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ARTICLE INFO

ABSTRACT

Article history:

Received XX Month 2024

Revised XX Month 2024

Accepted XX Month 2024

Online first

Published 1 September 2024

As stated in “COVID-19 Malaysia Updates” (2021), the origin of the COVID- 19 outbreak in Malaysia can be traced back to the first case arriving on Malaysian shores on January 25, 2020, when a passenger from China tested positive for the virus. Since then, people's lifestyles, habits, beliefs, feelings, and behaviors have changed. Working people started another way to work, which is Working from Home (WFH). The same goes for students, online distance learning (ODL) was introduced. Students need to adapt to a new type of learning from home. Undoubtedly, every student will achieve a different level of academic performance. However, the changes related to the COVID- 19 outbreak started affected the educational context including the students itself. All universities also need to face and there are some universities still facing those challenges until now. Therefore, this study can let us know the main factor influencing a student’s academic performance and how a pandemic affects students’ learning behavior. In this study, Multiple Linear Regression was used to find the most significant factors affected students’ academic performance during Online Distance Learning (ODL). This study was conducted at UiTM Arau Branch, Perlis Campus. involving students from part five Diploma in Mathematical Sciences and Bachelor of Science Management Mathematics. Sleeping hours, study hours, number of subjects taken, residential area and gender were the factors included in this study. From the result, it shows that residential area is the factor that influencing students’ academic performance the most and appears to have a statistically significant impact on the Grade Point Average (GPA) of students.

Keywords:

Academic

Learning

Mathematics

Online

Regression

Significant

DOI:

