324 © 2025 Conscientia Beam. All Rights Reserved. Exploring the relationship between demographic characteristics, academic procrastination, social support, and academic performance among open distance learning students: A random forest analysis Sunday Abidemi Itasanmi1+ Oluwatoyin Ayodele Ajani2 Catherine Njong Tawo3 IDepartment of Adult Education, University of Ibadan, Nigeria. Email: itasunny2000@gmail.com 2Languages and Social Sciences Education, University of Zululand, KwaDlangezwa, South Africa. Email: oaajani@gmail.com 3Department of Continuing Education and Development Studies, University of Calabar, Nigeria. Email: katetawo001@gmail.com (+ Corresponding author) ABSTRACT Article History Received: 22 July 2024 Revised: 10 January 2025 Accepted: 6 February 2025 Published: 26 February 2025 Keywords Academic performance Academic procrastination Demographic characteristics ODL Random forest analysis Social support. This study explored the relationship between demographic characteristics, academic procrastination, social support and academic performance among Open Distance Learning (ODL) students using a random forest approach. The study employed a quantitative research approach. The study participants consisted of 315 students from an ODL institution in Nigeria. The study used adapted scales to measure procrastination and social support and a self-reported performance scale to measure academic performance. Data were analyzed using chi-square analysis for classification while R-studio was used to fit a random forest model to classify and predict the study’s constructs. Results of the study revealed a significant relationship between academic performance and age. Similarly, gender has a significant influence on academic performance. Marital status significantly impacted academic performance, procrastination, and social support. Furthermore, it was revealed that programme level influences academic performance. Employment status was found to influence procrastination tendencies. Lastly, students with lower procrastination and higher social support achieved better academic performance. This study underscored the need for ODL institutions to implement demographic-specific support programmes focusing on procrastination reduction, time management skills, and robust social support networks. These can help enhance academic performance and address the diverse challenges faced by ODL students. Contribution/Originality: The study explored the interrelationships among demographic characteristics, academic procrastination, social support and academic performance among Open Distance Learning (ODL) students in Nigeria. The study contributes to existing literature by utilizing the random forest machine learning approach to classify and predict the interrelationships among the study’s constructs. This approach provided a unique perspective for analyzing a complex dataset, especially in an ODL context. 1. INTRODUCTION In recent years, Open and Distance Learning (ODL) has emerged as a significant educational modality, offering flexible learning opportunities and catering to a diverse student population who may have limited access to traditional formal education systems (Itasanmi, Oni, & Adelore, 2020; Mohamed & Victor, 2012). ODL refers to any learning activities within formal, informal and non-formal domains supported by information and communication Humanities and Social Sciences Letters 2025 Vol. 13, No. 1, pp. 324-343 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v13i1.4108 © 2025 Conscientia Beam. All Rights Reserved. https://orcid.org/0000-0002-2136-583X mailto:itasunny2000@gmail.com mailto:oaajani@gmail.com mailto:katetawo001@gmail.com https://www.doi.org/10.18488/73.v13i1.4108 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 325 © 2025 Conscientia Beam. All Rights Reserved. technologies that aim to bridge physical and psychological distances, enhancing interactivity and communication among learners, learning resources and facilitators (Yetik, Ozdamar, & Bozkurt, 2020). It is more cost-effective and can be pursued while maintaining full-time employment (Magaji & Adelabu, 2012). ODL provides access to programmes that would otherwise be unavailable for those living in remote areas (Mhishi, Bhukuvhani, & Sana, 2012). This mode of education is especially vital in developing countries where access to traditional educational institutions is often limited (Itasanmi et al., 2020; Mhishi et al., 2012). However, the success of ODL students depends on several factors including their time management skills and willingness to seek support when necessary (Ahmad, Mohd Khairi, Hassanuddin, Mamat, & Rosly, 2023). Academic procrastination which has been considered a failure of self-regulation directly impacts these success factors (Ucar, Bozkurt, & Zawackı-rıchter, 2021). Academic procrastination refers to the tendency of students to delay or postpone academic tasks such as studying for an exam, doing homework or writing an essay despite being aware of the need to complete these tasks by a specific deadline (González-Brignardello, Sánchez-Elvira Paniagua, & López-González, 2023). It is particularly characterized by the intentional delay of tasks despite knowing the potential negative consequences and this has become a common issue among students especially ODL students (Ucar et al., 2021). ODL provides an opportunity for students to attend classes and review lessons at their convenience. However, they lack in-person peer contact and a structured environment with a set routine. This can affect students particularly those with present-biased preferences causing them to skip assignments and postpone tasks that require effort (De Paola, Gioia, & Scoppa, 2023). This can significantly impact their academic performance. Understanding the predictors and correlates of academic procrastination in ODL settings becomes essential for institutions to develop effective interventions that will help students overcome procrastination and enhance their educational success. Social support, encompassing emotional, informational and instrumental assistance plays a critical role in students' academic success (Mishra, 2020). Social support refers to the sense of being valued, respected, cared for, and loved by the people in one’s life (Gurung, 2010). This support can come from various sources, including family, friends, teachers, the community or any social groups to which the student belongs. It may take the form of tangible assistance from others or perceived social support which reflects an individual's confidence in the availability of adequate support when needed (Roohafza et al., 2014). Studies have indicated that social isolation can be a significant challenge for ODL students leading to feelings of loneliness, decreased motivation and increased stress (Badruesham, Hasan, Nikman, Ismail, & Muhamad Arib, 2023; Izham, Anuar, & Khairuddin, 2022; Nawi, Yusof, Kamaludin, & Sain, 2021). According to Nair, Bhatia, Kumar AV, Pothakani, and Benedict (2024), students who perceive higher levels of social support often experience lower levels of stress, anxiety and depression. It has been observed that the lack of in-person interaction with peers and teachers within the ODL context can exacerbate the feeling of social isolation making it difficult for students to stay engaged and committed to their studies with a consequential effect on their academic performance (National University, 2021). The importance of social support for ODL students cannot be overemphasized. Social support mitigates the adverse effects of procrastination by providing the necessary encouragement and resources to stay on track contributing to better academic performance and a more positive learning experience for ODL students (Madjid, Sutoyo, & Shodiq, 2021; Yang, Zhu, & Hu, 2023). Several studies have examined procrastination tendencies (Cheng & Xie, 2021; De Paola et al., 2023; Klingsieck, Fries, Horz, & Hofer, 2012; Pulist, 2018; Ucar et al., 2021) perceived social support (Huang et al., 2022; Selvarani, 2011; Sulistyorini & Roswiyani, 2021) and factors associated with academic performance (Dadigamuwa & Senanayake, 2012; Oladejo & Onyeagbako, 2017; Rajadurai, Alias, Jaaffar, & Hanafi, 2018; Venter et al., 2011; Zamri, Omar, Anwar, & Fatzel, 2021) of ODL students worldwide. No study has attempted to explore the relationship