used in this study confirm that it is feasible to apply learning styles to differentiate various disciplines in students’ blended learning processes. These disciplinary differences impact how students engage in their blended learning activities and affect students’ ultimate blended learning success. Second, some questions in the ILS questionnaire carry more substantive information about students’ learning styles than other questions, and certain underlying psychological processes can be derived. These psychological processes reflect students’ discipline-specific epistemologies and represent the possible interaction between the disciplinary background and learning style. In addition, the introduction of SVM in this study can provide inspiration for future studies of a similar type along with the theoretical significance of the above findings.

Despite the notable findings of this study, it is subject to some limitations that may be perfected in further research. First, the current analysis examined the learning styles without allowing for the effects of other personal or contextual factors. The educational productivity model proposed by Walberg underlines the significance of the collected influence of contextual factors on individuals’ learning [ 129 ]. For example, teachers from different backgrounds and academic disciplines are inclined to select various teaching methods and to create divergent learning environments [ 130 ], which should also be investigated thoroughly. The next step is therefore to take into account the effects of educational background, experience, personality and learning experience to gain a more comprehensive understanding of students’ learning process in the blended setting.

In conclusion, the findings of this research validate previous findings and offer new perspectives on students’ learning styles in a blended learning environment, which provides future implications for educational researchers, policy makers and educational practitioners (i.e., teachers and students). For educational researchers, this study not only highlights the merits of using machine learning algorithms to explore students’ learning styles but also provides valuable information on the delicate interactions between blended learning, academic disciplines and learning styles. For policy makers, this analysis provides evidence for a more inclusive but personalized educational policy. For instance, in addition to learning styles, the linkage among students’ education in different phases should be considered. For educational practitioners, this study plays a positive role in promoting student-centered and tailor-made teaching. The findings of this study can help learners of different disciplines develop a more profound understanding of their blended learning tendencies and assist teachers in determining how to bring students’ learning styles into full play pedagogically, especially in interdisciplinary courses [ 131 – 134 ].

Supporting information

https://doi.org/10.1371/journal.pone.0251545.s001

S2 File. Informed consent for participants.

https://doi.org/10.1371/journal.pone.0251545.s002

S1 Dataset.

https://doi.org/10.1371/journal.pone.0251545.s003

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments on this paper and Miss Ying Zhou for her suggestions during the revision on this paper.

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The effect of the teacher's teaching style on students' motivation.

SUBMITTED BY:  MARIA THERESA BARBEROS,  ARNOLD GOZALO,  EUBERTA PADAYOGDOG  SUBMITTED TO:  LEE TZONGJIN, Ed.D.  CHAPTER I  THE EFFECT OF TEACHERS' TEACHING STYLE ON STUDENTS' MOTIVATION

Introduction

The teachers, being the focal figure in education, must be competent and knowledgeable in order to impart the knowledge they could give to their students. Good teaching is a very personal manner. Effective teaching is concerned with the student as a person and with his general development. The teacher must recognize individual differences among his/her students and adjust instructions that best suit to the learners. It is always a fact that as educators, we play varied and vital roles in the classroom. Teachers are considered the light in the classroom. We are entrusted with so many responsibilities that range from the very simple to most complex and very challenging jobs. Everyday we encounter them as part of the work or mission that we are in. It is very necessary that we need to understand the need to be motivated in doing our work well, so as to have motivated learners in the classroom. When students are motivated, then learning will easily take place. However, motivating students to learn requires a very challenging role on the part of the teacher. It requires a variety of teaching styles or techniques just to capture students' interests. Above all, the teacher must himself come into possession of adequate knowledge of the objectives and standards of the curriculum, skills in teaching, interests, appreciation and ideals. He needs to exert effort to lead children or students into a life that is large, full, stimulating and satisfying. Some students seem naturally enthusiastic about learning, but many need or expect their instructors or teachers to inspire, challenge or stimulate them. "Effective learning in the classroom depends on the teacher's ability to maintain the interest that brought students to the course in the first place (Erickson, 1978). Not all students are motivated by the same values, needs, desires and wants. Some students are motivated by the approval of others or by overcoming challenges.

Teachers must recognize the diversity and complexity in the classroom, be it the ethnicity, gender, culture, language abilities and interests. Getting students to work and learn in class is largely influenced in all these areas. Classroom diversity exists not only among students and their peers but may be also exacerbated by language and cultural differences between teachers and students.

