Open and Distance Learning: Factors Affecting the Learning Performance of First-Year Students in Higher Education
Institution
Siti Fazilah Hamid1*, Norlaili Harun1, Nor Hidayatun Abdul Razak1, Musramaini Mustapha1, Noorsuraya Mohd Mokhtar1
1 Faculty Business and Management, Universiti Teknologi Mara Pahang, Bandar Tun Abdul Razak Jengka, Pahang Malaysia
*Corresponding Author: [email protected] Accepted: 15 November 2021 | Published: 1 December 2021
DOI:https://doi.org/10.55057/ijares.2021.3.4.2
_________________________________________________________________________________________
Abstract: The pandemic of Coronavirus (COVID-19) has resulted the universities to switch from face to face learning to open and distance learning (ODL) whereby teaching and learning is undertaken remotely and on digital platforms. Although ODL is considered as a good solution to the teaching and learning problems, students still have problems engaging and participating in the learning process. Some students without reliable internet access and/or technology struggle to participate in digital learning. Hence, this research was conducted among the first-year diploma students in a public university in Malaysia. The purpose of this research is to identify the factors affecting ODL learning performance among the first-year students in higher education institution. This research used a quantitative method approach.
The data were collected using a purposive sampling technique which involved 92 of respondents. The data was gathered through online surveys and analyzed using the SPSS version 27. Research results show that motivation significantly affects students’ academic performance, but not other factors. Therefore, this study can add value to the ODL literature as a learning trends in higher education institution in Malaysia.
Keywords: open and distance learning, learning performance, higher learning institution _________________________________________________________________________
1. Introduction
In late December 2019, the world was shocked by the spread of Coronavirus (Covid-19) pandemic. It was first started in Wuhan, China and quickly spread throughout the world from the health services to shelter confinement with enormous death (Azmi & May, 2021). The spread of Covid-19 has sent shockwaves across the globe. There is no doubt that Covid-19 has changed education dramatically. The pandemic has forced school closures all across the world.
The pandemic led to the closure of for more than 900 million learners around the world schools and universities (Goudeau, Sanrey, Stanczak, Manstead & Darnon, 2021).
In order to prevent the contagious effect of this pandemic, the Malaysia government has to implement the Movement Control Order (MCO). The reinforcement of MCO has severely impacted all sectors including education sectors as universities were instructed to close their campuses (Tadeo, 2021). Hence, in ensuring the continuity of the education systems, most universities have switched from face-to-face learning to ODL. Currently most universities
have adopted this new norm in teaching and learning process. For instance, University of Cambridge has become the first university which has shifted to ODL for its full year students 2020/2021 intake (Goudeau, et al., 2021). Since then, most universities around the world have also made the same move. Moving to ODL can enable the flexibility of teaching and learning anywhere and anytime.
The similar case has been observed in Malaysia as schools and universities are closed and the learning processes shifted to ODL. The Ministry of Higher Education (MOHE, 2020) has instructed all public and private universities to shift their traditional face-to-face teaching and learning to ODL starting from March until December 2020 (Hasan, Mohammed, Gazem, Fazea, Abdulsalam & Omar, 2021). The closing of the educational institutions has pressured students and educators to accept the implementation of ODL. For example, academicians and students suddenly find themselves forced to use technology as they teach and learn.
Academicians also were required to change the teaching materials such as notes, assignments, group projects to suit the ODL. The sudden switch to use ODL have led academicians and students to unexpectedly adjust to a novel situation.
Universiti Teknologi MARA (UiTM), one of the public universities in Malaysia, has started its ODL from 12 April 2020 (Chung, Subramaniam & Dass, 2020). The resource person (RP), the faculty coordinator and lecturers in charge of the management syllabus from all 13 campuses of the university were asked to prepare the new lesson plan during Covid-19. During ODL, the final assessment is used to replace the face-to-face final examination and RP were asked to come up with only FOUR (4) ongoing assessments throughout the semester. In addition, to support the ODL processes, UiTM has introduced its own online platform in the Learning Management System (LMS) which is called U-Future to provide online connection between academics and students (Saidi, Sharip, Rahim, Zulkifli & Zain, 2021). Since the implementation of U-Future is not compulsory to the academicians, they have also used other online platforms such as WhatsApp, Telegram, Google Classroom, Google Meet and YouTube (Chung et al., 2020). The UiTM Academic Affair Division also provides webinars and workshops to train the use of these online platforms for the academicians to familiarize and easily adapt to the ODL approaches during the pandemic.