between academic procrastination, social support and academic performance among ODL students especially in a developing country like Nigeria. Additionally, it is believed that demographic characteristics such as age, gender, employment status and programme level may influence ODL students’ procrastination behaviours, Humanities and Social Sciences Letters, 2025, 13(1): 324-343 326 © 2025 Conscientia Beam. All Rights Reserved. the availability or perception of social support and academic performance. Thus, this study explores the intricate relationships between demographic characteristics, academic procrastination, social support, and academic performance among ODL students. This research seeks to identify key predictors and provide insights into how these factors collectively influence the academic outcomes of ODL students using random forest analysis, a robust machine learning technique capable of handling complex interactions and non-linear relationships (Vigneau, Courcoux, Symoneaux, Guérin, & Villière, 2018). This is expected to help not only by contributing to the existing body of knowledge but also by addressing the unique challenges faced by ODL students in developing countries. The findings of the study inform policy and practice, offering guidance on how to enhance academic support structures and improve student performance in ODL environments. 2. LITERATURE REVIEW 2.1. Social Support and Academic Procrastination The impact of social support on academic procrastination has continued to be a subject of considerable research interest among scholars, practitioners and educationists with findings consistently highlighting the significant role that social support plays in mitigating procrastination tendencies among students. Studies by Al-Rosyid (2018), Sari and Fakhruddiana (2019); Maulidia and Usman (2019); Madjid et al. (2021) and Li (2023) have shown that social support is a valuable resource for students struggling with procrastination as it equips them with coping mechanisms to manage academic workload and resist procrastination. For instance, Madjid et al. (2021) conducted a study examining the effects of social support and resilience on students’ academic procrastination. The study found social support to reduce the level of academic procrastination among the students. The researcher suggested that fostering social support among the students buffers against academic stress and challenges that often contribute to procrastination behaviours. Similarly, Li (2023) explores the relationship between social support and academic procrastination among high school students. His findings revealed a significant negative relationship between perceived social support and procrastination tendencies. The researcher suggests a need for students to recognize the importance of a social support system in helping them adjust their self-perceptions to fully engage in the learning process, thereby reducing the inclination to procrastinate. Moreover, Al-Rosyid (2018) explored the effect of self-efficacy and perceived social support on the academic procrastination of students. He found that students’ academic procrastination reduces when perceived social support increases. The researcher suggested that students’ family members, friends and relatives should endeavor to provide support and motivation that will allow them to stay on track with their academic responsibilities and prevent academic procrastination. 2.2. Social Support and Academic Performance Social support holds significant importance in students' lives and serves as a cornerstone for academic success. Scholars have posited that social support plays a pivotal role in promoting students’ learning and perseverance thereby impacting their academic performance (Dupont, Galand, & Nils, 2015). Extensive existing research on the effect of social support on academic performance has shown the significant and positive impact that social support has on students' academic performance. For example, a study by Apeh and Nteh (2020) investigated how social support affects high school students' academic achievement. The researcher observed that social support had a significant impact on the academic performance of students. They proposed encouraging parents, teachers, classmates, and friends to maintain and enhance their support for students through activities such as seminars, workshops and the distribution of educational materials to improve students' academic performance. Similarly, Yasin and Dzulkifli (2011) explored the relationship between social support and academic performance among undergraduate students. The study found a significant and positive relationship between social support and the academic performance of the students. Therefore, they emphasized the need for teachers to realize that students' Humanities and Social Sciences Letters, 2025, 13(1): 324-343 327 © 2025 Conscientia Beam. All Rights Reserved. academic performance is influenced not only by academic factors but also by the social support they receive from people around them. Furthermore, research by Saeed, Ahmed, Rahman, and Sleman (2023) investigated the relationship between social support and academic achievement with an exploration of the mediating role of self- esteem in the relationship. The research findings revealed that social support positively impacts academic performance by enhancing self-esteem. They advocated concerted efforts by stakeholders in the education sector to increase perceived social support from family and friends during learning activities in pursuit of students’ academic success. Moreover, a qualitative study conducted by Kunateh Abubakar (2021) established the relationship between social support and the academic performance of students in a developing country and revealed that social support enhances students' mental health which boosts their cognitive engagement in learning activities, thus influencing their academic success. Therefore, he suggested that education stakeholders such as teachers, policymakers, parents and the government should consider providing adequate psychosocial needs that could enhance students' academic performance. 2.3. Academic Procrastination and Academic Performance Procrastination and its effects on academic outcomes have become an interesting research subject worldwide as the prevalence of academic procrastination among students continues to grow (Kim & Seo, 2015). According to O’Brien (2000) and Özer, Demir, and Ferrari (2009) over 80% and at least half of all students engage in procrastination particularly when it comes to completing assignments and studying for examinations (Ahmed, Bernhardt, & Shivappa, 2023). A significant number of empirical studies exist examining the relationship between procrastination and academic performance. However, the results have largely been inconsistent. Some studies including Rotenstein, Davis, and Tatum (2009); Jiao, DaRos-Voseles, Collins, and Onwuegbuzie (2011); Lakshminarayan, Potdar, and Reddy (2013); Kim and Seo (2015); Goroshit (2018) and Jones and Blankenship (2021) found a negative impact of procrastination on students' academic performance. Some writers (Calonia, Doller, March, Velle, & Mojado, 2023; Janssen, 2015) found no significant relationship. Yet, a study by Schraw, Wadkins, and Olafson (2007) reported a positive impact of academic procrastination on academic performance. Generally, the inconsistencies in the findings related to the relationship between procrastination and academic performance remain unclear, thereby making it difficult to have a unified conclusion on the subject matter. 