Factors Influencing Learners’ Engagement in the Classroom
Norazmi Fadilah1*, Mazura Mahdzir2,3, Sharifah Mazlina Syed Khuzzan3, Norhidayah Sunarti3, Zety Pakir Mastan3, Nurulhuda Ahmad3, Nik Fatma Arisya Nik Yahya4
1Department of Civil Engineering, Politeknik Sultan Azlan Shah, Malaysia
2Faculty of Built Environment, Department of Quantity Surveying, TARUMIT, Malaysia
3 Faculty of Engineering and Quantity Surveying, Department of Quantity Surveying, INTI International University, Malaysia
4 Kulliyah of Architecture & Environmental Design (KAED), International Islamic University Malaysia, Malaysia
*Corresponding Author: [email protected]
Received: 25 March 2023 | Accepted: 5 May 2023 | Published: 1 June 2023 DOI:https://doi.org/10.55057/ajress.2023.5.2.6
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Abstract: The COVID-19 pandemic has affected many sectors including education, either secondary, primary, and higher education institutions (HEIs). Lecturers and learners faced many challenges to interact effectively since there is a new transition in teaching mode from physical class (F2F) to online learning. In other words, online learning becomes more difficult, particularly during the earlier year of the pandemic for both parties due to various reasons.
One of the major reasons is the lack of learners' engagement in the classroom, which has caused learners' emotions and motivation to learn have been drop significantly. In realizing that enhancing learners’ performance has become an important element for academic success, this paper tries to explore various influential factors related to learners’ engagement in the classroom. The data is collected using a quantitative method through the distribution of a questionnaire to QS degree students. Using factor analysis, it was found that the major contributor that might influence learner engagement was the learner factor, followed by the lecturer factor, educational or institutional factor, and other related factors. Looking into this situation, much effort is highly needed from lecturers and faculty to enhance their engagement in their academic performance.
Keywords: learners’ engagement, performance, online learning, motivation
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1. Introduction
Higher education is experiencing a paradigm shift from passive learning to active learning. The COVID-19 pandemic has further presented an opportunity for education providers to enhance teaching that includes non-campus modes. However, concerns regarding student engagement lie at the heart of the transition to active learning environments in the context of the increased demand for online education (Nepal & Rogerson, 2020).
Learners’ engagement in the classroom remains an important element of their success (Gute &
Wainman, 2019). In theory, Reeve (2008) cited in Wellborn’s (1991) article that engagement refers to the extent of a student’s active involvement in a learning activity. Nepal and Rogerson (2020) include the willingness or level (time and energy) of student participation and involvement in normal student academic, community, and social activities within their institution.
The concept of engagement was shared and defined by a few scholars in several areas. For instance, Finn and Zimmer (2012) have categorized engagement into four areas namely, academic engagement (behavioural), social engagement (behavioural), cognitive engagement (behavioural), and affective engagement (emotional). In another article written by Reeve (2008), he agreed with the behavioural and emotional areas highlighted above but added cognitive and agentic engagement into this definition.
According to Finn and Zimmer (2012), the detailed example of various types of engagement are as follows; (i) Academic engagement - behaviours related directly to the learning process, for example, their ability to complete the task assigned in the class or home. (ii) Social engagement behaviours related to whether students can follow written and unwritten classroom rules of behaviour, for example, be punctual and have good communication skills with teachers and peers (iii) Cognitive engagement behaviours related to the internal investment of cognitive energy, roughly speaking, the thought processes needed to attain more than a minimal understanding of the course content (iv) Affective engagement is a level of emotional response characterized by feelings of involvement in school as a place and a set of activities worth pursuing.
Meanwhile, according to Reeve (2008), the behavioural aspects refer to the learner's attention, concentration, and effort. Meanwhile, emotional engagement refers to the presence of task- facilitating emotions such as interest and the absence of task-withdrawing emotions such as distress (emotional engagement), her usage of sophisticated rather than superficial, learning strategies (cognitive engagement), and the extent to which she tries to enrich the learning experience rather than just passively receive it as a given (agentic engagement).