Currently, e-learning and other forms of education via digital platforms come up as the best tools to deal with this situation. Even though, the university have made concerted efforts to maintain learning continuity during this period, students have had to rely more on their own resources to continue learning remotely through the Internet. In particular, learners who do not have access to digital learning resources or lack the resilience and engagement to learn on their own, are at risk of falling behind. Moreover, academicians also had to adapt to new pedagogical concepts and modes of delivery of teaching, for which they may not have been trained. Data from Student Information Management System (SIMS) – an online platform system developed by UiTM Academic Affairs division showed that there was a decrease in dean lists percentage for semester one students during the pandemic as compared to the result of the semester one students before the pandemic. During the pandemic, ODL became a lifeline for education which opportunities of digital technologies offer a stopgap solution during a crisis. Hence, this research is conducted to investigate the ODL learning factors that contributed to the students’ poor performance. This research focused on the semester one diploma students because they are still new with the learning environment in the universities.
Many researchers did study on the students’ performance during the pandemic and students’
preferences of ODL tools to the undergraduate university students (Nabil et al., 2021; Saidi et
al., 2021); yet very limited studies were conducted on the first-year students' performance using ODL specifically during the pandemic with MCO restriction.
2. Literature Review
Open and Distance Learning
The Covid-19 pandemic has transformed the whole education process to online, which has been transited to ODL in most universities worldwide. According to Centre for Innovative Delivery & Learning Development (2020) ODL refers to the provision of flexible educational opportunities in terms of access and multiple modes of knowledge acquisition. ODL is a form of education where there is physical separation of educators from students during the instruction and learning process (Armstrong, Ramsey, Yankey & Brown, 2020). ODL is viewed as one of the effective instruments for widening access and providing flexibility for continuous professional development and lifelong learning (Zuhairi, Karthikeyan, &
Priyadarshana, 2019). ODL is the most important concern of educators and parents because this will directly affect student academic performance. Thus, it is important to take into consideration students’ competencies and characteristics during ODL.
Factors Affecting the Learning Performance
In this research, the factors that affecting the learning performance focused on teaching and learning, time management, motivation, technology and family commitment. Teaching and learning is related to the readiness or willingness of the learner to participate in collaborative learning and the factors influencing the readiness for online learning (Muthuprasad, Aiswarya, Aditya & Jha, 2021). McGhie’s (2017) mentioned that successful students should seek help from lecturers as well as from peers. Ferri, Grifoni and Guzzo (2020) argued that innovations in teaching methods are therefore needed to engage students, stimulating their proactive behaviour, which is difficult to obtain when one is only connected online.
Time management plays a significant role in improving learners’ performance and accomplishments (Ahmad, Batool & Hussain, 2019). Yang, Baldwin and Snelson (2017) reported that time management skills as an important factor for student persistence in online distance learning. For example, if students are not able to adopt a proactive approach to time management and prioritize study deadlines, the risk of overwhelm and stress increases.
Effective time management is associated with greater academic performance and lower levels of anxiety in students (Adams & Blair, 2019).
Motivation is important reasons for students to complete their studies (Sheung, Li & Wong, 2018). Motivation is the essence of teaching and learning process that need to be closely monitored in terms of its implementation and progress (Zuhairi et al., 2019). Meanwhile, Armstrong et al. (2020) argued that students are lack of motivation to learn during ODL.
Hence, encouragement and support from family members are important motivation for students to stay focused and work hard (McGhie, 2017). Educators also can play a key role in motivating students throughout their online study. Student support can be the crucial factor in ensuring their success.
Armstrong et al. (2020) indicated that the minimum technological requirements for successful ODL include the acquisition of hardware such as a computer, mobile device (cellular phones), or webcam, video conferencing applications such as WebEx or Zoom, Microsoft Windows or Apple operating systems, and a stable internet connection. The current technological
Through the internet, students can now obtain instruction and learn with ease at their home. It is very important to consider the preferences and perception of learners while designing the online courses to make the learning effective and productive (Muthuprasad, et al., 2021).