2.4. Demographic Characteristics, Academic Procrastination, Social Support and Academic Performance The demographic dynamics in students' academic procrastination, social support and academic performance have been widely studied and results have often indicated a complex interaction that significantly affects educational outcomes. Research has consistently shown that demographic factors such as age, gender and marital status can influence academic procrastination tendencies, the availability and effectiveness of social support and ultimately, academic performance (He, 2017; Khan, Arif, Noor, & Muneer, 2014; Prezza & Giuseppina Pacilli, 2002). However, the results of these studies have shown a mixed picture of the influence of demographic characteristics on academic procrastination, social support and academic performance. For example, Khan et al. (2014) and He (2017) found a significant difference in academic procrastination among students based on age and gender. Tavakoli (2013) found no age and gender difference in academic procrastination among students. Similarly, Vaux (1985) and Prezza and Giuseppina Pacilli (2002) revealed in their studies that the availability and effectiveness of perceived social support vary based on age, gender and marital status. Their study suggests that young adults, female and married tend to receive more social support than older, male and single people. Furthermore, Tabassum and Akhter (2020) found a significant difference in students’ academic performance based on gender and employment status. Adaeze and Chukwu (2022) found no significant gender difference in students’ academic performance. Humanities and Social Sciences Letters, 2025, 13(1): 324-343 328 © 2025 Conscientia Beam. All Rights Reserved. 2.5. Research Gap Extensive research has been conducted on the interrelationships among demographic characteristics, academic procrastination, social support and academic performance. However, the majority of the existing studies largely focused on traditional, on-campus students with limited attention to ODL students. ODL students often feel isolated as a result of a lack of direct peer and teacher support and usually face difficulties in self-regulation and time management (Kirmizi, 2013). Therefore, research is needed to understand the interrelationships within the ODL context especially in a developing country like Nigeria. Moreover, there is a dearth of studies on how demographic characteristics such as age, gender, marital status, employment status and programme level influence academic procrastination, social support and academic performance among ODL students. Lastly, the existing studies are majorly correlational and qualitative while there is a gap in employing advanced quantitative methods like random forest analysis that could provide a better understanding of the complex interactions among the study's constructs. Therefore, this study presents an opportunity to address these gaps and contribute not only to the theoretical understanding of the study's constructs in an ODL context but also to practically support ODL institutions and policymakers in enhancing academic success through targeted support systems and intervention strategies for ODL students in the country. 3. METHODOLOGY 3.1. Research Design This study adopted a quantitative research approach to explore the relationship between demographic characteristics, academic procrastination and social support among ODL students in Nigeria. This approach is considered appropriate because it allows the researcher to collect large data that can be quantitatively analyzed. This will help to identify trends and levels of association among the study’s variables. Table 1. Demographic distribution of participants. Variables Freq. Percentage Age 16-20 113 35.9 21-25 55 17.5 26-30 40 12.7 31-35 33 10.5 36-40 33 10.5 41-45 21 6.7 46-50 13 4.1 51-55 4 1.3 56-60 2 0.6 61 % above 1 0.3 Gender Male 148 47 Female 167 53 Marital status Single 208 66 Married 101 32.1 Divorced/ Widowed 6 1.9 Employment status Employed 135 42.9 Self-employed 111 35.2 Unemployed 69 21.9 Programme level 100 88 27.9 200 88 27.9 300 37 11.7 400 40 12.7 500 62 19.7 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 329 © 2025 Conscientia Beam. All Rights Reserved. 3.2. Research Population The population of this study consists of ODL students in Nigerian universities. However, ODL students in the University of Ibadan Distance Learning Centre are the target population for this study. 315 ODL students from the Distance Learning Centre, University of Ibadan participated in the study. A demographic analysis of the participants’ biodata indicates that over one-quarter (35.9%) are in the age range of 16-20. The majority (53%) are female. Similarly, 66% of the ODL students are single and about half (42.9%) are employed. Similarly, 100- and 200- level students constituted the majority of the participants with 55.8% (27.9% respectively for 100 and 200). Table 1 presents the demographic distribution of the participants. 3.3. Instrument The academic procrastination of the students was measured using adapted items from the Turkman Procrastination Scale (Tuckman, 1991) while the social support domain was measured using the Multidimensional Scale of Perceived Social Support developed by Zimet, Dahlem, Zimet, and Farley (1988). The two scales were anchored on a five-point Likert-type scale (strongly disagree=1, strongly agree=5). The current study calculated Cronbach's alpha reliability coefficient for the procrastination and social support scales to be 69 and 72 respectively which were considered acceptable. Students were categorized as low (<50) and high (>50) based on their total score on the scales. Regarding academic performance, a self-reported performance scale (first class, second class upper, second class lower, third class and pass) was presented to the student and they were asked to indicate their current result category (see Appendix 1 for the research instrument). 3.4. Research Procedure The data for this study was collected through an online survey administered in the 2021-2022 academic session. The researcher got the approval of the management of the Distance Learning Centre of the University and an invitation email highlighting the objectives of the study. The link to participate in the study was sent to all registered students. Participants were informed that participation in the survey was voluntary and assurance was given as regards the confidentiality of the information they provided. The data collection period spans from December 1, 2022 to February 31st, 2023. 3.5. Method of Data Analysis This study utilized SPSS and R software to analyse the data collected for the study. Specifically, SPSS was used for data coding and chi-square analysis for the classification of relationships among demographic characteristics, academic procrastination, social support and academic performance. R-studio was applied to fit a random forest model that classified and predicted academic performance with the demographic characteristics, academic procrastination and social support in the model. The random forest predictive model adopted for this study was specified as follows: Academic performance (AP) = f (demographic characteristics, academic procrastination and social support). AP = f (xi) where xi are the predictor variables such as demographic characteristics, academic procrastination, and social support while AP is the academic performance which is the dependent or outcome variable. Age of the participants were categorized as young adult (<=30), middle-aged adult (31-50) and old adult (>=51). 4. RESULT Table 2 shows that the predicted academic performance category of the students under study is classified correctly with a prediction accuracy of 92.51% and an error rate of less than 7% which indicates that the specified random forest model is adequate for the data. Humanities and Social Sciences Letters, 2025, 13(1): 324-343 330 © 2025 Conscientia Beam. All Rights Reserved. Table 2. Model confusion matrix. Academic performance First class Pass Second class (Lower division) Second class (Upper division) Third class Class error First class 65 0 0 0 0 0.00 Second class (Lower division) 5 0 3 0 0 1.00 Second class (Upper division) 0 0 40 0 0 0.00 Third class 0 0 0 106 0 0.00 Pass 0 0 0 6 2 0.75 Figure 1 shows the visualization of the variables in the random forest model. It was revealed in the figure that low procrastination contributes to higher academic performance. Moreover, a high social support level contributes to higher academic performance. The demographic variables' contribution pattern was also indicated in the model variable graph. Figure 1. Visualizing the importance of variables to the model. Figure 2 is the random forest model plot demonstrating the fact that the model has a minimal error rate that makes it reliable for data classification and prediction. Figure 2. Model plot. Note: Type of random forest: classification; number of trees: 500; no. of variables tried at each split: 3; OOB estimate of error rate: 6.17%; accuracy: 0.9251. Humanities and Social