Description of Medical Students’ Behavioral, Cognitive, and Psychological Engagement with Faculty Online Teaching Styles

Introduction

Most medical students are capable learners, and they can quickly achieve the learning outcomes with little guidance from an excellent medical teacher. It is not the transfer of knowledge, but the interaction between a good teacher and the student makes a significant transformative change in the efficiency and quality of the learner into a desirable doctor a community would expect.¹

According to pedagogical social learning theory,² learning and thinking are social activities, and the learning environment influences thinking. Learning also depends on the personal capabilities, the motivation that drives the learning, goals, place of learning, and the learning style. Students accept less teaching and learning activities when they do not match their needs. Hence, teachers need to change the strategies suitable to the changing learners’ learning and engagement behaviors.¹ Medical student engagement is an enigmatic multifaceted meta-construct represented by a bio-ecological model.³

Hence, medical teaching is one of the crucial influences on student engagement during on-campus medical teaching-learning activities. The global lockdown due to recent COVID-19 pandemic forced to change undergraduate medical teaching environment from traditional classroom to virtual classroom, giving a short time in reorganizing the teaching methods to tele teaching technologies as a substitute for in-person lectures. The modifications in the teaching-learning environment are not only a necessity but will also lay the foundation for innovations in medical education.⁴ However, significant challenges for effective online teaching and learning in medical education are a lack of sense of belongingness and connectedness, lack of student engagement, distractions, and technical issues.⁵

Students’ effective and efficient learning depends on the quality of teaching in blended learning that harms learners’ engagement. Hence, it emphasizes the outcome-based learning is more important than the learning process.³ The teaching style includes instructional behaviors such as how teachers provide information, and how they communicate with students during the teaching-learning process.⁶ The teaching style is an implicit attribute of a teacher who has different styles based on their perception of class, pedagogical teaching principles, and learners’ learning capabilities.⁷ Student engagement is a complex term whose definition was not consistent, and different methods were used to measure student engagement in the literature. There is no broad conceptual framework to understand how the students engage in the classroom and how the teacher plays a role in student engagement.⁸ Attention span, interest, curiosity, and passion determine student engagement in learning.⁹ A sudden shift in medical teaching to virtual platforms led to research on learner engagement and teaching effectiveness during online continuous medical education.¹⁰ Furthermore, there is plenty of research on how student engagement can be improved by using different methods during online didactic lectures.^11,12 The complexity of student engagement during online learning and the importance of teacher instructional role in student engagement are considered in planning the present study. The study was taken up to understand how the teachers’ instructional attributes correlate with the learner’s cognitive, psychological, and behavioral engagement. This study hypothesizes that the expansion of teacher role towards developing connectedness with students during virtual teaching-learning activities will motivate the learners towards psychological, behavioral, and cognitive engagement.

Materials and Methods

The present study is a quantitative cross-sectional opinion survey that uses prevalidated student engagement scale (Figure 1) and teaching style inventory in higher education (TSIHE) (Figure 2).

Figure 1 Online Student engagement scale. Abbreviations: CPE, Cognitive Problem Solving; PC, Peer Collaboration; PM, Psychological Motivation; CS, Community Support; II, interaction with instructor; LM, Learning Management. Note: Adapted from Lee J, Song HD, Hong AJ. Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability. 2019;11. Creative Commons.15.

Figure 2 Teaching style inventory in Higher Education. Abbreviations: TSI, Teacher Student Interaction; DMN, Decision-Making Negotiation; TS, Teaching Structuring; B.C, Behavioral Control. Note: Adapted from Abello, Alonso-Tapia, Panadero. Development and validation of the teaching styles inventory for higher education (TSIHE). Anales de Psicologia. 2020;36:143–154.14.

The study population was all medical undergraduate students who attended large-group online lectures at Gulf Medical University (GMU) and Ras Al Khaimah Medical and Health Sciences University (RAKMHSU) during 2020–21 academic year. The study population included was 447 and 450 of undergraduate students from basic sciences and clinical sciences of GMU and RAKMHSU, respectively.