Yang et al. (2017) indicated that family commitments among students where students would take part in maintaining the home through performing housework. This constituted activities such as washing clothes, washing dishes, sweeping floor, caring for young ones and preparing meals for the family. Yang et al. (2017) suggested that students that have potential household commitments need to balance their study and family responsibilities.
3. Methodology
This research used a quantitative approach to gather the data. The self-administrated online questionnaires were designed using a Google form. The online questionnaires were the best way to collect data due to physical distancing restrictions imposed during the Covid-19 pandemic. The questionnaires were provided with a cover letter to the respondents, explaining the essence of the research and concealment. The unit of analysis consisted of part one diploma students from the Faculty of Business and Management in UiTM Pahang Branch. The data were collected using purposive sampling, a technique in which the researcher initially samples a small group of people relevant to the research objectives. This research managed to get 92 respondents as a population. The sampling frame of respondents was obtained from the Division of Academic Affairs, UiTM Pahang Branch.
The survey items were adapted from the factors affecting academic performance of students by Martha (2009). Some items were modified in order to get the required responses to the research questions. The questionnaires consisted of Section A and Section B. Section A asked the General Information of Age, Gender, Enrolment status, Program, Family members involved in ODL at home, the home area and the State/Federal Territory of the respondents.
Questions in Section B were related to the factors influencing the ODL learning performance such as Teaching and Learning (10 items), Time Management (5 items), Motivation (5 items), Technology (5 items) and Family Commitment (5 items). The questionnaires were designed in Malay and English language to provide options for the respondents when answering. The questionnaires were measured using 5 points Likert-Scale ranging from 1 = strongly disagree and 5 = strongly agree. The questionnaires covered a range of questions related to the factors that affect students’ academic performance. Statistical Package for Social Sciences (SPSS) version 27 was used for analysis.
4. Results
Demographic Profiles
Data were collected at one point of time using an online survey through emails and 92 respondents completed the survey. Of these respondents, 14 percent were males and 86 percent were females. Majority of them were from the age of between 18 years old to 20 years old (88%). Most of the respondents were full time students (98.9%). About 70.7 percent respondents from office management and technology and 29.3 percent respondents were business studies program. With regards to family members involved in ODL at home, 31.5 percent involved one family members, 28.3 percent involved two family members, 21.7 percent involved three family members, 13 percent involved four family members and 5.4 percent more than five family members. By home area, the respondents from urban were 67.4% and 32.6%
were from rural area. Also, 48.9% of the respondents had stayed in Selangor, 27.2% in Kuala
Lumpur, 21.7% in Pahang and 2.2% in Putrajaya. Table 1.1 shows respondents demographic information.
Table 1.1: Respondents demographic information
Demographic Frequency
(n=92)
Percentages (%) Gender
Male 14.1 14.1
Female 85.9 85.9
Age
18-20 81 88.0
21-24 9 9.8
25-29 2 2.2
Enrolment status
Full Time 91 98.9
Part Time 1 1.1
Program
Business Studies 27 29.3
Office Management and Technology 65 70.7
Family members involved in ODL at home
1 29 31.5
2 26 28.3
3 20 21.7
4 12 13.0
More than 5 5 5.4
Home area
Rural 30 32.6
Urban 62 67.4
State or Federal Territory
Kuala Lumpur 25 27.2
Pahang 20 21.7
Putrajaya 2 2.2
Selangor 45 48.9
Descriptive Statistics of Instrument
Using the statistical software SPSS version 27, the mean, standard deviation, variance, minimum value and maximum value of each indicator were examined. Outcomes of the study was offered as frequencies, mean, percentage, and the relevant statistical test. Table 1.2 outlines the descriptive statistics for all indicators.
Table 1.2: Descriptive Statistics for All Indicators
Construct N Minimum Maximum Mean Std.