2. Literature Review
Generally, four major influential factors might contribute to this issue namely:
a) learner factors. These include (i) behavioural aspect (motivation, commitment, attitude);
(ii) Networking/interaction/relationship with peers (active/passive); (iii) learning styles;
(iv) Health issue; (v)Emotional aspect ,(vi) Cognitive aspect (mindset, belief, awareness, vision, intellectual, level of confidence etc) (Nepal & Rogerson, 2020; Werang & Leba, 2022; Groves, Sellars, Smith, & Barber, 2015; Barua, Zhou, Gururajan, & Chan, 2018;
Picton, Kahu & Nelson, 2018; Steen-Utheim & Foldnes, 2018).
b) lecturers factors which include (i) networking/interaction/relationship with lecturers (active/passive, shows support), (ii) teaching style (authoritative, etc),(iii) lecturers’
competency (knowledge base, experience, technical, IT skill, etc), (iv) teaching materials (relevancy, easy to understand, appropriate and sufficient) (Nepal & Rogerson, 2020;
Karafil & Oguz, 2019; Groves, Sellars, Smith, & Barber, 2015; Barua, Zhou, Gururajan,
& Chan, 2018; Steen-Utheim & Foldnes, 2018).
c) educational/institutional institutions which consist of (i) facilities provided (basic/advanced tools/labs/software) (ii) the setting of the classroom (physical layout and arrangement, Lighting and ventilation) (iii) continuous support (loan, guideline, appreciation for best student, etc) (iv) curriculum/syllabus design (learning goals, outcomes, objectives, assessment) (Nepal & Rogerson, 2020; Karafil & Oguz, 2019;
Werang & Radja Leba, 2022; Groves, Sellars, Smith, & Barber, 2015; Picton, Kahu, &
Nelson, 2018; Steen-Utheim & Foldnes, 2018).
d) and other related factors that might include (i) family support (financial, relationship) and (ii) living area and condition (distance, heavy area) (Werang & Leba, 2022)
The implications of missing learners’ engagement in the classroom or lecture are quite apparent. Researchers such as Picton, Kahu, and Nelson (2018) highlighted that the success of learners in studies would largely depend on their great engagement. Reeve (2008) further believed that their engagement in class reflects the lecturers' ability to motivate learners in the classroom positively. This notion was agreed upon by Carini, Kuh, and Klein (2006), who believed that it can be a benchmark for learners' personal and learning development as they will learn more with frequent practice or study.
Finn and Zimmer (2012) also added that learners who are academically and socially engaged in class with their peers are likely able to perform well in academics performance. For instance, paying attention, completing homework and coming to class prepared, and participating in academic curricular activities. Without it, they might experience various problems in (i) academics with low achievement and (ii) behaviour with low motivation and frustration. This was agreed by Nepal and Rogerson (2020) who mentioned that greater student engagement translates into valued student experiences and higher academic performance.
3. Problem Statement
Despite many studies that have been made by previous scholars on this issue, the discussion on learners’ engagement in online learning remains limited (Werang & Leba, 2022). Only a few studies have been dedicated to exploring the factors influencing the engagement of higher education students (Barua, Zhou, Gururajan, & Chan, 2018). Limited research also was identified in the Malaysian context, especially for technical QS students. In realizing the importance of engagement, this paper tries to explore various influential factors related to learners’ engagement in HEIs the classrooms.
4. Methodology
4.1 Data Sampling
There are many sampling methods available in research sampling. Within this context, the most appropriate to determine the sample size is simple random sampling. This is consistent with McCombes (2019), who stated that there is an equal chance for every individual to be selected in simple random sampling. The population size refers to undergraduate students from QS backgrounds within International Islamic University Malaysia (IIUM) campus. The undergraduate students are comprising of 55 3rd-year students.
4.2 Data Analysis
Data analysis is a process of evaluating data by using analytical tools. The data is collected using Google Forms to gather information related to the learners' engagement factors. Using descriptive statistics and frequency analysis, the data (i.e., questionnaire) will be transformed into tables and diagrams, charts.
