Student Engagement Tracking and Interpersonal Skills Development Analysis using Log Dataset

Authors

  • P Karthikeyan Thiagarajar College of Engineering, Madurai, Tamil Nadu
  • A M Abirami Thiagarajar College of Engineering, Madurai, Tamil Nadu
  • Thangavel Murugan College of Information Technology

DOI:

https://doi.org/10.16920/jeet/2025/v38is2/25008

Keywords:

Learning Management System; Moodle Log Dataset; Pedagogical Practice; Visual Approach.

Abstract

Migrating from traditional classroom learning to online platforms presents numerous challenges, particularly in determining suitable pedagogical designs for content delivery. Moodle log dataset analysis is one technique that addresses these challenges. Supported by visual analytics, this analysis helps teachers enhance educational outcomes, personalize learning experiences, and provide insights into student behavior. This study addresses one such challenge through the Research Question (RQ): "How can instructors enhance content delivery, student engagement, and analyze interpersonal skills development for skill-oriented courses?" To scientifically answer this RQ, instructors used various visual approaches aimed at improving pedagogical practices in a Design Thinking course. The study involved learners from two different cohorts, with Moodle log datasets collected to apply these visual approaches. Using the Tableau tool, diverse visualizations were developed to analyze student behavior regarding pedagogical practices in each cohort. Instructors then implemented changes in content delivery methods and pedagogical activities to improve interpersonal skills and performance. The results and discussions demonstrate that applying various visual approaches with student log data significantly aids instructors in effectively analyzing student behavior and engagement in Learning Management Systems (LMS), leading to improved student outcomes.

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Published

2025-05-12

How to Cite

Karthikeyan, P., Abirami, A. M., & Murugan, T. (2025). Student Engagement Tracking and Interpersonal Skills Development Analysis using Log Dataset. Journal of Engineering Education Transformations, 38(2), 56–65. https://doi.org/10.16920/jeet/2025/v38is2/25008

Issue

Volume 38, Special Issue, ICTIEE 2025, January 2025

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Ademi, N. Loskovska, S. & Kalajdziski, S. (2019). Prediction of Student Success Through Analysis of Moodle Logs: Case Study. ICT Innovations Big Data Processing and Mining, pp.27-40, DOI: 10.1007/978-3-030-331108_3.

Andrés, O.R., Rodríguez, A.M., Riaño, A.G.P., García, C.E., & Marín, M. (2023). Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e-learning platforms. Computer Applications in Engineering Education, 32(1).

Arpasat, P., Premchaiswadi, N., Porouhan, P. & Premchaiswad, W. (2021). Applying Process Mining to Analyze the Behavior of Learners in Online Courses. International Journal of Information and Education Technology, 11(10), pp. 436-443.

Byun, J., Pennington, D., Cardenas, J., Dutta, S. & Kirwan, J. (2014). Understanding Student Behaviors in Online Classroom: Data Scientific Approach. In proceedings of IEEE International Congress on Big Data, pp. 802803, Anchorage, AK, USA.

Boca, G.D. (2021). Factors Influencing Students’ Behavior and Attitude towards Online Education during COVID-19. Sustainability, 13, pp. 74-69.

Estacio, R.R. & Raga, R.C. (2017). Analyzing students online learning behavior in blended courses using Moodle. Asian Association of Open Universities Journal, 12(1), pp. 52-68.

Filipović, D., Balaban, L. & Oreski, D. (2018). Cluster analysis of students’ activities from logs and their success in self-assessment tests. In the 29th Proceedings of the Central European Conference on Information and Intelligent Systems, Varaždin, Croatia, pp. 85-93.

González, M.R., Ruíz, M.P. & Ortin, F. (2020). Massive LMS log data analysis for the early prediction of courseagnostic student performance. Computers & Education, 143, pp.104-108.

Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A. & Sarker, K.U. (2021). Dataset of Students’ Performance Using Student Information System, Moodle and the Mobile Application eDify. Data, 6, pp.1-10, https://doi.org/10.3390/data6110110.

Kadoic, N. & Oreski, D. (2018). Analysis of Student Behavior and Success Based on Logs in Moodle. In the proceedings of MIPRO 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, pp.730-735.

Konomu, D. (2017). Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis. In the proceedings of the International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2017, Marseille, France, pp.1835-1844.

Lin, C.L., Jin, Y.Q. & Zhao, Q. (2021). Factors Influence Students’ Switching Behavior to Online Learning under COVID-19 Pandemic: A Push–Pull–Mooring Model Perspective. The Asia-Pacific Education Researcher, 30, pp.229–245.

Ljubobratović, D. & Matetic, M. (2019). Using LMS activity logs to predict student failure with random forest algorithm. In the 7th International Conference The Future of Information Sciences INFuture2019 Knowledge in the Digital Age, Zagreb, Croatia, pp.113-119.

Mazza, R., Bettoni, M., Faré, M. & Mazzola, L. (2012). MOCLog – Monitoring Online Courses with log data. In the Proceedings of 1st Moodle Research Conference Heraklion, Greece, pp. 132-139.

Mtebe, J.S. & Kondoro, A.W. (2019). Mining Students’ Data to Analyse Usage Patterns in eLearning System of Secondary Schools in Tanzania. ournal of Learning for Development, 6(3), pp.228-244.

Peng, R. & Fu, R. (2021). The effect of Chinese EFL students’ learning motivation on learning outcomes within a blended learning environment. Australasian Journal of Educational Technology, 37(4), pp. 61-74, https://doi.org/10.14742/ajet.6235, 2021

Rachel, V., Sudhamathy, G. & Parthasarathy, M. (2018). Analytics on Moodle Data Using R Package for Enhanced Learning Management. International Journal of Applied Engineering Research, 13(22), pp. 15580-15610.

Riegel, K. & Evans, T. (2021). Student achievement emotions: Examining the role of frequent online assessment. Australasian Journal of Educational Technology, 37(6), pp.75-87, https://doi.org/10.14742/ajet.6516, 2021.

Rotelli, D., & Monreale, A. (2023). Processing and Understanding Moodle Log Data and their Temporal Dimension. Journal of Learning Analytics, 10(1), pp. 126-141.

Shoufan, A. (2020). Lecture-Free Classroom: Fully Active Learning on Moodle. IEEE Transactions on Education, 63(4), pp. 314-321.

Tamada, M.M., Giusti, R. & Netto, J.F.M. (2021). Predicting Student Performance Based on Logs in Moodle LMS. In the proceedings of the IEEE Frontiers in Education Conference (FIE), Lincoln, NE, USA, pp. 1-8.

Tan, X., She, J., Chen, S., Ohno, S. & Kameda, H. (2021). Analysis of Student Learning Behavior based on Moodle Log Data. In the proceedings of the 14th International Conference on Human System Interaction (HSI), Gdańsk, Poland, pp. 1-4.

Zambrano, J.L., Lara, J.A. & Romero, C. (2020). Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs. Applied Sciences, 10(1), pp. 1-23.

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), pp. 102–115.