Since 2003, many foreign professional teachers, particularly from the Philippines, came to New York City to teach with little knowledge of American school settings. Filipino teachers have distinct styles and expressions of teaching. They expect that: education is interactive and spontaneous; teachers and students work together in the teaching-learning process; students learn through participation and interaction; homework is only part of the process; teaching is an active process; students are not passive learners; factual information is readily available; problem solving, creativity and critical thinking are more important; teachers should facilitate and model problem solving; students learn by being actively engaged in the process; and teachers need to be questioned and challenged. However, many Filipino teachers encountered many difficulties in teaching in NYC public schools. Some of these problems may be attributed to: students' behavior such as attention deficiency, hyperactivity disorder, and disrespect among others; and language barriers such as accent and poor understanding of languages other than English (e.g. Spanish).

As has been said, what happens in the classroom depends on the teacher's ability to maintain students' interests. Thus, teachers play a vital role in effecting classroom changes.

As stressed in the Educator's Diary published in 1995, "teaching takes place only when learning does." Considering one's teaching style and how it affects students' motivation greatly concerns the researchers. Although we might think of other factors, however, emphasis has been geared towards the effect of teacher's teaching style and student motivation.

Hypothesis:

If teacher's teaching style would fit in a class and is used consistently, then students are motivated to learn.

Purpose of the Study

The main thrust of the study was to find out the effect of the teacher's teaching style on students' motivation.

Action Research Questions

This paper attempted to answer specific questions such as: 1. What is the effect of teacher's teaching style using English As A Second Language Strategies on student's motivation? 2. How does teacher's teaching style affect students' motivation? 3. What could be some categories that make one's teaching style effective in motivating students?

Research Design/Methods of Collecting Data

The descriptive-survey method was used in this study, and descriptive means that surveys are made in order to discover some aspects of teacher's teaching style and the word survey denotes an investigation of a field to ascertain the typical condition is obtaining. The researchers used questionnaires, observations, interviews, students' class work and other student outputs for this study. The questionnaires were administered before and after ESL strategies were applied. Observation refers to what he/she sees taking place in the classroom based on student's daily participation. Student interviews were done informally before, during, and after classes. Several categories affecting motivation were being presented in the questionnaire.

Research Environment and Respondents

The research was conducted at IS 164 and IS 143 where three teachers conducting this research were the subjects and the students of these teachers selected randomly specifically in the eighth and sixth grade. The student respondents were the researchers' own students, where 6 to 7 students from each teacher were selected. Twenty students were used as samples.

To measure students' motivation, researchers used questionnaires which covered important categories, namely: attitudes, student's participation, homework, and grades. Open-ended questions were also given for students' opinion, ideas and feelings towards the teacher and the subject. The teacher's teaching style covers the various scaffolding strategies. The data that were collected from this research helped the teachers to evaluate their strengths and weaknesses so as to improve instruction. The results of this study could benefit both teachers and students.

Research Procedure

Data gathering.

The researchers personally distributed the questionnaires. Each item in each category ranges from a scale of 5-1 where 5 rated as Strongly Agree while 1 as Strongly Disagree. The questionnaires were collected and data obtained were tabulated in tables and interpreted using the simple percentage. While the open ended questions, answers that were given by the students with the most frequency were noted.

Review of Related Literature

Helping students understand better in the classroom is one of the primary concerns of every teacher. Teachers need to motivate students how to learn. According to Phil Schlecty (1994), students who understand the lesson tend to be more engaged and show different characteristics such as they are attracted to do work, persist in the work despite challenges and obstacles, and take visible delight in accomplishing their work. In developing students' understanding to learn important concepts, teacher may use a variety of teaching strategies that would work best for her/his students. According to Raymond Wlodkowski and Margery Ginsberg (1995), research has shown no teaching strategy that will consistently engage all learners. The key is helping students relate lesson content to their own backgrounds which would include students' prior knowledge in understanding new concepts. Due recognition should be given to the fact that interest, according to Saucier (1989:167) directly or indirectly contributes to all learning. Yet, it appears that many teachers apparently still need to accept this fundamental principle. Teachers should mind the chief component of interest in the classroom. It is a means of forming lasting effort in attaining the skills needed for life. Furthermore teachers need to vary teaching styles and techniques so as not to cause boredom to the students in the classroom. Seeking greater insight into how children learn from the way teachers discuss and handle the lesson in the classroom and teach students the life skills they need, could be one of the greatest achievements in the teaching process.