After obtaining the ethical approval (RAKMHSU-REC-143-2020-21-F-M & IRB/MHPE/STD/10/April-2021) from the RAKMHSU ethics committee and GMU Institutional Review Board (IRB) and permission from the Dean of Medical College, the email addresses of all the MBBS undergraduates were collected. The undergraduate students were individually emailed a Google form that contained both a student engagement questionnaire, TSIHE, and consent. The students were requested to voluntarily participate in the survey by answering all the teaching style and student engagement questionnaire items. The statements in the TSIHE questionnaire were modified as perceptions of the students on faculty teaching styles without changing any meaning of the statements.

Total 45.63% (423 of 927) of undergraduate students participated in the survey, which was considered a good sample size. Demographic data of the samples from both universities were comparable (Table 1).

Table 1 Demographic Data of Study Population

Instruments Used in the Study

Teaching Styles Instrument

Teaching Style Inventory in Higher Education (TISHE) was a prevalidated instrument by collecting data from 3312 university students to measure the teachers’ teaching styles.¹⁴ This instrument consists of twenty-eight items that measure four constructs, namely teacher–student interaction (TSI) with ten items, decision-making negotiation (DMN), teaching structuring (TS), and behavioral control (BC) with six items each. The reliability of items within the construct and between the constructs was excellent, with Cronbach’s alpha value 0.974.

Answers from the participants were collected using a four-point Likert scale with one completely agreeing, two as agree, three as disagree, and four completely disagreed for each item of TSIHE. The teaching style constructs were classified into two categories based on Likert scale scores. Teacher Student Interaction was grouped into emotionally attached and emotionally detached, whereas decision-making negotiation was grouped into compromised with decision-making and authoritarian. Similarly, the teaching structuring domain was grouped as flexible and rigid. Strict and Lenient were the two groups for the behavioral control domain.

Student Engagement Instrument

The instrument used for student engagement was a prevalidated student engagement scale for e-learning on 737 Korean university students.¹⁵ The instrument contains twenty-five (25) items and three cognitive, psychological, and behavioral engagement constructs. Each main domain of student engagement is a combination of two sub-constructs. Cognitive engagement is due to cognitive problem solving tested by five items, and peer collaboration contains five items. At the same time, psychological engagement is a combination of psychological motivation tested by five items and community support tested by three items. Similarly, behavioral engagement contains interaction with the instructor tested by two items and learner management by four items. The reliability of items within the construct and between the constructs was excellent, with Cronbach’s alpha value 0.901.

A five-point Likert scale measured each item of the student engagement scale with one completely agreeing, two agree, three not sure, four disagree, and five as completely disagree. The three domains of student engagement were grouped based on Likert score into engaged and disengaged.

Data Analysis

The data collected was analyzed by using Statistical Package for Social Sciences (SPSS) software 22. Both descriptive and inferential statistics were used for data analysis. Descriptive statistics were used for analyzing the students’ responses to the questionnaires. The difference in the student engagement and the perceptions of their faculty teaching styles among both university students was analyzed by using by unpaired Student’s t-test. Lambda coefficient was used for correlation between the four domains of teaching styles as perceived by the students and their psychological, behavioral, and cognitive engagement during online teaching. The results from both the universities were compared to exclude biases and identify contextual differences in the student engagement. To keep the results anonymous the universities were named as university 1 and university 2. Conclusions were made based on the results.

Results

The majority of students from both the universities perceived that their faculty were emotionally attached during teacher–student interaction, compromised in decision-making negotiations, flexible in teaching structure, and were lenient in behavioral control of the class (Table 2). Though university 2 students perceived more positively than the university 1 students about their faculty online teaching style, there was no statistical difference among the students from both the universities on perception of their faculty’s online instructional style.