Sciences Letters, 2025, 13(1): 324-343 331 © 2025 Conscientia Beam. All Rights Reserved. Tables 3 and 4 show the train and test variable classifications respectively with the train variable higher than the test variable which implies adequacy as the train data is expected to be greater than the test data. Meanwhile, the p-value<0.05 for both train and test variables suggests that the random forest is statistically significant and this suggests a significant relationship between academic performance, academic procrastination and demographic variables. Table 3. Prediction by train variable. Academic performance First class Pass Second class (Lower division) Second class (Upper division) Third class First class 65 0 0 0 0 Pass 0 8 0 0 0 Second class (Lower division) 0 0 40 0 0 Second class (Upper division) 0 0 0 106 0 Third class 0 0 0 0 8 Note: Accuracy: 1; 95% CI: (0.9839, 1); No information rate: 0.467; P-value [Acc > NIR]: < 2.2e-16; Kappa: 1. Table 4. Prediction by test variable. Academic performance First class Pass Second class (Lower division) Second class (Upper division) Third class First class 30 3 0 0 0 Pass 0 1 0 0 0 Second class (Division) 0 0 15 0 0 Second class (Upper division) 0 0 0 32 3 Third class 0 0 0 0 4 Note: Accuracy: 0.9318; 95% CI: (0.8575, 0.9746); No information rate: 0.3636; P-value [Acc > NIR]: < 2.2e-16; Kappa: 0.9018. Humanities and Social Sciences Letters, 2025, 13(1): 324-343 332 © 2025 Conscientia Beam. All Rights Reserved. Table 5. Demographic factors and academic performance of ODL students. Variables Academic performance Chi-square value P value First class Second class (Upper division) Second class (Lower division) Third class Pass Age <=30 57 61 17 1 7 26.001 0.011 31-50 28 57 29 11 4 >=51 10 20 9 3 1 Gender Male 36 61 36 10 5 13.581 0.009 Female 59 77 19 5 7 Marital status Single 73 93 27 6 9 18.556 0.017 Married 21 42 27 8 3 Divorced / separated/ widowed 1 3 1 1 0 Programme level 100L 52 25 5 1 5 81.532 0 200L 28 41 15 3 1 300L 6 21 8 1 1 400L 2 26 8 2 2 500L 7 25 19 8 3 Employment status Employed 37 57 29 9 3 11.807 0.16 Unemployed 24 24 14 3 4 Self-Employed 34 57 12 3 5 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 333 © 2025 Conscientia Beam. All Rights Reserved. Table 5 revealed that ODL students’ academic performance is significantly associated with age group (young adults (<=30), middle-aged adults (31-50) and old adults (>=51) (P = 0.011<0.05 gender (P= 0.009<0.05), marital status (P= 0.017<0.05) and programme level (P =0<0.05). However, Table 5 shows that the academic performance of the ODL students is not significantly associated with employment status (P= 0.16>0.05). Table 6 shows that ODL students’ academic procrastination is significantly associated with age (P = 0.007<0.05), marital status (P= 0.003<0.05) and employment status (P =0.024<0.05). However, their academic procrastination is not significantly associated with gender (P =0.283>0.05), and programme level (P =0.545>0.05). Table 6. Demographic factors and academic procrastination of ODL students. Variables Academic procrastination Chi-square value P value Low High Age 9.874 0.007 <=30 77 66 31-50 93 36 >=51 25 18 Gender 1.153 0.283 Male 87 61 Female 108 59 Marital status 11.543 0.003 Single 115 93 Married 76 25 Divorced / separated/ widowed 4 2 Programme level 3.079 0.545 100L 56 32 200L 49 39 300L 23 14 400L 24 16 500L 43 19 Employment status 7.427 0.024 Employed 89 46 Unemployed 33 36 Self-employed 73 38 Table 7. Demographic factors and social support. Variables Social support Chi-square value P value Low High Age <=30 21 122 2.033 0.362 31-50 19 110 >=51 10 33 Gender Male 28 120 1.940 0.164 Female 22 145 Marital status Single 39 169 18.930 0 Married 7 94 Divorced / separated/ widowed 4 2 Programme level 100l 20 68 7.235 0.124 200l 14 74 300l 7 30 400l 3 37 500l 6 56 Employment status Employed 25 110 2.413 0.299 Unemployed 7 62 Self-employed 18 93 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 334 © 2025 Conscientia Beam. All Rights Reserved. Table 7 revealed that ODL students' social support was significantly influenced by their marital status (P =0<0.05). Singles have higher social support than married and divorced/separated/widowed students. Age (P =0.362>0.05), gender (P =0.164>0.05), programme level (P =0.124>0.05) and employment status (P =0.299>0.05) have no significant influence on the perceived social support of the students. Table 8 shows clearly in the classification that students with low procrastination have higher academic performance with first-class and second-class upper and lower division compared with students with high procrastination. Table 8. Relationship between academic performance and academic procrastination. Variables Academic procrastination Total Low High Academic performance First class 64 31 95 Second class (Upper division) 86 52 138 Second class (Lower division) 32 23 55 Third class 8 7 15 Pass 5 7 12 Total 195 120 315 Chi-square tests Value Df Asymptotic significance (2- sided) _ Pearson chi-square 4.087 4 0.394 _ Table 9 shows that students with high social support have higher academic performance in first class and second class compared with students with lower social support. Table 9. Relationship between academic performance and social support. Variables Social support Total Low High Academic performance First class 14 81 95 Second class (Upper division) 20 118 138 Second class (Lower division) 12 43 55 Third class 2 13 15 Pass 2 10 12 Total 50 265 315 Chi-square tests Value df Asymptotic significance (2-sided) _ Pearson chi-square 1.823 4 0.768 _ 5. DISCUSSION This study explored the relationship between demographic characteristics, academic procrastination, social support, and academic performance among ODL students using a random forest approach. Results of the study revealed that ODL students’ academic performance and academic procrastination were significantly associated with their age. The result indicated that young adults perform better academically. However, they procrastinate more than middle-aged and older adults. This result is consistent with the findings of Pellizzari and Billari (2012) and Khan et al. (2014) but inconsistent with Akkaya (2007) and Dixson, Worrell, Olszewski‐Kubilius, and Subotnik (2016) who found age not to be a contributor to academic procrastination and performance respectively. However, this result corroborates the assertion made by Steel and Ferrari (2013); Svartdal et al. (2016) and He (2017) that as Humanities and Social Sciences Letters, 2025, 13(1): 324-343 335 © 2025 Conscientia Beam. All Rights Reserved. age increases, students’ procrastination tends to decrease. This result could be attributed to differences in cognitive abilities, technological proficiency, involvement in extracurricular activities and family and work commitments. According to the researchers’ perspective, young adults tend to have higher cognitive abilities such as memory and problem-solving skills and are generally more technologically proficient which can enhance their academic performance compared to middle-aged and older adults. Conversely, young adults are more involved in social activities and other non-academic pursuits with low time management skills which can lead to procrastination. Unlike middle-aged and older adult’ students who juggle family and work responsibilities along with academics. This might reduce their time for academic tasks but necessitate better planning and less procrastination. Results also revealed that the academic performance of the ODL students is significantly dependent on their gender as the academic performance of females is higher than that of males. This result is consistent with the findings of similar studies (Khwaileh & Zaza, 2011; Ngozi, 2011; Parajuli & Thapa, 2017). However, the result is in contrast with the research findings of Oladejo and Onyeagbako (2017) and Adekunle, Seyi, and Falilat (2020) who found no significant gender difference in academic performance among ODL students in Nigeria. The study indicated a significant influence of marital status on ODL students’ academic performance, academic procrastination, and social support. The findings regarding academic performance aligned with previous research by Nasu (2018); Aboderin and Govender (2019) and Aboderin and Govender (2023) suggesting a relationship between marital status and