Furthermore, researchers have begun to identify some aspects of the teaching situation that help enhance students' motivation. Research made by Lucas (1990), Weinert and Kluwe (1987) show that several styles could be employed by the teachers to encourage students to become self motivated independent learners. As identified, teachers must give frequent positive feedback that supports students' beliefs that they can do well; ensure opportunities for students' success by assigning tasks that are either too easy nor too difficult; help students find personal meaning and value in the material; and help students feel that they are valued members of a learning community. According to Brock (1976), Cashin (1979) and Lucas (1990), it is necessary for teachers to work from students' strengths and interests by finding out why students are in your class and what are their expectations. Therefore it is important to take into consideration students' needs and interests so as to focus instruction that is applicable to different groups of students with different levels.

CHAPTER II  PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

This chapter presents and analyzes data that answer the subsidiary problems of the study. Table I showed that out of the 20 student respondents, 50% were males and 50% females. Of the male students respondents, only 2 males belong to the high group while 8 males from the low group. For the females, each of the group had 5 respondents. It also showed that there were 7 respondents from the high group and 13 came from the low group.

Table 1:Respondents by Gender

Table 2 showed that out of the 20 students respondents, 80% of students were of Hispanic origin; 10% of respondents were White (not of Hispanic origin); and 10% were Black (not of Hispanic origin); while 0% were of American Indian, Asian or Pacific Islander ethnicity. The results also showed that among the Hispanic, 40% came from the low and 40% came from the high group. There were only 10% White respondents from both groups. There were 10% respondents who were Black from both groups.

Table 2: Respondents by Ethnicity

Table 3 showed that 15% of the respondents had grades between 96-100 in Science, 0% between 91-95, while 15% scored between 86-90, the same as the range between 81-85. However, on the low group 25% of the respondents had grades between 71-75, 5% each had a range between 66-70 and 61-65; while 15% of the respondents did not have Science last year.

Table 3: Grades in Science

Table 4 revealed that for students' motivation-attitude, more than half of the respondents agreed that they are always excited to attend classes this school year. 75% of the students believed that Science is fun and interesting. Similarly, 80% of the respondents agreed that Science is important for them and 60% said that they love Science.

For student motivation-participation, it showed that more than half of the respondents affirm that they are always prepared in their Science classes. 75% of the students participated in Science activities; 50% did their Science assignments consistently.

For student motivation-homework, it could be noted that 60% of the students completed their homework on time and 50% found homework useful and important. 85% of the students said that they got enough support to do homework at home and 90% said that the teachers checked their homework.

For student motivation-grades, 65% got good grades in Science. 65% of the respondents said that they study their lessons before a test or a quiz. More than half of the respondents disagreed that the terms or words used in the test were difficult to understand. Less than half of the respondents agreed tests measure their understanding of Science concepts and knowledge, while 80% thought that grading is fair. On the other hand, the data under teaching style as noted on table 4 showed that 65% of the students strongly agreed that they have a good relationship with their Science teacher and no one disagreed. 75% noted that their Science teachers used materials that were easy to understand. 60% said that their teachers presented the lessons in many ways. More than half of the students said that they understood the way their Science teachers explained the lesson while 25% were not sure of their answer. 75% said that they got feedback from their Science teacher.

Table 4: Data on the Five Categories

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Learning by teaching 

Students in qatar, used to one-way lecture formats, thrive using a teamwork-centered approach taught by a fulbright scholar. .

Dr. Charity Lovitt, associate teaching professor in the University of Washington Bothell’s School of STEM , loves to travel and does so frequently. “I have been an Alaska Airlines MVP Gold member for three years in a row,” she joked. 

While she is certainly an experienced traveler, last year Lovitt took a trip that was unlike any before. 

“I go on most trips with my husband to visit close friends and family, but in August of 2023 I went to the State of Qatar completely on my own,” she said. “It was a place I had never been to with a language I barely spoke. I also had no experience with their culture and went there to work with colleagues I had never met. To say I was out of my comfort zone would be an understatement.” 