4.3 Background of Respondents
The questionnaire is sent to 100 students from International Islamic University Malaysia (IIUM) via E-mail and WhatsApp. Out of 100, about 55 responses are obtained after the two- week deadlines given. The response rate for the study is 55% which is considered acceptable and valid. In this section, the questions are designed to gather some basic information from the respondents, such as age, gender, years of study, and point of entry as well as subjects learned.
Table 1: Profile of the Respondents
Characteristics Number Percentage
Age
21-22 51 92.7%
23 and above 4 7.3%
Gender
Male 25 45.5%
Female 30 54.5%
Years of study
Year 1 19 34.5%
Year 2 16 29.1%
Year 3 17 30.9%
Year 4 3 5.5%
Point of entry
SPM leavers 54 98.2%
STPM leavers 1 1.8%
Subject learned
Technical 47 85.5%
Non-technical 8 14.5%
Table 1.0 shows the gender frequency for students who studied at IIUM. Out of 55 respondents, the majority of the respondents are female, with 30 responses (54.5%), whereas the minority is come from the male group with 45.5%.
As for the age range, the frequency table showed that the age group can be categorized into young adults (21-22 years) and adults (23 and above). The aged majority is between 21-22 at 92.7% and 23 and above at 7.3%.
For the years of study, there are four groups of respondents responded to the questionnaire.
The highest percentage is Year 1 with 19 responses (34.5%). This is followed by Year 3 with 17 responses (30.9%), Year 2 with 16 responses (29.1%), and Year 4 with 3 responses (5.5%).
Meanwhile, for the point of entry, the majority of the respondents are SPM leavers with 54 responses (98.2%) and 1 response (1.8%). Within-subject learned context, most of the respondents shared their opinions on technical aspects that they have learned with 47 responses (85.5%) and 8 responses (14.5%).
5. Results and Discussions
5.1 Factors influencing learners’ engagement in lectures or classes.
This paper examines the factors influencing learners’ engagement in the classroom using factor analysis. Based on the analysis, the highest and lowest factor is determined. Using Table 5.1 also, about 4 factors are identified and the Eigenvalues exceed 1.
The main reason for using factor analysis is to identify the main factors that might influence learners’ engagement in classes or lectures. The interpreted data results extracted 6 factors and showed Eigenvalues exceeding 1, and these factors are deemed to make up 81.993% of the total variance across 16 variables (refer to Table 2.0).
According to IBM (2014), the Kaiser-Meyer-Olkin (KMO) Test will measure the sampling adequacy and is also used to identify the proportion of variance of various variables. In this paper, the value is 0.847, higher than 0.5, a high value (close to 1.0). It means that the responses given with the sample are adequate. Besides that, Table 5.0 shows the value of Bartlett’s test of sphericity is 0, not more than 0.05 of the significance level. It also implies that this analysis is valuable with the data.
Table 2: KMO and Bartlett’s test
The factor analysis identified the four main factors: learner factors, lecturers’ factors, educational/institutional institutions factors, and other factors.
The first factor was highlighted as learner factors. All factors were listed as behavioural aspect (motivation, commitment, attitude), networking/interaction/relationship with peers (active/passive), learning styles, health issue, emotional aspect, cognitive aspect (mindset, belief, awareness, vision, intellectual, level of confidence, etc). The variance for the first factor was 72.733%.
The next factor was lecturers’ factors which includes networking/interaction/relationship with lecturers (active/passive, shows support), teaching style (authoritative, etc), lecturer’s competency (knowledge base, experience, technical, IT skill, etc) and teaching materials (relevancy, easy to understand, appropriate and sufficient). The variance explained by this factor was 74.348%.
The third factor relates to educational/institutional factors. These include facilities provided (basic/advanced tools/labs/software), the setting of the classroom (physical layout and arrangement, lighting and ventilation), continuous support (loan, guideline, appreciation for best students, etc,) and curriculum/syllabus design (learning goals, outcomes, objectives, assessment). The variance for this factor was 72.166%.
