Evaluating Student Activity and Learning on Moodle: A Data-Driven Analysis and Insights of Usage Reports
Abstract
Learning Management Systems (LMS) are extensively utilized to enhance teaching and learning in higher education institutions. These platforms provide invaluable insights into users' usage data and behavior within the online environment. Therefore, evaluating users' activity on these platforms is essential to maximize their effectiveness. This study aims to evaluate usage reports and engagement patterns and analyze online student learning activity using Moodle's tracking features. To achieve this, statistical and visualization techniques were employed to analyze student data from a year-long module delivered in a blended mode during the first semester at a South African university. The study utilized LMS log data to evaluate students' and instructors' usage patterns and engagement levels on the online platform, focusing on module-related activities. The data mining analysis revealed that LMS usage was significantly higher when students were on campus during the first semester and relatively lower when off-campus or in residence. In addition, no significant differences were observed in the type of LMS tools used or module activities across the eight months of the first semester. In conclusion, this data-driven approach and its findings underscore the importance of monitoring LMS activity.
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Alier, M., Casañ Guerrero, M. J., Amo, D., Severance, C., & Fonseca, D. (2021). Privacy and E-Learning: A pending task. Sustainability, 13(16), Article 9206. https://doi.org/10.3390/su13169206
Bradley, V. M. (2021). Learning Management System (LMS) is used with online instructions. International Journal of Technology in Education, 4(1), 68–92. https://doi.org/10.46328/ijte.36
Burušić, J., Šimunović, M., & Šakić, M. (2021). Technology-based activities at home and STEM school achievement: The moderating effects of student gender and parental education. Research in Science & Technological Education, 39(1), 1–22. https://doi.org/10.1080/02635143.2019.1646717
Cabero-Almenara, J., Arancibia, M. L., & Del Prete, A. (2019). Technical and didactic knowledge of the Moodle LMS in higher education: Beyond functional use. Journal of New Approaches in Educational Research, 8(1), 25–33. https://doi.org/10.7821/naer.2019.1.327
Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64(8), 723–733. https://doi.org/10.1177/016146816306400801
Cerezo, R., Bogarín, A., Esteban, M., & Romero, C. (2020). Process mining for self regulated learning assessment in e learning. Journal of Computing in Higher Education, 32(1), 74–88. https://doi.org/10.1007/s12528-019-09225-y
Cwala, S., Mbodila, M., & Elegbeleye, F. A. (2024). E-Records system for healthcare patient folder management. In Software engineering methods design and application (pp. 97–113). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70285-3_8
Dawson, S., & Siemens, G. (2014). Analytics to literacy: The development of a learning analytics framework for multiliteracies assessment. International Review of Research in Open and Distributed Learning, 15(4), 284–305. https://doi.org/10.19173/irrodl.v15i4.1878
Elegbeleye, F. A., Mbodila, M., Mabovana, A., & Esan, O. A. (2022). Data privacy on using four models: A review. In Proceedings of the 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1–9). https://doi.org/10.1109/ICECET55527.2022.9872999
Fink, M. (2022). Learning analytics in learning management systems in the Austrian higher education system [Master’s thesis, University of Applied Sciences Technikum Wien]. Repositum. https://repositum.tuwien.at/handle/20.500.12708/192607
Gamage, S. H. P. W., Ayres, J. R., & Behrend, M. B. (2022). A systematic review on trends in using Moodle for teaching and learning. International Journal of STEM Education, 9(1), Article 9. https://doi.org/10.1186/s40594-021-00323-x
Garcia, M. B., Arif, Y. M., Khlaif, Z. N., Zhu, M., de Almeida, R. P. P., de Almeida, R. S., & Masters, K. (2024). Effective integration of artificial intelligence in medical education: Practical tips and actionable insights. In Transformative approaches to patient literacy and healthcare innovation (pp. 1–19). IGI Global. https://doi.org/10.4018/979-8-3693-3661-8.ch001
Hakimi, M., Katebzadah, S., & Fazil, A. W. (2024). Comprehensive insights into e-learning in contemporary education: Analyzing trends, challenges, and best practices. Journal of Education and Teaching Learning (JETL), 6(1), 86–105. https://doi.org/10.51178/jetl.v6i1.1720
Hamid, S., Ismail, S. N., Hamzah, M., & Malik, A. W. (2022). Developing engagement in the learning management system supported by learning analytics. Computer Systems Science & Engineering, 42(1), 335–350. https://doi.org/10.32604/csse.2022.021927
Kumar, V., & Sharma, D. (2016). Creating a collaborative and convenient learning environment using cloud-based Moodle LMS: An instructor and administrator perspective. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 11(1), 35–50. https://doi.org/10.4018/IJWLTT.2016010103
Lang, C., Siemens, G., Wise, A., & Gašević, D. (2017). Handbook of learning analytics. Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17
Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. In A. Peña-Ayala (Ed.), Learning analytics: Fundamentals, applications, and trends (pp. 143–169). Springer. https://doi.org/10.1007/978-3-319-52977-6_6
Macfarlane, R. (2021). Obstetrics for schools: Eliminating failure and ensuring the safe delivery of all learners. Crown House Publishing Ltd.