Lovitt went to Qatar as a part of the Fulbright U.S. Scholar program that sends 400 scholars from the U.S. to institutions around the world to teach, conduct research, exchange ideas and contribute to mutual understanding. 

Translating teaching methods  

Lovitt had more than 150 different countries to choose from but decided on Qatar because the UW Bothell campus has such a diverse student population. 

“More than 30% of our international students come from Middle Eastern, North African and Central Asian countries,” she said. “Qatar is a hub for education and innovation in the Middle East, and brings in students from across the region.” 

From her arrival in August to her departure in January, Lovitt taught first-year science courses to college students. “My goal was to determine how to adapt student-centered practices that are widely used in American schools to the Middle Eastern classroom,” she said. “There are key differences in learners’ values, perceptions, communication styles and expected outcomes in each setting. 

Her pedagogy was based in Process Oriented Guided Inquiry Learning, an approach to teaching and learning in which students work in teams to discover key ideas and practice important skills. “POGIL increases student engagement, learning and retention,” she said. 

Students in Qatar, however, are not used to this method of instruction. Instead, they are accustomed to lectures. “I knew I would have to work hard to engage them in active learning,” she said, “and I was certainly up for the challenge.” 

Surprising student insights  

On the first day of class, Lovitt spent time getting to know her students. She set up an online poll to determine what her students wanted to major in and was surprised to see a majority chose engineering. “This was very different than at UW Bothell, where most of my students want to major in Health Studies,” she said. 

She then created another online poll asking students to name one thing they would need to be successful in their future career. 

“Given the emphasis on engineering, I thought that students would state they needed math and science content, which, of course, they do,” she said. “But I was surprised to find that their response was nearly identical to the ones I receive in my U.S. classes. Most of the skills are communication, time management or teamwork. 

This information proved invaluable to shaping the students’ classoom experience. “None of these skills can be gained from traditional lecture or memorizing chemical formulas,” Lovitt said. “However, these skills are ingrained in the POGIL method of teaching and can be taught if the students work in teams during class — providing an opportunity to learn the content while developing these skills.” 

Thriving in teamwork

Rather than lecture on course expectations and highlight the syllabus, Lovitt next asked students to work in teams to do a syllabus scavenger hunt and identify the most important expectations. 

“Each team had four members,” she explained “A manager to keep the team on task, a recorder to record team notes on a sheet of paper, a spokesperson to verbally report team results and a reflector who made sure that all students agreed on an answer before they moved on.” 

Prior to arriving in Qatar, Lovitt had been cautioned by other instructors that the students wouldn’t feel comfortable working in teams and didn’t like asking questions in the class, mostly because they’re nervous about speaking in English. 

“I did not have this issue,” she said. “The students were engaged and enthusiastic. The 75 minutes of class flew by, and we barely had time to finish! I and the students left the class energized and excited for the term.” 

The remainder of the term followed a similar pattern. And day after day, Lovitt saw improvement in her students’ engagement. “They began to thrive in teamwork and found confidence in their schoolwork,” she said. “I had students who were very shy in the beginning become effective and good at advocating for themselves and their learning.” 

Challenging cultural differences  

Lovitt’s time in Qatar was not without challenges. Among the most prominent was the separation of male and female students. 

“This was one of the most distinct differences from teaching in the U.S.,” she said. “The female building is completely enclosed, and there are signs everywhere noting ‘only females, except male faculty.’ When students drive to campus, there are male entrances and female entrances. There are female security guards at every female building entrance to ensure that only females or male faculty enter.” 

She admitted that the strict gender separation was at first difficult to accept. “From the courses I taught at UW Bothell, I knew of numerous examples where women were kept out of scientific conversations because they were not allowed in male-only spaces,” she said. “Given my past experiences as a woman in STEM, combined with my American upbringing that separate is not equal, the idea of separate spaces was difficult to embrace.” 

Difficult, she said, but not impossible — and as time went on, she even began to see some positive aspects. “This is the first time in my career that I have taught only women. Being in STEM, it’s pretty common for women to be a minority in the classroom, but that is changing. In my undergrad, around 30% of the people in the classroom were female and the rest were male,” she said. 

“For the first time in my career, I didn’t have to worry about the male to female ratio in class teams, thus removing one of the barriers to female success in STEM.” 