academic performance among ODL students. Similarly, the relationship between marital status and social support corroborated prior work by Prezza and Giuseppina Pacilli (2002). However, the study's finding that marital status influences procrastination tendencies among ODL students contradicts existing research by Asgari, Bolbolian, Sefidi, and Zadeh (2021) and Lu, He, and Tan (2022) which found no significant impact of marital status on student procrastination. Specifically, the study revealed that single students perform better academically and procrastinate more than married and divorced students while having higher social support. The researchers attribute these results to several factors. Single students might have more time to dedicate to their studies leading to better academic performance. However, they also exhibit higher levels of procrastination, potentially due to a lack of the structure and accountability that marriage or cohabitation can provide. Additionally, according to Kim and Seo (2015) single students may have larger social support networks compared to their married or divorced peers. On the other hand, married or divorced ODL students may face competing priorities due to family commitments or the stress of divorce which can impact their academic focus. Despite these challenges, a stronger sense of urgency to succeed (driven by career advancement or personal fulfilment) might lead the married to procrastinate less than single students (Krause & Freund, 2014). However, their social support networks might be smaller compared to those of single students. Furthermore, the academic performance of students significantly depends on their programme level with the chances of obtaining a first-class grade being higher at the 100 level compared to other levels. This finding suggests that students entering ODL programmes often exhibit high levels of enthusiasm and motivation. Prior knowledge helps students revisit concepts and knowledge areas from their previous education, potentially enhancing their academic performance and increasing their chances of achieving a first-class grade at the 100 level. However, as students’ progress in the programme, the coursework becomes more complex and demanding. Advanced levels involve a cumulative increase in academic workload including more challenging courses, project work, and research requirements. This heightened difficulty can make it harder for ODL students to balance their responsibilities potentially impacting their academic performance. Academic procrastination depends on students' employment status. Employed students showed both the lowest and highest levels of procrastination compared to unemployed and self-employed students, reflecting the diverse challenges they face in the ODL context. Those in flexible and supportive work environments manage academic responsibilities effectively leading to lower procrastination. In contrast, students in demanding and inflexible jobs struggle more resulting in higher procrastination. Self-employed students may experience disruptions due to the Humanities and Social Sciences Letters, 2025, 13(1): 324-343 336 © 2025 Conscientia Beam. All Rights Reserved. unpredictable nature of their work while having greater control over their schedules. Unemployed students may face financial stress and uncertainty that affects their motivation and increases procrastination despite having more time to study. Thus, employment status significantly influences academic procrastination among ODL students shaped by the specific demands and flexibility of their work conditions. Lastly, the study indicates that students with low procrastination and higher social support achieve higher academic performance with more students obtaining first-class and second-class upper and lower-division grades compared to those with high procrastination and low social support. This result aligns with previous studies (Achdiyah, Latipun, & Yuniardi, 2023; Akinsola, Tella, & Tella, 2007; Cerezo, Esteban, Sánchez-Santillán, & Núñez, 2017; Kim & Seo, 2015; Lakshminarayan et al., 2013; Mishra, 2020; Song, Bong, Lee, & Kim, 2015; Tinajero, Martínez-López, Rodríguez, & Páramo, 2020; Ucar et al., 2021). According to researchers’ perspectives, students who exhibit low levels of procrastination are typically good at time management, setting priorities, and adhering to study schedules. This disciplined approach enables them to complete assignments on time, prepare thoroughly for exams, and engage deeply with their coursework, leading to higher academic performance (Hailikari, Katajavuori, & Asikainen, 2021). Similarly, students with robust social support systems receive emotional encouragement, practical help and academic assistance from family and friends. This high level of support can mitigate the stress of academic challenges, providing a buffer that enhances their ability to cope and achieve academic success (Mishra, 2020). Conversely, students who procrastinate heavily are more likely to experience stress, incomplete assignments and poor preparation for exams leading to poor performance (Gadosey, Schnettler, Scheunemann, Fries, & Grunschel, 2021). Additionally, students who lack a strong support network may feel isolated and miss the emotional and practical assistance needed to navigate academic challenges. The absence of encouragement and accountability can decrease motivation negatively impacting their performance (Stadtfeld, Vörös, Elmer, Boda, & Raabe, 2019). 6. CONCLUSION The current study offers significant insight into the relationship between demographic characteristics, academic procrastination, social support, and academic performance among ODL students in Nigeria. The results of the study highlight the complex interplay among the study variables emphasizing that demographic factors (age, gender, marital status, programme level and employment status) are critical factors influencing students’ learning outcomes in the ODL context. Specifically, this study underscored the need for ODL institutions to implement demographic-specific support programmes that will focus on procrastination reduction, time management skills, career counselling, family support services, flexible scheduling, and robust social support networks. This can help enhance academic performance and address the diverse challenges faced by ODL students. 6.1. Suggestion The following are suggested based on the results of the study: 1. ODL institutions should implement support programs tailored to the needs of different age groups. Programmes for young students should focus on procrastination reduction and time management skills, while older students should receive career counselling and support for balancing academic and personal responsibilities. 2. ODL institutions should develop and implement initiatives such as gender-specific advising and inclusive practices to address the needs of both male and female students. 3. Stakeholders should introduce flexible scheduling, childcare support, family counselling services, and online support networks to cater to the unique needs of students based on their marital status. 4. ODL institutions should ensure a gradual increase in coursework complexity and provide continuous support to help students transition smoothly through different program levels. Humanities and Social Sciences Letters, 2025, 13(1): 324-343 337 © 2025 Conscientia Beam. All Rights Reserved. 6.2. Limitations and Suggestions for Further Studies This study involved 315 ODL students from the Distance Learning Centre, University of Ibadan. While the study’s findings offer valuable insights, they may not be easily generalizable to other ODL institutions in Nigeria or other developing countries due to variations in institutional structures and student demographics. Expanding the study to include multiple ODL institutions across different regions of Nigeria and other developing countries would enhance the generalizability of the findings and allow for comparative analyses. This study employed a cross- sectional design capturing data at a single point in time and relying on self-reported measures for academic performance and procrastination. These self-reported measures may be subject to social desirability bias or inaccuracies in self-assessment. Future research should adopt a longitudinal design to track changes in academic procrastination, social support, and academic performance over time. This approach would allow for a better understanding of causality and the dynamics of these relationships. Additionally, the current study utilized only a quantitative approach. Future studies should complement quantitative data with qualitative methods such as interviews or focus groups. This mixed-method approach would provide richer, contextual insights into the experiences of ODL students particularly regarding their struggles with procrastination and the types of social support they find most beneficial. Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the Department of Adult Education, University of Ibadan, Nigeria has granted approval for this study on 12 July 2024 (Ref. No. 2024/RB/016). Transparency: The authors declare that the manuscript is honest, truthful and transparent, that no important aspects of the study have been omitted and that all deviations from the planned study have been made clear. This study followed all rules of writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: All authors contributed equally to the conception and design of the study. All authors have read and agreed to the published version of the manuscript. REFERENCES Aboderin, O. S., & Govender, D. W. (2019). A critical analysis of factors influencing academic performance of distance E-learners in a Nigerian University. PONTE International Journal of Science and Research, 75(9), 67–83. Aboderin, O. S., & Govender, D. W. (2023). Predictors of academic performance of distance e-learners in a Nigerian University. International Journal of Research in Business and Social Science (2147-4478), 12(1), 296-307. https://doi.org/10.20525/ijrbs.v12i1.2296 Achdiyah, H. N., Latipun, L., & Yuniardi, M. S. (2023). The influence of social support on academic performance: The mediating role of cognitive engagement. Jurnal Ilmiah Psikologi Terapan, 11(2), 85-90. Adaeze, R.-O., & Chukwu, K. U. (2022). Gender differences and the writing achievement of university fresh students: A study of federal university of technology Owerri. Journal of Gender, Culture and Society, 2(1), 11–16. https://doi.org/10.32996/jgcs.2022.2.1.2 Adekunle, O. O., Seyi, O. B., & Falilat, A. J. (2020). Demographic factors and status as predictors of open and distance learning students academic performance in computer science. Communication in Physical Sciences, 6(2), 915-926. Ahmad, N., Mohd Khairi, N. H., Hassanuddin, N. A., Mamat, S. S., & Rosly, N. S. (2023). Student’s perceptions of the effectiveness on time management skills in assisting their online distance learning. Jurnal Intelek, 18(2), 227-232. https://doi.org/10.24191/ji.v18i2.22383 Ahmed, I., Bernhardt, G. V., & Shivappa, P. (2023). Prevalence of academic procrastination and its negative impact on students. Biomedical and Biotechnology Research Journal, 7(3), 363-370. https://doi.org/10.4103/bbrj.bbrj_64_23 Akinsola, M. K., Tella, A., & Tella, A. (2007). Correlates of academic procrastination and mathematics achievement of university undergraduate students. Eurasia Journal of Mathematics, Science and Technology Education, 3(4), 363-370. https://doi.org/10.12973/ejmste/75415 https://doi.org/10.20525/ijrbs.v12i1.2296 https://doi.org/10.32996/jgcs.2022.2.1.2 https://doi.org/10.24191/ji.v18i2.22383 https://doi.org/10.4103/bbrj.bbrj_64_23 https://doi.org/10.12973/ejmste/75415 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 338 © 2025 Conscientia Beam. All Rights Reserved. Akkaya, E. (2007). Academic procrastination among faculty of education students: The role of gender, age, academic achievement, perfectionism and depression. Master's Thesis, Middle East Technical University. Al-Rosyid, M. H. (2018). Self efficacy, perceived social support, and academic procrastination on final semester students of university x. International Journal of Research Publications, 15(1), 9–9. Apeh, H., & Nteh, K. (2020). Impact of social support on students’academic achievement in senior secondary schools in the federal capital territory, Abuja Nigeria. Ilorin Journal of Education, 40(2), 60-70. Asgari, S., Bolbolian, M., Sefidi, F., & Zadeh, A. (2021). The relationship between test anxiety and academic procrastination among the dental students. Journal of Education and Health Promotion, 10(1), 67. Badruesham, N., Hasan, H., Nikman, K., Ismail, M. I., & Muhamad Arib, N. (2023). An analysis of recommendations on psychological well-being during open and distance learning (ODL): Students’ viewpoints. Jurnal Intelek, 18(2), 128-138. https://doi.org/10.24191/ji.v18i2.22265 Calonia, J. T., Doller, D. H., March, P. M., Velle, L., Jean, L., & Mojado, G. S. (2023). Investigating academic procrastination and its implication for academic achievement in an online learning setting. Zenodo CERN European Organization for Nuclear Research, 8(7), 1-7. https://doi.org/10.5281/zenodo.8181368 Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating behavior in computer-based learning environments to predict performance: A case study in Moodle. Frontiers in Psychology, 8, 1403. https://doi.org/10.3389/fpsyg.2017.01403 Cheng, S.-L., & Xie, K. (2021). Why college students procrastinate in online courses: A self-regulated learning perspective. The Internet and Higher Education, 50, 100807. https://doi.org/10.1016/j.iheduc.2021.100807 Dadigamuwa, P. R., & Senanayake, S. (2012). Motivating factors that affect enrolment and student performance in an ODL engineering program. International Review of Research in Open and Distributed Learning, 13(1), 238-249. https://doi.org/10.19173/irrodl.v13i1.1034 De Paola, M., Gioia, F., & Scoppa, V. (2023). Online teaching, procrastination and student achievement. Economics of Education Review, 94, 102378. https://doi.org/10.1016/j.econedurev.2023.102378 Dixson, D. D., Worrell, F. C., Olszewski‐Kubilius, P., & Subotnik, R. F. (2016). Beyond perceived ability: The contribution of psychosocial factors to academic performance. Annals of the New York Academy of Sciences, 1377(1), 67-77. https://doi.org/10.1111/nyas.13210 Dupont, S., Galand, B., & Nils, F. (2015). The impact of different sources of social support on academic performance: Intervening factors and mediated pathways in the case of master's thesis. European Review of Applied Psychology, 65(5), 227-237. Gadosey, C. K., Schnettler, T., Scheunemann, A., Fries, S., & Grunschel, C. (2021). The intraindividual co-occurrence of anxiety and hope in procrastination episodes during exam preparations: An experience sampling study. Learning and Individual Differences, 88, 102013. https://doi.org/10.1016/j.lindif.2021.102013 González-Brignardello, M. P., Sánchez-Elvira Paniagua, A., & López-González, M. Á. (2023). Academic procrastination in children and adolescents: A scoping review. Children, 10(6), 1016. https://doi.org/10.3390/children10061016 Goroshit, M. (2018). Academic procrastination and academic performance: An initial basis for intervention. Journal of Prevention & Intervention in the Community, 46(2), 131-142. Gurung, R. A. R. (2010). Health psychology: A cultural approach. Belmont, Ca: Wadsworth Publishing. Hailikari, T., Katajavuori, N., & Asikainen, H. (2021). Understanding procrastination: A case of a study skills course. Social Psychology of Education, 24(2), 589-606. https://doi.org/10.1007/s11218-021-09621-2 He, S. (2017). A multivariate investigation into academic procrastination of university students. Open Journal of Social Sciences, 5(10), 12-24. https://doi.org/10.4236/jss.2017.510002 Huang, X., Deng, Y., Ge, P., Sun, X., Huang, M., Chen, H., . . . Wu, Y. (2022). College students’ degree of support for online learning during the covid-19 pandemic and associated factors: A cross-sectional study. International Journal of Environmental Research and Public Health, 19(24), 16814. https://doi.org/10.3390/ijerph192416814 https://doi.org/10.24191/ji.v18i2.22265 https://doi.org/10.5281/zenodo.8181368 https://doi.org/10.3389/fpsyg.2017.01403 https://doi.org/10.1016/j.iheduc.2021.100807 https://doi.org/10.19173/irrodl.v13i1.1034 https://doi.org/10.1016/j.econedurev.2023.102378 https://doi.org/10.1111/nyas.13210 https://doi.org/10.1016/j.lindif.2021.102013 https://doi.org/10.3390/children10061016 https://doi.org/10.1007/s11218-021-09621-2 https://doi.org/10.4236/jss.2017.510002 https://doi.org/10.3390/ijerph192416814 