10.24191/jcrinn.v9i2

# introduction In the era of globalization, social media is one of the essential things. Technological development is advancing day by day. People all over the world are mainly addicted to the use of social media. In 2018, the internet users worldwide were about 4.021 billion, and 3.196 billion people use social networks regularly worldwide (Azizi et al., 2019). Online distance learning was introduced because of the COVID-19 pandemic. All students must adapt to a new way of knowledge: Online Distance Learning (ODL). According to Price Banks and Vergez (2022), they discovered that the majority of students preferred in-person courses. Connectivity problems and uneven learning structures served as the foundation for this. The main challenge or issue for most students in online learning is internet connectivity and lack of concentration and focus in class (Pazil et al., 2022). Students are also physically separated from teachers and the school and are primarily in charge of their education (Bagriacik Yilmaz, 2019). However, some students adapt quickly and maintain their learning methods even during ODL. Thus, one of ODL’s main goals is to make the student-teacher relationship more convenient and adaptable (Bandara & Kanchana Wijekularathna, 2017). Classes are methodically planned and held simultaneously at a convenient time for both professors and students after extensive negotiation with both parties. They are recorded and made accessible so that students may if required, revisit them afterward (Fish & Snodgrass, 2019). It is also fascinating to note that some students claim they put more time and effort into their online assignments; even then, it's reasonable to believe that they will be performing better academically (Cerezo et al., 2016; Conijn et al., 2017; Joksimović et al., 2015; Motz et al., 2019). But Motz et al. (2021) find the opposite. Students who put more effort into their assignments felt more accomplished when studying under normal conditions. However, they also received lower grades. It is unquestionably crucial to examine the issues of workload and access to digital resources. Academic performance is the measurement of student achievement across various academic subjects. Teachers, lecturers, and education officials frequently use high school graduation rates, annual standardized examinations, and college admission exams, including Grade Point Average (GPA), to gauge student achievement. GPA is a student’s mark and grade for each semester. It is calculated based on grades for each subject taken for a semester. Grades from each subject will be multiplied and divided by the number of credit hours taken. Other than that, the Cumulative Grade Point Average (CGPA) is a mechanism to evaluate a university graduate’s knowledge in Malaysia. Marks from each subject will be multiplied and divided by the number of credit hours taken. Improving their GPAs for every semester can also help students increase their CGPA. An excellent academic performance does need a lot of effort and hard work. It does depend on the student themselves. Undoubtedly, every student will achieve a different level of academic performance. Besides, the changes related to the COVID-19 outbreak have also affected the educational system. All universities have faced and are still facing many challenges. There is always a chance for a student to miss a lecture due to problems such as internet connection, devices to attend online classes and without two-way communication between lecturer and students. However, some of students might have problem with their time management. This problem causes the students failed to manage their time to do some revision after online class end, sleeping hours and other daily activities at home. Many factors can influence students’ academic performance. Based on the previous studies, students’ performance is indicated by the students’ capability to establish the required skills and knowledge expected by future employers and to fulfil the public expectation (Shaffee et al., 2019). Students successfully understand and improve their current knowledge and can decide when facing the subject's difficulties (Sardauna & Yusof, 2018). Moreover, the studies have been done among Nigerian students found that academic assessment, parent or family background, and teaching methods have a more significant impact on student’s academic performance than the school's conductivity and general educational environment (Ayodele et al., 2016). Another factor affecting academic performance is sleeping hours. Sleep is a crucial component of learning and practice, as well as physical and mental wellness, and is an essential component of human health and life (Jalali et al., 2020). Facilities are the other factor that affects student learning achievement. Akomolafe and Adesua (2016) said that adequate learning facilities would encourage and stimulate students to be more active in learning. Teachers mainly need good learning facilities to create the ideal working environment. The same happens to the students; if the learning facilities are sufficient, there will be good motivation to learn, which can increase learning achievement. Regarding Mushtaq and Khan (2012), the students actively engaged in the learning process are observed to have a positive correlation with the CGP. Previously, Multiple Linear Regression (MLR) have been successfully employed in educational research. For example, Mahmud et al. (2022) employed multiple linear regression to determine the factor that affect students’ academic performance in during Open Distance Learning (ODL) class. It has been discovered that hometown areas and hours students spent preparing before class are significantly to the model. Therefore, it has been demonstrated that students who live in rural