For me what matters most is that my students experience a sense of belonging in the classroom . . . to me that is more important than just learning the content. Dr. Charity Lovitt, associate teaching professor, School of STEM 

Mutual lessons learned

Lovitt will return to UW Bothell in September 2024 and plans to implement changes to her teaching style based on what she learned abroad — including adding Arabic examples to her work since subjects such as alchemy (chemistry) and algebra originally came from the Middle East. 

She said she is also committed to being more aware of student culture and customs when designing teams. “For me, what matters most is that my students experience a sense of belonging in the classroom . . . to me that is more important than just learning the content.” 

Just as Qatar University made an impact on Lovitt, she made a difference for her students — and even other faculty at Qatar University: By the end of her program, some professors were interested in the POGIL method and in trying cooperative approaches to their teaching

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Favorite Class or Professor 

There are too many courses and faculty to list! Professor Jocelyne Cesari challenged my writing and analytical abilities; she pressed me to think deeply about Islam, politics, violence, and peacebuilding. Professors Jacqueline and Homi Bhabha at the Kennedy School and FAS, respectively, gave me the theories and practical knowledge to pursue my own research on gender and migration post-graduation. Professor Muhammad Habib took my broken and shy Arabic and turned me into someone who can confidently hold a conversation with a native speaker.  

Finally, to Diane Moore, Atalia Omer, Hilary Rantisi, and Salma Waheedi – taking Narratives of Displacement and Belonging in Palestine/Israel was a blessing. I have never felt so heard and held by a teaching team. Thank you. 

Message of Thanks  

My mother – If I have done any good in these two years, if I have known any measure of success, it is due to her constant prayers and deep love. 

My husband – I could not have done half of what I did if I did not have the love and support he provided at home.  

My siblings and friends for the ways in which they have pushed me, carried me, and brought joy to my life. The HDS DIB Office – Melissa, Steph, and Matt – for witnessing me in all my chaos and supporting me through it all. The HDS RPL Office, and especially Diane Moore, Hussein Rashid, Hilary Rantisi, Susie Hayward, Judy Beals, and Atalia Omer, for possessing the kind of intellectual and personal bravery this university so needs. Ann Braude, my advisor, who approved my course plans no matter how insane they started looking. Katie, who has held and sustained us with much more than just food (although the food was always appreciated). The HDS Muslim community for all its diversity and poetry and dance and pride in our beautiful tradition. 

All Harvard students in solidarity with Palestine – what an honor it has been to know you. 

What I Hope to Be Remembered By 

Oh my gosh, I’ll be happy if I am remembered at all! I feel like I spent most of my time in class or studying. If anything, I hope I am remembered as someone who was intellectually curious, introspective, and kind. 

Future Plans 

This summer, I’ll be working with Al-Haq through RPL’s Religion, Conflict, and Peace Initiative. Then, in September, I’m headed to Amman, Jordan, to begin a nine-month research fellowship on gender and migration with support from Harvard and the Refugees, Displaced Persons, and Forced Migration Studies Center at Yarmouk University. 

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Nevada Today

Nate hodges receives the 2024 f. donald tibbitts distinguished teacher award, colleagues and students cheer on their professor in a surprise classroom visit.

When President Brian Sandoval walked into Nate Hodges classroom on April 15 to present him with the F. Donald Tibbitts Distinguished Teacher Award, he nearly froze with surprise.

“When the door first opened and I saw President Sandoval standing there, I was like, ‘Oh my god…Did I just get the Tibbitts award?!’ and then all these people began to pour into the classroom: the Provost, then the Dean of Liberal Arts, then the Associate Deans, my Chair, and then the committee,” Hodges recalls.

Hodges, teaching associate professor in the Department of Theatre and Dance, was nominated in September by collogues for his proven dedication to teaching and student success, and his inspirational attitude at the University of Nevada, Reno.

“Professor Hodges is an outstanding teacher of jazz dance who brings passion, enthusiasm, and professionalism to the classroom,” Ann M Archbold, professor in the Department of Theatre and Dance said. “He is very thoughtful in making the courses progressive in their skill-building which results in notable student achievement and mastery. Colleagues who have undertaken peer review assessments of his teaching commend him on his student engagement in the classroom, the dynamic classroom environment, and his well-developed pedagogical approaches to the subject manner. This is all evidence that he is providing high-quality instruction and is holding his students to rigorous standards. Departmental faculty unanimously acknowledge that ‘Nate is by far the best teacher in the department.’”