The fourth factor was related to other factors. Family support (financial, relationship) living area, and condition (distance, heavy area). The variance for this factor was 81.993%. Based on Table 3.0, it can be summarized that there are three main important factors which include learners, lecturers’ and educational/institutional institutions factors.
Table 3: Factors influencing learners’ engagement in lecturers or classes Factor
loadings
Eigenvalues Sum of squared
Loadings
% of Variance Cum % Factor 1: Learner factors
Behavioural aspect 4.364 4.364 72.733 72.733
Networking/interaction Relationship with peers
.539
Learning styles .494
Health issue .279
Emotional aspect .215
Cognitive aspect .109
Factor 2: Lecturers’ factors Networking/interaction Relationship with lecturers
2.974 2.974 74.348 74.348
Teaching style .518
Lecturer’s competency .334
Teaching materials .174
Factor 3: Educational/institutional institutions factors
Facilities provided 2.887 2.887 72.166 72.166
The setting of the classroom .516
Continuous support .352
Curriculum/syllabus design .245
Factor 4: Other factors
Family support 1.640 1.640 81.993 81.993
Living area and condition .360
5.2 Relationships between factors related to learners’ engagement in the classroom and their profile background.
Table 4: Pearson’s correlation coefficient (r) matrix between learners’ engagement in class and their profile background
Variables Age Gender Years of study Point of entry Subject learned
Factor 1 .913 .879 0.377 .373 .217
Factor 2 .593 .689 .603 .366 .121
Factor 3 .514 .586 .618 .055* .417
Factor 4 .777 .978 .793 .447 .253
Factor 5 .708 .907 .873 .241 .105
Factor 6 .638 .713 .686 .359 .070
Factor 1 .081 .674 .111 .364 .341
Factor 2 .720 .587 .144 .402 .007
Factor 3 .535 .369 .041* .334 .002**
Factor 4 .419 .512 .382 .402** .109
Factor 1 .164 .005** .656 .241 .678
Factor 2 .231 .121 .386 .105 .806
Factor 3 .247 .213 .957 .240 .840
Factor 4 .220 .866 .326 .421 .792
Factor 1 .240 .058* .724 .123 .340
Factor 2 - .060* .001** .782 .401
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Using Pearson's correlation coefficient (r), this section examines the relationship between learners' engagement in class and their profile background. In Table 4.0, Pearson's r varies
between +1 (perfect positive correlation) and -1 (perfect negative correlation). It determines that there is a relationship between the two elements.
In general, student factor tends to correspond with age, gender, years of study, points of entry, and subjects learned positively. As can be seen in Table 4.0, the behavioral aspect was having strong correlation with age (0.91and 3), and gender (0.879) and was considered weak with years of study (0.377), point of entry (0.373) and subject learned (0.217). This is followed by networking/interaction/relationship with peers (active/passive), which has been moderately correlated with age (0.593), gender (0.689), and years of study (0.603). However, the relationship between the point of entry and the subject learned is getting weak with 0.366 and 0.121. Learning styles, health issues, emotional aspects, and cognitive aspects are other factors that showed a strong correlation with age, gender, and years of study, having a range between 0.514 to 0.978. Meanwhile, the relationship between learning styles, health issues, emotional aspects, and cognitive aspects with the point of entry and subject learned were considered weak (range between 0.055 to 0.447). Nguyen, Cannata, and Miller (2018) also pointed out the importance of student interaction with peers in their study.
The lecturer factor also has shown a positive correlation with gender. This includes networking/interaction/relationship with lecturers (active/passive, shows support) with 0.674.
Meanwhile, a similar factor indicates a weak relationship between age, years of study, points of entry, and subjects learned, with a range between 0.81 to 0.364. Another lecturer factor namely teaching style (authoritative, etc), also showed a positive correlation with gender.
Meanwhile, a similar factor indicates a weak relationship with age, years of study, points of entry, and subjects learned, with a range between 0.007 to 0.369.