areas perform significantly better academically than students who live in cities, and the more time students spend preparing for class, the lower their CGPA. Furthermore, Jiménez et al. (2020) examine the impact of academic performance variables by using multiple linear regression. It was found that the variables that affect students in achieving higher academic performances were: age, scholarship access, student salary (part-time jobs), and availability of professors that use digital platforms while the variables that having a full-time job, having a tech device for gaming, failing a class, and failing a semester showed a negative impact on student academic performance. Meanwhile, Ibrahim et al. (2024) studied multiple linear regression in determining the factor that contribute to students' failure such as test score, absentee, gender, and repetition. Based on the finding, attendance and test score have significantly affect to the student’s final examination score. Hence, the main objective of this study is to perform multiple linear regression in order know the main factor that affects students’ academic performance during Open Distance Learning (ODL) class among Diploma in Mathematical Sciences and Bachelor of Science Management Mathematics students in semester five at UiTM Arau Branch, Perlis Campus. # methodology This data was collected after the announcement of the final exam results, July 2022 - February 2023 by using online questionnaire via google form. In Section A, respondents needed to fill their gender and GPA for semester 4. In Section B, the respondents answered few questions about factors influencing students’ academic performance during Online Distance Learning (ODL). The questions included in Section B are “How many subject(s) taken”, “How many hours do you sleep” and “How many hours you do revision including weekend. Multiple Linear Regression was used to find the most significant factors affected students’ academic performance during Online Distance Learning (ODL) by formulating the problem which is set of variables, validating assumptions and evaluating the fitted model. During the validating assumptions stage, five assumptions must be met or the process must be redone from the beginning (Montgomery et al., 2021). Throughout evaluating the fitted model, the estimated model was tested with three tests before it could be claimed as the best model and could subsequently be used to forecast values. Data was analysed using R-studio to represent the finding. # finding and discussion ## Set of Variables The study analyzed the factor on students' performance in subject by measuring students' GPA on five factors which are sleeping hours, study hours, number of subjects taken, mean score of study environment, men score of study lifestyle and gender as shown in Table 1. Table 1: Set of variables | | | | | ------------------------------------ | ----------------------- | ----------------- | | **Variable** | **Type of variable** | **Variable Code** | | Grade Point Average (GPA) Semester 4 | Quantitative continuous | GPA | | Sleeping hours | Quantitative continuous | Sleep | | Study hours | Quantitative continuous | Study | | Number of subjects taken | Quantitative discrete | Subject | | Residential Area | Qualitative | Area | | Gender | Qualitative | Gender | ## Forming the Model Where : Grade Point Average (GPA) : the intercept : The regression coefficient for independent variables : Sleeping hours : Study hours : Number of subjects taken : Residential Area : Gender : Model's error term or residuals ## Validating Assumption In regression analysis, many assumptions about the model and the Multiple Linear Regression (MLR) model are one of the fussier of the statistical techniques as it makes several assumptions about the data. If one or more assumptions are violated, then the model in hand is no longer reliable and not acceptable in estimating the population parameters (Daoud, 2018). In this study, four assumptions were discussed. Table 2: Parameter Estimates | model | Beta | Std. Error | Std. Beta | t | sig | Correlations | VIF | | ----------- | ------- | ---------- | --------- | ------- | ----- | ------------ | ----- | | (intercept) | 4.030 | 0.716 | | 5.625 | 0.000 | | | | Gender | \-0.023 | 0.099 | \-0.034 | \-0.235 | 0.816 | \-0.028 | 1.133 | | Study | 0.173 | 0.124 | 0.205 | 1.399 | 0.170 | 0.339 | 1.177 | | Sleep | 0.212 | 0.649 | 0.049 | 0.327 | 0.745 | 0.053 | 1.202 | | Subject | \-0.720 | 0.644 | \-0.154 | \-1.118 | 0.271 | \-0.165 | 1.039 | | Area | \-0.233 | 0.080 | \-0.411 | \-2.897 | 0.006 | \-0.455 | 1.068 | 1) > **The relationship between independent variables and dependent variables is linear** **The Pearson correlation analysis was done to determine the strength of the relationship between the dependent variable which is students’ academic performance (GPA) and independent variables are sleeping hours, study hours, number of subjects taken, residential area and gender. From the result in table 2, it was found that all correlations values are in between ±0 \< r \< ± 0.5. Pearson Correlation was identified 0.053 and 0.339 for sleeping hours and study hours respectively with GPA semester 4. It shows that exists weak positive relationship between sleeping hours and study hours with GPA semester 4. Meanwhile, exists weak negative relationship for number of subjects taken residential area and gender with Pearson Correlation values -0.165, -0.455 and -0.028.** There is no control or constant component, as evidenced by the connection between the two variables. 2) > **The value of Residuals is Independent** The Durbin-Watson is used to detect autocorrelation in the residuals of the regression model, with values close to 2 indicating no significant autocorrelation. This assumption had been met, as the value obtained 2.0601.