Deviation Teaching and Learning
I think ODL is interesting 92 1.00 5.00 3.0000 1.00548
I think ODL requires creativity for learning to be effective
92 1.00 5.00 3.8261 .85945
I think it is easy to focus on learning during ODL 92 1.00 5.00 2.6413 1.15389 I think it is easy to understand what is being taught
during ODL
92 1.00 5.00 2.5870 1.11091
I think it is important to solve the ODL problems to learn effectively
92 1.00 5.00 3.5217 .99976
I think it is easy to score well in courses evaluation during ODL
92 1.00 5.00 3.0870 1.01258
I think online group discussion during ODL is very effective
92 1.00 5.00 2.6957 1.17426
I think online tutorial classes during ODL are very helpful
92 1.00 5.00 3.3152 1.04754
I think classroom learning is more effective than ODL
92 1.00 5.00 3.6957 1.09684
Time Management
I managed my learning time well during ODL 92 1.00 5.00 2.7826 1.00358 I attended all my online classes during ODL 92 1.00 5.00 3.9022 1.05934 I found it easy to learn according to the timetable
during ODL
92 1.00 5.00 3.3696 1.01329
I completed all assignments on time during ODL 92 1.00 5.00 3.6848 .99371 I organized my learning and leisure time properly
during ODL
92 1.00 5.00 3.0761 .99707
Motivation
I have attended lectures to understand what is being taught during ODL
92 1.00 5.00 4.0109 .95497
I have attended lectures as it is compulsory either conducted normally or during ODL
92 2.00 5.00 4.2391 .84346
I have a strong desire to do well in my courses during ODL
92 1.00 5.00 4.0543 .90620
I think the lecturers have taught interestingly during ODL
92 1.00 5.00 3.4130 .81405
I found it difficult to motivate myself to learn during ODL
92 1.00 5.00 2.3696 1.09662
Technology
I have adequate learning devices (computer / laptop / smartphone / tablet) for use during ODL
92 1.00 5.00 4.0652 1.03568
I have problems with Internet access during ODL 92 1.00 5.00 2.6413 1.09526 I enjoyed learning by using devices (computer /
laptop / smartphone / tablet) during ODL
92 1.00 5.00 3.4130 1.00714
I have sufficient computer skills for ODL needs 92 1.00 5.00 3.3478 .99928 I can quickly get various learning-related materials
via the Internet during ODL
92 1.00 5.00 3.4783 .98871
Family Commitment
I have a lot of household chores to do during ODL 92 1.00 5.00 3.6848 .95996 I must work to support the family income during
ODL
92 1.00 5.00 2.5109 1.21795
I felt exhausted for my family commitment during ODL
92 1.00 5.00 3.3587 1.18212
I spent more time on family commitment during ODL
92 1.00 5.00 3.0435 1.06815
I felt obligated to my family commitment during ODL
92 1.00 5.00 3.2500 1.07545
The data analysis was carried out in order to fulfill the objectives of this study. Firstly, the study looks at the frequency testing. The overall descriptive statistics for all of the five variables; teaching and learning, time management, motivation, technology, family commitment and financial as shown in Table 1.3. The data as shown in Table 1.3 has a skewness of less than 2 and a kurtosis value of lessthan 4. This shows that the data is normally distributed. According to Hair, Black, Babin & Anderson (2014) kurtosis values should not exceed ±3 and skewness values should fall within the range of ±1.
Table 1.3: Overall Descriptive Statistics for All Variables
Construct Mean Standard Deviation Skewness Kurtosis
Teaching and Learning 3.1130 .63336 -.261 -.405
Time Management 3.3630 .78945 -.387 .791
Motivation 3.6174 .60522 -.5.13 1.520
Technology 3.3891 .74442 -.126 .560
Family Commitment 3.1696 .91288 -.021 -.357
Next, the study looks at the reliability of the scale. This is an important analysis to ensure that the scale used for this research is both reliable and able to explain the phenomena. The study employs Cronbach’s Alpha Coefficient to track the internal consistency of the scale. Table 1.4 below demonstrates the results of overall reliability on each construct including the number of items kept under each construct.
Table 1.4: Reliability of the Scale
Construct Number of Items Cronbach’s Alpha
Teaching and Learning 10 .805
Time Management 5 .838
Motivation 5 .662
Technology 5 .775
Family Commitment 5 .884
Pallant (2011) said that a coefficient of scale above 0.7 is a construct with valid measurement.
