Collaborative Learning Management System with Analytical Insights: A Preliminary Study DOI: https://doi.org/10.33093/ijomfa.2024.5.1.6

Main Article Content

Daniel Lai
Sook-Ling Lew
Shih Yin Ooi

Abstract

The mode of teaching and learning had been drastically changed over the decades. Therefore, one approach might not fit into all scenarios. Collaborative learning promotes collaboration between the students in completing given tasks with common goals. In this paper, problem statements were formed: (i) the collaboration between students and their teachers in the virtual learning environment has been at the bare minimum, (ii) the learning management system implemented has not been fully utilised with the data and information collected academically. Moreover, systematic literature review (SLR) is practised to investigate insights about collaborative learning, learning management system (LMS) and analytical approaches for student profiling. The aim of this paper is to address three research questions formed in the SLR: (i) What is the most commonly practised methodology for collaborative learning? (ii) What are the typical practised analytical methods and models for student profiling? and (iii) What factors influence students to use the learning management system? Besides, collaborative learning enables the students to conduct group discussions and assignments, promoting mutual interactions and creating knowledge amongst them. Additionally, the third-party LMS lacks synchronous chat feature. A student's profile grants educators valuable insights into the student's academic performance and learning progress. This information contributes to predicting the student’s performance with the assistance of analytical approaches applied. The applied analytical approaches provide useful information about the student’s learning behaviour, allowing the teachers to take adequate action. As a result, a conceptual framework is constructed with hypotheses formulated, reflecting the relations between each construct. Besides, a dedicated collaborative learning management system with machine learning capabilities is an ideal solution, tackling students’ collaboration among peers and between teachers with their academic performance and behaviour taken into account.

Article Details

Section
Management, Finance and Accounting

References

AbuKamar, M., & Kamar, A. A. (2022). Barriers and motivation for online learning among pharmacy students in Jordan. Tropical Journal of Pharmaceutical Research, 21(4), 841-845. https://doi.org/10.4314/tjpr.v21i4.22

Al-Mamary, Y. H. S. (2022). Why do students adopt and use Learning Management Systems?: Insights from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), 100088. https://doi.org/10.1016/j.jjimei.2022.100088

Aung, M. M. (2022). Collaborative Learning method using in sociolinguistics online Education. https://springuniversitymm.com/

Bai, X., Zhang, F., Li, J., Guo, T., Aziz, A., Jin, A., & Xia, F. (2021). Educational Big Data: Predictions, Applications and Challenges. Big Data Research, 26, 100270. https://doi.org/10.1016/j.bdr.2021.100270

Børte, K., Nesje, K., & Lillejord, S. (2020). Barriers to student active learning in higher education. Teaching in Higher Education, 28(3), 597-615. https://doi.org/10.1080/13562517.2020.1839746

Bradley, V. M. (2020). Learning Management System (LMS) Use with Online Instruction. International Journal of Technology in Education, 4(1), 68-92. https://doi.org/10.46328/ijte.36

Burbules, N. C., Fan, G., & Repp, P. (2020). Five trends of education and technology in a sustainable future. Geography and Sustainability, 1(2), 93–97. https://doi.org/10.1016/j.geosus.2020.05.001

Garcia-Penalvo, F. J. (2022). Developing robust state-of-the-art reports: Systematic Literature Reviews. Education in the Knowledge Society, 23, 1-21. https://doi.org/10.14201/eks.28600

Herrera-Pavo, M. Á. (2021). Collaborative learning for virtual higher education. Learning, Culture and Social Interaction, 28, 100437. https://doi.org/10.1016/j.lcsi.2020.100437

Khan, A., Hasana, M. K., Ghazal, T. M., Islam, S., Alzoubi, H. M., Mokhtar, U. A., Alam, R., & Ahmad, M. (2022). Collaborative Learning Assessment via Information and Communication Technology. Proceedings - 2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022. https://doi.org/10.1109/RIVF55975.2022.10013841

Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia - Social and Behavioral Sciences, 31, 486-490. https://doi.org/10.1016/j.sbspro.2011.12.091

Lwande, C., Muchemi, L., & Oboko, R. (2021). Identifying learning styles and cognitive traits in a learning management system. Heliyon, 7(8), E07701. https://doi.org/10.1016/j.heliyon.2021.e07701

Madiah, H., & Mohemad, R. (2023). A review of learning management systems (LMS) framework towards the element of outcome based education (OBE). AIP Conference Proceedings, 2484. https://doi.org/10.1063/5.0113769

Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2021). Factors affecting students’ learning performance through collaborative learning and engagement. Interactive Learning Environments, 31, 2371-2391. https://doi.org/10.1080/10494820.2021.1884886

Sadia Shaheen, Faiqa Kiran, Jamila Khurshid, & Sehar Zulfiqar. (2023). Challenges for Online Learning in Higher Education: A Developing Country Perspective. In K. Walters (Ed.), Dynamic Curriculum Development and Design Strategies for Effective Online Learning in Higher Education (pp. 39–55). IGI Global.

Su, F., & Zou, D. (2022). Technology-enhanced collaborative language learning: theoretical foundations, technologies, and implications. Computer Assisted Language Learning, 35(8), 1754-1788. https://doi.org/10.1080/09588221.2020.1831545

Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 1-23. https://doi.org/10.1186/s41239-021-00313-7

Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., & Caballé, S. (2021). Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning. Lecture Notes in Networks and Systems, 158 LNNS. https://doi.org/10.1007/978-3-030-61105-7_21

Yakubu, M. N., & Abubakar, A. M. (2022). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, 51(2), 916-934. https://doi.org/10.1108/K-12-2020-0865