Maluleke, A. F. (2024). Enhancing learning analytics through learning management systems engagement in African higher education. Journal of Education and Learning Technology, 5(6), 130–149. https://doi.org/10.38159/jelt.2024565
Martin, F., Ndoye, A., & Wilkins, P. (2016). Using learning analytics to enhance student learning in online courses based on Quality Matters standards. Journal of Educational Technology Systems, 45(2), 165–187. https://doi.org/10.1177/0047239516656369
Mbaleki, C., Elegbeleye, F. A., Esan, O. A., & Rabotapi, T. (2023). Power supply rationing in an era of e learning: Evidence from the rural university. EUREKA: Social and Humanities, 6, 3–12. https://doi.org/10.21303/2504-5571.2023.003184
Perić Prkosovački, B. A., Đokić Jovin, S. V., & Brkić Jovanović, N. R. (2024). Moderating role of learning strategies through student assessment of distance learning. Inovacije u nastavi, 37(1), 75–90. https://doi.org/10.5937/inovacije2401075P
Raj, N. S., & Renumol, V. G. (2024). An improved adaptive learning path recommendation model driven by real time learning analytics. Journal of Computers in Education, 11(1), 121–148. https://doi.org/10.1007/s40692-022-00250-y
Rahman, M. J. A., Daud, M. Y., & Ensimau, N. K. (2019). Learning Management System (LMS) in teaching and learning. International Journal of Academic Research in Business and Social Sciences, 9(11), 1529–1535. https://doi.org/10.6007/IJARBSS/v9-i11/6717
Ramansyah, W., Praherdhiono, H., Degeng, I. N. S., & Kuswandi, D. (2024). A gamified MOOC: The development of an interactive Moodle based learning environment. Ubiquitous Learning: An International Journal, 17(1), 93–119. https://doi.org/10.18848/1835-9795/CGP/v17i01/93 119
Rebeca, C., Alejandro, B., María, E., & Romero, C. (2020). Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education, 32(1), 74–88. https://doi.org/10.1007/s12528-019-09225-y
Rose, S. (2024). Interaction via Moodle for teaching and learning: Perceptions of lecturers and students. i.e.: Inquiry in Education, 16(1), Article 3. https://digitalcommons.nl.edu/ie/vol16/iss1/3
Rotelli, D., & Monreale, A. (2022). Time on task estimation by data driven outlier detection based on learning activities. In Proceedings of the 12th International Learning Analytics and Knowledge Conference (LAK22) (pp. 1–11). Association for Computing Machinery. https://doi.org/10.1145/3506860.3506913
Shoaib, M., Sayed, N., Singh, J., Shafi, J., Khan, S., & Ali, F. (2024). AI student success predictor: Enhancing personalized learning in campus management systems. Computers in Human Behavior, 158, Article 108301. https://doi.org/10.1016/j.chb.2024.108301
Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340
Tatineni, S. (2020). Recommendation systems for personalized learning: A data driven approach in education. Journal of Computer Engineering and Technology (JCET), 4(2), 18–31. https://doi.org/10.15224/978-1-63248-091-3-10
Tomei, L. A., Maine, J., Moussa, K., Holler, M. B., Hobbs, B., & Austin, S. (2024). The top 12 technologies for teaching and learning in the post pandemic era. In L. A. Tomei & D. Carbonara (Eds.), Exploring technology infused education in the post pandemic era (pp. 1–95). IGI Global. https://doi.org/10.4018/979-8-3693-2885-9.ch001
Uhlenhake, K. J. (2019). Using learning management system data logs to examine student interaction with available LMS tools and course login frequency [Doctoral dissertation, Baker University]. https://www.bakeru.edu/dissertation/using-learning-management-system-data-logs-to-examine-student-interaction-with-available-lms-tools-and-course-login-frequency/
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003
Zhang, Y., Ghandour, A., & Shestak, V. (2020). Using learning analytics to predict students’ performance in Moodle LMS. International Journal of Emerging Technologies in Learning (iJET), 15(20), 102–115. https://doi.org/10.3991/ijet.v15i20.15915
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