Meanwhile, Tabachnick and Fidell (1996) stated that the minimum value for a good factor loading analysis is 0.5. As shown in Table 1.3, the construct family commitment has the highest Cronbach’s alpha, as high as 0.884, and the motivation has the lowest Cronbach’s alpha, as .662. Therefore, as the minimum reliability of the constructs is above 0.6, it can be assumed that all constructs of the instrument showed an acceptable reliability level.
Factor Analysis
Factor analysis is a statistical technique used to reduce many variables to a few dimensions (Seiler, 2004). Responses were subjected to a factor analysis using the maximum likelihood method of extraction and varimax, orthogonal rotation. Based on Seiler (2004), both Kaiser- Guttman criterion of retaining factors with eigenvalues greater than 1.0 and Catell's scree test were considered. Bartlett's test of Sphericity is to measure the applicability of factor analysis.
The value of Kaiser-Meyer-Olkin Measure of Sampling Adequacy is recorded at 0.820 (<0.01) provided an acceptable adequacy of using factor analysis. Figure 1.1 shown scree plot diagram showing the Eigenvalues of the items.
Figure 1.1 Scree plot
5. Discussion and Conclusion
This research aimed to investigate the factors affecting the learning performance of first-year students in Higher Education Institution. The findings of this study indicated that majority of the students evinced a positive attitude towards ODL. Respondents indicated that they were motivated to learn regardless of the learning environment. They have attended the classes and understand what is being taught during ODL. Students also were determined to do well in their courses during ODL. Motivated learners are more likely to engage in challenging activities, adopt an approach to learning and exhibit enhanced performance even under challenging circumstances (Armstrong et al., 2020). Thus, it is important that academicians continue to engage and interact with their students, provide assignments, and, importantly, provide timely feedback.
For technology, results showed the most of the respondent have adequate learning devices such as computer and smartphone. The availability of technological devices helped ODL succeed because students used their device in this context. However, results also found that many students do not have stable internet connection. Given that ODL will continue for the foreseeable future, it is imperative for university to further examine this matter and seek viable solutions to assisting students with gaining reliable internet access.
For time management, most of the respondents have completed all assignments on time during ODL. The results of this study suggest students develop good time management skills. Ahmad et al. (2019) indicated that the students’ achievement of distance learning when they are managing their time effectively. Hence, there is a clear association between student performance and their ability to manage time effectively.
For teaching and learning, the majority of the respondents indicated that ODL requires creativity for learning to be effective. However, most of the respondents also reported that ODL could be more challenging than traditional classroom. Thus, all these factors should be considered while developing an online course to make it more effective and productive for the learner. It must be admitted that transition to ODL requires extra workload handled by academicians: additional effort must be devoted to create new types of assignments to promote active student engagement. An academician will need to prepare for the delivery of effective feedback to reduce time loss and providing clear instructions to their students. By carefully considering the assignments aimed at boosting student engagement, academicians will create a productive online learning environment and increase positive learning outcomes in higher education institution.
For family commitment, most of the respondents indicated that they have a lot of household chores to do during ODL such as taking care of family and juggling other commitments.
Involvement students in doing household chores can be identified to have potential risk for their academic performance. Hence, this research suggested that the students should be able to organize their daily tasks so that that they can easily spare some time for learning. Students must be accommodated between their study and family commitments.
Therefore, the findings of this research can be important input in determining the factors affecting learning performance to promote effective learning. The results provide information on the strategies employed by the university to positively impacted student motivation during ODL and what need to be improved. It also provided academicians with the opportunity to try new teaching strategies using tools available online. Even though, once the Covid-19 pandemic
settles down, it is possible in education systems continued increase using online platforms for study aids, albeit in a hybrid mode in combination with regular classes. Thus, this research will prove useful for reimagining and redesigning the higher education with components involving online mode.