The lecturer’s competency (knowledge base, experience, technical, IT skills, etc.) showed a positive correlation with age at the value of 0.535. Meanwhile, a similar factor indicates a weak relationship between gender, years of study, points of entry, and subjects learned, with a range between 0.002 to 0.72. As for the fourth factor, teaching materials (relevancy, easy to understand, appropriate, and sufficient) showed a positive correlation with the gender of 0.512.
Whereas the remaining variables (age, years of study, points of entry, and subjects learned) have a weak relationship with teaching materials, ranging between 0.109 to 0.419. For instance, Martin and Bolliger (2018) highlighted two important factors namely learner and lecturers’
support and active feedback that might influence effective engagement between both students and lecturers. It can be done by sending regular announcements or email reminders to the students and providing grading rubrics for all assignments.
Meanwhile, educational or institutional factors tend to correspond with students’ age, gender, years of study, points of entry, and subjects learned positively. It can be seen that factor such as facilities provided (basic/advanced tools/labs/software) was statistically significant with years of study (0.656) and subject learned (0.678). This is consistent with the setting of the classroom (physical layout and arrangement, lighting and ventilation) which shows a strong correlation with the subject learned (0.806). Similarly, for the continuous support (loan, guideline, appreciation for the best student, etc), a strong correlation was identified between years of study (0.957) and subject learned (0.840). Lastly, the curriculum/syllabus design (learning goals, outcomes, objectives, assessment) showed a positive correlation between gender (0.866) and subject learned (0.792). Meanwhile, the remaining variables showed a weak relationship with factors as shown in Table 4.3 (range from 0.005 to 0.421).
This notion was also agreed upon by Gunuc and Kuzu (2014), McBrien, Jones, and Cheng (2009), and Gedera, Williams, and Wright (2015), who stated that the educational/institutional
institutions factor remains important to facilitate learners’ engagement in class through the utilization of software in the virtual classroom. He, Zheng, Di, and Dong (2019) further added the use of online learning systems set out by HEIs can increase learners’ engagement in class.
For better engagement too, they added that the lecturer can collaborate and discuss effectively using online communication tools. The engagement varied in terms of learner’s behaviour, cognition, and emotion or affective (Min Hu & Hao Li, 2017; Salas-Pilco, Yang, & Zhang, 2022).
Lastly is family support (financial, relationship) which was positively correlated with years of study (0.724). Similarly living area and condition (distance, heavy area) that has a positive correlation with gender (0.60) and points of entry (0.782). However, this did not apply to other variables (etc age, gender, points of entry and subject learned) as it was negatively correlated with family support (financial, relationship). This is followed by age, years of study, and subject learned which have a weak correlation with living area and condition (distance, heavy area).
In summary, some of the variables show positive correlation and some variables might not, due to the various perspectives shared by different learners’ profiles. All variables remain important in influencing learners’ learning in class.
6. Conclusion
Based on the research findings, it showed that learners’ characteristics such as gender, age, years of study, point of entry and subject learned have been used to determine the factors that might influence learners’ learning in class. These include learner factors, lecturer factors, educational/ institutional factors, and other related factors.
Using the factor analysis table and based on the learners’ characteristics, it was found that out of four main factors, only three main factors can be considered significant to assist learners in learning namely learner, lecturers, and educational/institutional institutions factors. Thus, this research proved that to boost their performance in learning, learners need to be active and alert with their behavioural aspects, networking/interaction/relationship with peers, learning styles, health issues, emotional aspects, and cognitive aspects.
The involvement of lecturers to assist them to focus on learning also remains important. These include networking/interaction/relationship with lecturers, teaching style, lecturer’s competency, and teaching materials. Lastly, learners' learning also can be effectively learned if the following factors were considered namely facilities provided, the setting of the classroom, continuous support, and curriculum/syllabus design.
As suggested, the respondents believed that there are many approaches to make effective engagement in class. One of them is lecturers, whereby they need to improve their personalities to ensure learners can approach them amicably. Besides that, the duration of lectures class also needs to be revised as previously it was quite long which caused them, they lose focus in learning. The engagement among learners can also be improved through the formation of group assignments (mixed gender).
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