Table 3: Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	0.5343	0.2855	0.1939	0.2383	2.0601

3) > **Homoscedasticity** **Homoscedasticity, also referred to homogeneity of variances. It shows the consistency in the dispersion of differences between predicted and observed values for any given random variable within an experiment. Based on Figure 1, it reveals that the spread of residual remains roughly constant across all levels. Therefore, homoscedasticity assumption had been met.** > ![](66726d4f91646_media/media/image11.emf) 4) > **The Residuals follow a Normal Distribution** The Q-Q plot in Figure 2 shows the residuals fall along a roughly straight line at a 45-degree angle. Since the data fall perfectly on the line, it indicates that the data follows the theoretical distribution. The data are normally distributed. ![](66726d4f91646_media/media/image12.emf) ## ## ## 5) > **Checking Multicollinearity** Multicollinearity is when there is a correlation between independent variables in a model. Based on Table 2, the VIF values for the predictor variables in the model are close to 1, multicollinearity is not a problem in the model. Therefore, there is no absence of multicollinearity among the independent variables. ## Evaluate the Model Evaluating the estimated model is a necessary but often overlooked procedure. However, it is a necessary prerequisite before the estimated model can be claimed as the best model and used to forecast values. The subsections that follow describe some of the most common statistical testing procedures.

Table 4: ANOVA
Model	Sum of Squares	DF	Mean Square	F	sig.
1	Regression	0.884	5	0.177	3.116	0.01842
	Residual	2.214	39	0.057
	Total	3.098	44

1) **Fitness of the Model** This is a test for the overall fitness of the model. The examination will reveal whether all or part of the independent variables should remain in the model. The test criterion used is the *F-test statistic*. The null hypothesis to be tested states that all coefficients in the model are equal zero, that is : The regression model is not significant : The regression model is significant The overall F-test can be found in the ANOVA table in the statistical output. To interpret the *F*-test of significance, the *p*-value for the *F*-test must be compared to a 5% significance level. From Table 4, the *p*-value 0.018 is less than the significance level of 0.05, then is rejected and hence the data provide sufficient evidence to conclude that the regression model is significant. 2) **Goodness of Fit** The standard measure of the goodness of fit is the *coefficient of determination*, . From Table 2, the coefficient of determination, shows that 28.55% of the total variation in CGPA is explained by the independent variables in the model. The correlation coefficient indicates that there is a moderate positive correlation between observed and predicted values. 3) **Statistical Significance of the Independent Variables** The independent is significant when the *p-value* is less than 0.05. Area (B = -0.233, p =0.006 \< 0.05) contributed significantly to the model while gender (B = -0.023, p =0.816 \> 0.05), study (B = 0.173, p =0.170 \> 0.05), sleep (= 0.212, p =0.745 \> 0.05), and subject (B = -0.720, p =0.271 \> 0.05) are did not. These values are presented in Table 4. From this value, it can be concluded that residential area is significant variables towards students’ GPA. Therefore, the estimated model coefficient is GPA = 4.030 - 0.023Gender + 0.173Study + 0.212Sleep - 0.720Subject - 0.233Area # conclusion The Covid-19 pandemic will undoubtedly bring about many changes in people's daily lives. Specifically, students are unable to attend classes as normal. Everything needs to be done online. Online Distance Learning (ODL) can offer benefits as well as drawbacks for students. This study wants to find the significant factors that might influence students’ academic performance during Online Distance Learning (ODL). To achieve the objectives of this study, sleeping hours, study hours, number of subjects taken, residential area and gender were the factor that has been analyzed using Multiple Linear Regression method. The result shows that residential area is a significant factor with students’ academic performance during pandemic. This result fitted with (Mahmud et al., 2022). However, there were some challenges faced during this study which is lack of time and respondents. For future research, it is recommended to spend as much time as possible gathering data and increasing the sample size based on additional strata from various academic programs. As a result, the outcome may be more precise. Furthermore, the next study can look into characteristics that influence GPA or CGPA in face-to-face classes. # Acknowledgements/Funding The authors would like to acknowledge the support of Universiti Teknologi Mara (UiTM), Cawangan Negeri Sembilan, Kampus Kuala Pilah and Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia for providing the facilities and financial support on this research # Conflict of interest statement The authors agree that this research was conducted in the absence of any self-benefits, commercial or financial conflicts and declare the absence of conflicting interests with the funders. # Authors’ contributions **Anis Muneerah Shaiful Bahari**: Conceptualisation, methodology, formal analysis, investigation and writing-original draft; **Nurhaswani Alias**: Conceptualisation, methodology, and formal analysis; **Zainovia Lockman**: Conceptualisation, formal analysis, and validation; **Haslina Misran**: Conceptualisation, supervision, writing- review and editing, and validation # References Akomolafe, C. 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