Table 2 Online Teaching Styles of Faculty as Perceived by Students

Student Engagement During Online Teaching

During the online classes, 64% of students from university 1 and 52% of students from university 2 were disengaged; maximum disengagement was seen in the psychological domain with 70.5% from university 1 and 68.5% from university 2. Around 36% of the students from university 1 were engaged, of which the maximum (46%) engagement was in the cognitive domain, followed by the behavioral domain (40%) and a minor engagement was in the psychological domain (22%), whereas 61%, 52%, and 31.5% of university 2 students showed a good amount of cognitive, behavioral, and psychological engagement compared to university I students. Though the difference in the degree of student engagement as a whole was statistically significant among students from both the universities, when compared the three domains of student engagement across the level of study, both university students showed no statistically significant difference in their engagement in all 3 domains (Tables 3 and 4). The overall difference is due to more cognitive engagement of first two years of students from university 1 where a dedicated faculty in basic sciences was involved in teaching. Similarly, better psychological engagement was seen among two final clinical year students from the university 2 where a dedicated clinical faculty was involved in teaching. In both universities, there was a significantly lower overall engagement among 3^rd year students in university 1 and 2^nd year students in university 2 who were in transition from basic sciences to clinical sciences.

Table 3 Students’ Engagement During Online Teaching

Table 4 Difference in Students’ Engagement for Online Teaching Across the Educational Level

Lambda co-efficient was calculated by non-parametric analysis using crosstabs for nominal data to determine the strength of association between the four domains of teaching styles and the three domains of student engagement. The zero value of Lambda co-efficient was considered no association, whereas weak association when Lambda coefficient was 0.01–0.9. Similarly, moderate association and evidence of strong associations were considered if the lambda coefficients were 0.1 to 0.29 and 0.30–0.99, respectively. Finally, a perfect association was considered when the lambda coefficient was 1.

Students’ cognitive engagement moderately to strongly correlated with faculty decision-making negotiations style during online teaching. At the same time, there was a moderate correlation between students’ cognitive engagement and faculty teacher–student interactions style, behavioral control, and faculty teaching structure style (Table 5).

Table 5 Relationship Between Four Teaching Styles and Online Student Cognitive Engagement

The students’ behavioral engagement had a strong correlation with teaching student interaction, decision-making negotiations, and teaching structuring styles, whereas there was no correlation between behavioral engagement and behavioral control teaching style among the university 1 students. However, the university 2 students’ behavioral engagement was moderately correlated with all four domains of teaching styles (Table 6) during online classes.

Table 6 Relationship Between Four Teaching Styles and Online Student Behavioral Engagement

Overall, the psychological engagement during online classes was very low among the students. Interestingly, there was no correlations between the four domains of teaching styles and students’ psychological engagement (Lambda coefficient zero) (Table 7).

Table 7 Relationship Between Four Teaching Styles and Online Student Psychological Engagement

Discussion

There is an increasing emphasis on student engagement as a strong predictor of student and institutional outcomes.¹³ However, it is not an individual but a complex, multifaceted construct influenced by content, interactions, and context. Multiple teachers’ instructional behaviors are one of the influencing factors at the micro-level, as revealed in the present study and other supporting studies.^4,16–21 Due to the uncertainty of environmental influences and contexts, students were considered ”differently engaged²² rather than disengaged.

The present study showed a uniformity in student engagement among gender, region, and basic sciences and clinical sciences students. Similar findings were seen in some of the studies,¹⁹ and some found that there were gender, regional, and educational level differences in the engagement of males being more familiar with new technologies.^23–25

Interestingly, there was a significant decline in all domains of student engagement in third-year medical students of university 1 and in second-year students of university 2. These years were in transition from modular teaching to clinical exposure which was crucial for students’ experiential learning. These middle years medical graduates’ disengagement can be explained by the critical transition, unpreparedness for online education, and clinical attachment loss might have created psychological insecurity, demotivation, and disinterest. This point was supported by a mixed-method study using virtual simulations with increased engagement and other studies.^26–30

The present study found a strong correlation between cognitive engagement and decision-making negotiation teaching style in university 1 students and moderate correlation in university 2 students. Students’ ability to solve knowledge-related problems and peer collaboration are essential factors for cognitive engagement. During online classes, student–teacher partnership in the decision-making of learning activities encourages the student’s autonomy and peer collaboration. It was similarly shown in a multi-center study on the effect of teaching style on online student engagement and learning experiences,^31,32 supporting the present study’s findings.