At the center of Hodges’ work is the success of his students, as he creates a safe and comfortable environment to explore curiosity and relate classwork to current topics. It is exactly that and his determination to showcase the importance of an education in Liberal Arts that lead him to receive this important award.

“I think there is a very strange narrative being written by the media about how upper education is unnecessary, and that there is no value in the liberal arts, especially dance,” Hodges said. “But, my class teaches students how to be comfortable in their own skin, to understand and be able to articulate their personal boundaries, and to be able to think about their choices in how they present themselves in front of people. These are skills they can use in job interviews, work presentations, and meetings. The liberal arts teach the skills that connect us to humanity and not just provide information.”

In a brief interview, Hodges dove deeper into his teaching style and the meaning of his work. 

What does being named an F. Donald Tibbitts Distinguished Teacher Awardee mean to you?

“For me, this is a huge validation and affirmation for years of hard work. As a teaching professor, I teach quite a bit of classes in addition to all the choreography I do. I don’t do anything half-way because my students count on me to offer challenging yet accessible and rigorous yet fun material. I want my students to be as successful as possible, and that makes pouring quite a bit of time, energy, thought, and literal sweat into everything I do. In an R1, there can be lot of emphasis placed on research and tenure professors, so it feels very rewarding to be recognized for my teaching and what I bring to this university. I think sometimes, teachers can question ourselves ‘am I really having an impact?’ and this is a nice validation that I am.”

Your teaching style is described as energetic, passionate, engaging, and unconventional. Your students often leave you glowing reviews with phrases like “best teacher” and “fantastic” appearing often. How would you describe your teaching style and how has it evolved since you began teaching?

“I think in our current culture, there is a lack of value for the arts, especially dance, even though you are constantly seeing it everywhere. I believe strongly in the value of physical expression, the agency of the moving body, and that dance and movement is the embodiment of our culture. I also understand that not every student is the same. You have introverts and extraverts, students who have never been exposed to dance and those that have grown up with it, students who are visual learners or auditory or kinesthetic; I try to create classes that have multiple ways of communicating and multiple avenues for engaging different people. I also employ pedagogical strategies that use engagement activities that break up lecture, and as much interactive opportunities as possible. Class should not only be informative, but social, experiential, collaborative, and, dare I say it, fun!”

How do you translate what you teach in the classroom into department productions and how do these two worlds overlap – teaching and performance?

“For me, collaboration is hugely important. The best ideas, I think, come out of when different people with different perspectives come together to create art or solve a problem. So, I have no problem having conversations about the choreography with the director in front of the students. This way they can see how two professionals communicate, come to ideas through collaboration, and can see how creative decisions are made. It is also really important to walk the cast through why the choreography is what it is, how it supports the director’s artistic goals and vision, and where within it they can find opportunity to infuse it with their character and how it furthers the storyline. I am very thoughtful about the movement I create, especially when it deals with complicated, controversial, or difficult subject matter. I want the cast to be able to confidently articulate why they are doing the movement they are if approached by an audience member.”

Dance is an especially creative and expressive medium. How do you create a safe and inclusive environment for your students, providing space for them to feel comfortable expressing themselves while still challenging them as dancers?

“College is the perfect place, right out of high school but before the professional world, to not only figure out who you are, but to discover the kind of artist you want to be, what your professional and creative boundaries are, and how far you can safely push yourself. I work to create a culture where it is made explicit that we are here to learn, grow, and push ourselves; that there is something for everyone to learn and expand on. I infuse quite a bit of peer observation and feedback into my classes, so that we normalize the process of receiving and giving kind but constructive criticism so that we are not precious about our work, and we continue to grow while also supporting each other. Everyone has a different body with a different kinesthetic history, that’s what makes us all so interesting and beautiful, and those differences should be celebrated rather than stifled.”

In closing, Hodges thanked his department, his students, and colleagues for the award, and the chance to teach at the University of Nevada, Reno.

“I really want to thank my entire department. I am so lucky to genuinely like everyone that I work with, that we all respect each other and support each other, even when we may have different viewpoints. Additionally, the students in our department are just the best! I feel very, very lucky to have found a home at the University.”

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