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 339 © 2025 Conscientia Beam. All Rights Reserved. Itasanmi, S. A., Oni, M. T., & Adelore, O. O. (2020). Students’ assessment of open distance learning programmes and services in Nigeria: A comparative description of three selected distance learning institutions. International Journal of Recent Educational Research, 1(3), 191–208. https://doi.org/10.46245/ijorer.v1i3.64 Izham, M. A. A. N. I., Anuar, N., & Khairuddin, I. E. (2022). Factors that contribute to students' attrition in open and distance learning (ODL) environment: A systematic review. Environment-Behaviour Proceedings Journal, 7(SI10), 103-109. https://doi.org/10.21834/ebpj.v7isi10.4136 Janssen, J. (2015). Academic procrastination: Prevalence among high school and undergraduate students and relationship to academic achievement. Jiao, Q. G., DaRos-Voseles, D. A., Collins, K., & Onwuegbuzie, A. J. (2011). Academic procrastination on the performance of graduate-level cooperative groups in research methods courses. Journal of the Scholarship of Teaching and Learning, 11(1), 119–138. Jones, I. S., & Blankenship, D. C. (2021). Year two: Effect of procrastination on academic performance of undergraduate online students. Research in Higher Education Journal, 39(39), 1–11. Khan, M. J., Arif, H., Noor, S. S., & Muneer, S. (2014). Academic procrastination among male and female university and college students. FWU Journal of Social Sciences, 8(2), 65-70. Khwaileh, F. M., & Zaza, H. I. (2011). Gender differences in academic performance among undergraduates at the University of Jordan: Are they real or stereotyping. College Student Journal, 45(3), 633-648. Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences, 82, 26-33. https://doi.org/10.1016/j.paid.2015.02.038 Kirmizi, O. (2013). Investigating self-regulated learning habits of distance education students. Journal of History Culture and Art Research, 2(2), 161-174. Klingsieck, K. B., Fries, S., Horz, C., & Hofer, M. (2012). Procrastination in a distance university setting. Distance Education, 33(3), 295-310. https://doi.org/10.1080/01587919.2012.723165 Krause, K., & Freund, A. M. (2014). How to beat procrastination. European Psychologist, 19(2), 132–144. https://doi.org/10.1027/1016-9040/a000153 Kunateh Abubakar, S. (2021). Social support and academic achievement: Exploring the link in a developing country. Психологични изследвания, 24(3), 335-346. Lakshminarayan, N., Potdar, S., & Reddy, S. G. (2013). Relationship between procrastination and academic performance among a group of undergraduate dental students in India. Journal of Dental Education, 77(4), 524-528. Li, M. (2023). A study on the relationship between social support and academic procrastination among junior high school students. The Educational Review, USA, 7(8), 1178-1183. https://doi.org/10.26855/er.2023.08.025 Lu, D., He, Y., & Tan, Y. (2022). Gender, socioeconomic status, cultural differences, education, family size and procrastination: A sociodemographic meta-analysis. Frontiers in Psychology, 12, 719425. https://doi.org/10.3389/fpsyg.2021.719425 Madjid, A., Sutoyo, D. A., & Shodiq, S. F. (2021). Academic procrastination among students: The influence of social support and resilience mediated by religious character. Jurnal Cakrawala Pendidikan, 40(1), 56-69. Magaji, S., & Adelabu, J. (2012). Cost-benefit of e-learning under ODL of developing economies. Huria: Journal of the Open University of Tanzania, 13(2), 107-122. Maulidia, J., & Usman, O. (2019). Effect of self-efficacy, learning motivation, fear of failure and parent’s social support towards academic procrastination. Learning Motivation, Fear of Failure and Parent’s Social Support Towards Academic Procrastination (December 27, 2019). Mhishi, M., Bhukuvhani, C. E., & Sana, A. F. (2012). Science teacher training programme in rural schools: An ODL lesson from Zimbabwe. International Review of Research in Open and Distributed Learning, 13(1), 72-86. https://doi.org/10.19173/irrodl.v13i1.1058 https://doi.org/10.46245/ijorer.v1i3.64 https://doi.org/10.21834/ebpj.v7isi10.4136 https://doi.org/10.1016/j.paid.2015.02.038 https://doi.org/10.1080/01587919.2012.723165 https://doi.org/10.1027/1016-9040/a000153 https://doi.org/10.26855/er.2023.08.025 https://doi.org/10.3389/fpsyg.2021.719425 https://doi.org/10.19173/irrodl.v13i1.1058 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 340 © 2025 Conscientia Beam. All Rights Reserved. Mishra, S. (2020). Social networks, social capital, social support and academic success in higher education: A systematic review with a special focus on ‘underrepresented’students. Educational Research Review, 29, 100307. https://doi.org/10.1016/j.edurev.2019.100307 Mohamed, A. A., & Victor, M. A. M. (2012). The role of open and distance learning in promoting professional training and development in Tanzania. A case study of the open University Tanzania. Huria: Journal of the Open University of Tanzania, 13(2), 395–400. Nair, A. A., Bhatia, A. K., Kumar AV, D., Pothakani, M. Y., & Benedict, S. M. (2024). Exploring the perceived social support among college students. Mohamed Yousuf and Benedict, Sunil Maria, Exploring the Perceived Social Support Among College Students.(January 15, 2024). Nasu, N. V. H. (2018). Accounting students academic performance in distance education in Brazil: A study focused on sociodemographic factors. Paper presented at the CONTECSI - International Conference on Information Systems and Technology Management. National University. (2021). Challenges of distance learning for students national university. Retrieved from https://www.nu.edu/blog/challenges-of-distance-learning-for-students/ Nawi, S. M., Yusof, S. M., Kamaludin, P. N. H., & Sain, N. (2021). Exploring Malaysian tertiary students’ behavioural, cognitive, emotional and social engagement and disengagement in ODL. International Journal of Academic Research in Business and Social Sciences, 11(4). 962–98. Ngozi, M. B. (2011). Relationship between gender and university students' academic performance in arts-related subjects. Gender and Behaviour, 9(1), 3701-3709. https://doi.org/10.4314/gab.v9i1.67468 O’Brien, W. K. (2000). Applying the transtheoretical model to academic procrastination. University of Houston. Retrieved from https://www.proquest.com/openview/764639c7251897b4a68591334465b99b/1?pq- origsite=gscholar&cbl=18750&diss=y Oladejo, M. A., & Onyeagbako, S. O. (2017). Demographic differentials and distance learners’ academic performance at National Open University of Nigeria. Journal of Educational Foundations, 7, 45-53. Özer, B. U., Demir, A., & Ferrari, J. R. (2009). Exploring academic procrastination among Turkish students: Possible gender differences in prevalence and reasons. The Journal of Social Psychology, 149(2), 241–257. Parajuli, M., & Thapa, A. (2017). Gender differences in the academic performance of students. Journal of Development and Social Engineering, 3(1), 39-47. Pellizzari, M., & Billari, F. C. (2012). The younger, the better? Age-related differences in academic performance at university. Journal of Population Economics, 25, 697-739. https://doi.org/10.1007/s00148-011-0379-3 Prezza, M., & Giuseppina Pacilli, M. (2002). Perceived social support from significant others, family and friends and several socio‐demographic characteristics. Journal of Community & Applied Social Psychology, 12(6), 422-429. https://doi.org/10.1002/casp.696 Pulist, S. (2018). What makes the distance learners succeed: A study of procrastination, motivation, issues and challenges of achievers of ODL system in India. Universal Review, 7, 167-179. Rajadurai, J., Alias, N., Jaaffar, A. H., & Hanafi, W. N. W. (2018). Learners' satisfaction and academic performance in open and distance learning (ODL) universities in Malaysia. Global Business & Management Research, 10(3), 511-524. Roohafza, H. R., Afshar, H., Keshteli, A. H., Mohammadi, N., Feizi, A., Taslimi, M., & Adibi, P. (2014). What's the role of perceived social support and coping styles in depression and anxiety? Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 19(10), 944–949. Rotenstein, A., Davis, H. Z., & Tatum, L. (2009). Early birds versus just-in-timers: The effect of procrastination on academic performance of accounting students. Journal of Accounting Education, 27(4), 223-232. Saeed, K. M., Ahmed, A. S., Rahman, Z. M., & Sleman, N. A. (2023). How social support predicts academic achievement among secondary students with special needs: The mediating role of self-esteem. Middle East Current Psychiatry, 30(1), 46. https://doi.org/10.1186/s43045-023-00316-2 https://doi.org/10.1016/j.edurev.2019.100307 https://www.nu.edu/blog/challenges-of-distance-learning-for-students/ https://doi.org/10.4314/gab.v9i1.67468 https://www.proquest.com/openview/764639c7251897b4a68591334465b99b/1?pq-origsite=gscholar&cbl=18750&diss=y https://www.proquest.com/openview/764639c7251897b4a68591334465b99b/1?pq-origsite=gscholar&cbl=18750&diss=y https://doi.org/10.1007/s00148-011-0379-3 https://doi.org/10.1002/casp.696 https://doi.org/10.1186/s43045-023-00316-2 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 341 © 2025 Conscientia Beam. All Rights Reserved. Sari, W. L., & Fakhruddiana, F. (2019). Internal locus of control, social support and academic procrastination among students in completing the thesis. International Journal of Evaluation and Research in Education, 8(2), 363-368. https://doi.org/10.11591/ijere.v8i2.17043 Schraw, G., Wadkins, T., & Olafson, L. (2007). Doing the things we do: A grounded theory of academic procrastination. Journal of Educational Psychology, 99(1), 12-25. https://doi.org/10.1037/0022-0663.99.1.12 Selvarani, J. (2011). Adult learners in open distance: Stressors, coping strategies, and social support. A study of undergraduate students at the WOU Penang learning centre: Some preliminary findings. Retrieved from https://library.wou.edu.my/vertical/vf2011- 10.pdf Song, J., Bong, M., Lee, K., & Kim, S.-i. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal of Educational Psychology, 107(3), 821. https://doi.org/10.1037/edu0000016 Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z., & Raabe, I. J. (2019). Integration in emerging social networks explains academic failure and success. Proceedings of the National Academy of Sciences, 116(3), 792-797. Steel, P., & Ferrari, J. (2013). Sex, education and procrastination: An epidemiological study of procrastinators’ characteristics from a global sample. European Journal of Personality, 27(1), 51-58. https://doi.org/10.1002/per.1851 Sulistyorini, E., & Roswiyani, R. (2021). Social support and quality of life on online-learning university students. Paper presented at the 1st Tarumanagara International Conference on Medicine and Health (TICMIH 2021). Svartdal, F., Pfuhl, G., Nordby, K., Foschi, G., Klingsieck, K. B., Rozental, A., . . . Rębkowska, K. (2016). On the measurement of procrastination: Comparing two scales in six European countries. Frontiers in Psychology, 7, 1307. https://doi.org/10.3389/fpsyg.2016.01307 Tabassum, R., & Akhter, N. (2020). Effect of demographic factors on academic performance of university students. Journal of Research and Reflections in Education, 14(1), 64–80. Tavakoli, M. A. (2013). A study of the prevalence of academic procrastination among students and its relationship with demographic characteristics, preferences of study time, and purpose of entering university. Educational Psychology, 9(28), 100–122. Tinajero, C., Martínez-López, Z., Rodríguez, M. S., & Páramo, M. F. (2020). Perceived social support as a predictor of academic success in Spanish university students. Annals of Psychology, 36(1), 134-142. Tuckman, B. W. (1991). The development and concurrent validity of the procrastination scale. Educational and Psychological Measurement, 51(2), 473-480. Ucar, H., Bozkurt, A., & Zawackı-rıchter, O. (2021). Academic procrastination and performance in distance education: A causal- comparative study in an online learning environment. Turkish Online Journal of Distance Education, 22(4), 13-23. https://doi.org/10.17718/tojde.1002726 Vaux, A. (1985). Variations in social support associated with gender, ethnicity, and age. Journal of Social Issues, 41(1), 89–110. https://doi.org/10.1111/j.1540-4560.1985.tb01118.x Venter, J., De, C. B., Van, D. M., Swanepoel, A., Doussy, E., & De, H. K. (2011). Increasing throughput: factors affecting the academic performance of entry-level undergraduate taxation students at an ODL institution in South Africa. Progressio, 33(1), 171–188. Vigneau, E., Courcoux, P., Symoneaux, R., Guérin, L., & Villière, A. (2018). Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Quality and Preference, 68, 135-145. https://doi.org/10.1016/j.foodqual.2018.02.008 Yang, X., Zhu, J., & Hu, P. (2023). Perceived social support and procrastination in college students: A sequential mediation model of self-compassion and negative emotions. Current Psychology, 42(7), 5521-5529. https://doi.org/10.1007/s12144-021-01920-3 Yasin, A. S. M., & Dzulkifli, M. A. (2011). The relationship between social support and academic achievement. International Journal of Humanities and Social Science, 1(5), 277-281. https://doi.org/10.11591/ijere.v8i2.17043 https://doi.org/10.1037/0022-0663.99.1.12 https://library.wou.edu.my/vertical/vf2011-10.pdf https://library.wou.edu.my/vertical/vf2011-10.pdf https://doi.org/10.1037/edu0000016 https://doi.org/10.1002/per.1851 https://doi.org/10.3389/fpsyg.2016.01307 https://doi.org/10.17718/tojde.1002726 https://doi.org/10.1111/j.1540-4560.1985.tb01118.x https://doi.org/10.1016/j.foodqual.2018.02.008 https://doi.org/10.1007/s12144-021-01920-3 Humanities and Social Sciences Letters, 2025, 13(1): 324-343 342 © 2025 Conscientia Beam. All Rights Reserved. Yetik, E., Ozdamar, N., & Bozkurt, A. (2020). Seamless learning design criteria in the context of open and distance learning. In Managing and Designing Online Courses in Ubiquitous Learning Environments. In (pp. 106-127). Hershey, PA: IGI Global. Zamri, N., Omar, N. B., Anwar, I. S. K., & Fatzel, F. (2021). Factors affecting students’ satisfaction and academic performance in open & distance learning (ODL). International Journal of Academic Research in Business and Social Sciences, 11(11), 1-16. Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30-41. APPENDIX Appendix 1 presents the modified scale used in measuring academic procrastination. Appendix 1. Academic procrastination. S/N Items Strongly disagree Disagree Neutral Agree Strongly agree I needlessly delay completing my academic assignments until the last minute Whenever I make study plans, I hardly keep to it I get easily distracted by other, more fun things when I am supposed to work on my academic work I always delay studying for exams until the exam approaches I am an incurable time waster when it comes to academic activities generally I postpone meeting with my instructor or academic advisor for support I manage to find an excuse for not attending classes, especially during the interactive sessions I manage to find an excuse for not attending classes, especially during the interactive sessions I delay making tough academic decisions When academic exercise proves too challenging to tackle, I believe in postponing it I don’t feel moved when colleagues are looking for additional materials to complement our course manuals I am not a library enthusiast. I don’t feel comfortable using the library Appendix 2 presents the modified scale used in measuring social support. Appendix 2. Social support. S/N Items Strongly disagree Disagree Neutral Agree Strongly agree There is a special person who is around when I am in need. There is a special person with whom I can share my joys and sorrows My family tries to help me. I get the emotional help and support I need from my family Humanities and Social Sciences Letters, 2025, 13(1): 324-343 343 © 2025 Conscientia Beam. All Rights Reserved. I have a special person who is a real source of comfort to me My friends try to help I can count on my friends when things go wrong I can talk about my problems with my family I have friends with whom I can share my joys and sorrows There is a special person in my life who cares about my feelings My family is willing to help me make decisions I can talk about my problems with my friends Appendix 3 presents the self-reported scale for academic performance. Appendix 3. Academic performance. Academic performance Grade point First class 3.5-4.00 Second class upper 3.0-3.49 Second class lower 2.0-2.99 Third class 1.0-1.99 Pass <1.0 Views and opinions expressed in this article are the views and opinions of the author(s), Humanities and Social Sciences Letters shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content.