We found a moderate correlation between cognitive engagement and teacher–student interaction, behavioral control teaching styles in both university students. Student knowledge acquisition and problem-solving behaviors were encouraging with an emotional attachment during teacher–student interaction and a strict behavioral control teaching style. The finding was similar to in-class engagement of the students in active learning where the cognitive process, verbal and non-verbal learner behaviors co-occur through good interaction with instructors.³³ There was a weak correlation between student cognitive engagement and teaching structuring in university 1 students and a moderate correlation in university 2 students. Flexibility in teaching structuring engaged the students cognitively than rigid teaching structure. Online flexible teaching structure is an immediate micro-environment management that influences the student interest in cognitive learning.³⁴ While using technology like mobiles and other communication devices, optimized challenges and clear goals in structuring teaching create a better cognitive engagement.³⁵

The present study found a strong correlation between student behavioral engagement and decision-making negotiation teaching style, teaching structure style, and teacher–student interaction in university 1 students, whereas moderate correlation was seen among university 2 students. Behavioral student engagement was a continuous learning, effort, and sustained concentration in learning¹⁵ were possible with a combination of interaction with instructors and self-managing and self-determination in online learning activities.^36–39 The enhanced behavioral engagement was an effect of bidirectional factors like teacher involvement and student motivation, as supported by some studies,^40,41 that were similar to our findings.

Behavioral and cognitive behaviors overlap in student engagement and learning activities. Our study found that interactive instructions, instructional environment, and peer collaboration influence student cognitive and behavioral engagement. Other studies supported these findings.^38,42–44

The present study established no correlation (λ = 00) between psychological student engagement and teaching styles in both the university students. Motivation and community support are important influences on student psychological engagement. Some studies showed that online teaching-learning enhanced motivation.^4,38 In their review, Nick Zepke et al concluded that self-determination theory and self-belief are important motivators of student engagement, similar to our study that identifies the student psychological engagement does not have any association with the teaching styles. However, there is a need for the institutes and faculty to create opportunities to enhance the students’ self-belief. A self-determined pedagogical learning style characterizes autonomy, competence, and feeling of relatedness. Self-determination plays a vital role in online courses.^2,37,45 However, the andragogic teaching and learning approaches are still needed for successful student engagement⁴⁶ as the early undergraduate medical students were adolescent (<21 years) according to the World Health Organization definition. The transition from andragogic learning to pedagogical learning in learning activities is desirable in the process of information transformation to student transformation.

Strengths of the Study

The present study used TSIHE, a multidimensional instrument for measuring the faculty’s psychological and pedagogical traditions of teaching styles¹⁴ for the first time in the medical education literature.

Similarly, the student engagement scale for online engagement after the validation by the author¹⁵ was found reliable for further studies.

The sample size was good enough to measure the student engagement and its relation with faculty instructional behaviors.

This study established a relationship between faculty instructional behaviors and student engagement in online courses for the first time in literature and the Middle East medical education context, providing guidance for future research.

Weaknesses of the Study

The study adopted a quantitative opinion survey methodology and cannot be generalized. However, it can guide further research. The survey collected the student experiences retrospectively rather than during online courses. Hence, there may be a recall bias.

Conclusion

To summarise, the perceptions among the students from two universities on their faculty online teaching styles were similar. The degree of students “disengagement from both universities was high compared to their engagement for online classes. Both university students” cognitive and behavioral engagement was moderate to strongly correlated with four domains of teaching style. Interestingly, there was no correlation between online faculty instructional methods and students’ psychological engagement from both the universities. The present study establishes the relationship between student engagement and teaching instructional practices. However, there is a need to develop robust evidence on the influencing factors on student engagement during online and blended contexts.

The literature review of this study found that there are not enough studies developing the faculty development programs to meet the student-centred learning in blended and online medical education. There is a need to develop appropriate multidimensional instruments to measure student engagement and teaching styles during online and blended teaching-learning activities.