6. Limitation and Future Research
Limitations to this research include the student participation. Due to pandemic Covid-19, the collection of data is dependent on the online feedback from respondents through the Google form. Although the Google form is easy to use and share with respondents through emails, the willingness to participate is up to the respondents. Email reminders are useful yet might not be effective as potential respondents perhaps ignore the message. This research is limited only to Business and Management students from diploma program. This research also focused on the variables teaching and learning, time management, motivation, technology and family commitment. Therefore, it is recommended that further research should be undertaken considering that the use of digital tools in education has dramatically increased during this crisis and it is set to continue, thus, there is a pressing need to understand the impact of ODL.
Acknowledgement
The authors would like to acknowledge Universiti Teknologi MARA (UiTM) Cawangan Pahang for sponsoring this research under the Skim Geran Penyelidikan Dana Lestari Khas 2020 (600-UiTMKPH (PJI. 5/2/4/9) DLK (010/2020) of Research Management Centre, Universiti Teknologi MARA (UiTM) Cawangan Pahang.
References
Adams, R. V. (2019). Impact of time management behaviors on undergraduate engineering students’ performance. Creative Work, 10, 1-11.
Ahmad, S., Batool, A., & Hussain, A. (2019). Path relationship of time management and academic achievement of students in distance learning institutions. Distance & Online Learning, 5(2), 191-208.
Armstrong, E., Ramsey, K., Yankey, B., & Brown, S. (202). Covid19 and distance learning:
Effects on Georgia State University School of public health students. Public Health, 8, 1-10.
Azmi, A. S., & May, L. S. (2021). The impact of open distance learning (ODL) on students’
performance in Universiti Teknologi MARA Kelantan branch. International Conference on Language, Education, Humanities & Social Sciences (i-LedHS2021), 406-409.
Centre for Innovative Delivery & Learning Development, CIDL. (2020).
https://cidl.uitm.edu.my
Chung, E., Noor, N. M., & Vloreen, N. M. (2020). Are you ready? An assessment of online learning readiness among university students. International Academic Research in Progressive Education and Development, 9(1), 301–317.
Ferri, F., Grifoni, P., & Guzzo, T. (2020). Online learning and emergency remote teaching:
Opportunities and challenges in emergency situations. Societies, 10, 2-18.
Goudeau, S., Sanrey, C., Stanczak, A., Manstead, A., & Darnon, C. (2021). Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nature Human Behaviour, 5, 1273–1281.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis:
A global perspective. Upper Saddle River, N.J.: Pearson Education.
Exploring the impact of transformation to fully online learning during COVID-19 on Malaysian university students’ academic life and performance. International Interactive Mobile Technologies, 15(5), 25-46.
Martha, K. (2009). Factors affecting academic performance of undergraduate students. (Master Thesis, Makerere University).
McGhie, V. (2017). Entering university studies: Identifying enabling factors for a successful transition from school to university. Higher Education, 73(3), 407-422.
Ministry of Higher Education, MOHE. (2020). https://www.mohe.gov.my
Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, J. K. (2021). Students’ perception and preference for online education in India during COVID-19 pandemic. Social Sciences &
Humanities, 1, 100-111.
Pallant, J. (2011). A step by step guide to data analysis using SPSS. Australia: Everbest Printing Co.
Saidi, R. M., Sharip, A. A., Rahim, N. Z., Zulkifli, Z. A., & Zain, S. M. (2021). Evaluating students’ preferences of open and distance learning (ODL) tools. Procedia Computer Science, 179, 955–961.
Seiler, M. J. (2004). Performing Financial Studies a Methodological Cookbook. Prentice Hall.
Sheung, O., Li, K., & Wong, T. M. (2018). Student persistence in open and distance learning:
Success factors and challenges. Asian Association of Open Universities, 13(2), 191-202.
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics. New York:
HarperCollins.
Tadeo, Carl, P. S. (2021). Online Distance Learning: A teaching strategy on improving students’ performance in social studies in the new normal. Humanities and Educational Advancements, 2(5), 46-50.
Yang, D., Baldwin, S., & Snelson, C. (2017), Persistence factors revealed: Students’ reflections on completing a fully online program. Distance Education, 38 (1), 23-36.
Zuhairi, A., Karthikeyan, N., & Priyadarshana, S. T. (2019). Supporting students to succeed in open and distance learning in the Open University of Sri Lanka and Universitas Terbuka Indonesia. Asian Association of Open Universities, 15(1), 13-35.
