Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior

Authors

  • Tuti Purwoningsih Universitas Terbuka
  • Wahyu Inayanto
  • Muhammad Yunus

DOI:

https://doi.org/10.33830/ptjj.v24i2.5055.2023

Keywords:

Learning analytics, Exploratory Data Analysis, E-learning, Learning Management System

Abstract

This article aims to explore learning analytics in e-learning through a comprehensive analysis of student characteristics and behavior. E-learning has become increasingly significant in education, particularly due to the social situation influenced by the pandemic. The Learning Management System (LMS) has become a crucial tool for educators to track and record student learning in e-learning environments. Learning analytics can aid in understanding the context of students, ensuring that they receive a personalized learning experience aligned with learning objectives. However, educators often face challenges in conducting learning analytics for e-learning students, primarily due to the large number of students to analyze and limited data availability. This study seeks to provide a detailed description of e-learning students within the Open and Distance Education (ODE) system. ODE students exhibit high diversity in demographic profiles, learning behaviors, and competency backgrounds. To support this research, we utilize datasets containing student demographic profiles and learning activity data during e-learning sessions. The datasets are obtained from the academic system and LMS log data of Universitas Terbuka. The article employs Exploratory Data Analysis (EDA) and data science approaches as the foundation for predictive and prescriptive analytics of student learning outcomes. Relevant features are extracted from the dataset to build a robust predictive model. The analysis results present patterns and relationships between student characteristics, learning behaviors, and academic achievements. This research aims to provide valuable insights for the development of more effective and personalized e-learning strategies to enhance student learning outcomes in the context of distance education. Moreover, the analysis findings can serve as a basis for informed academic decision-making to improve the quality of e-learning environments.

References

Balachandran, A. (2014). Large scale data analytics of user behavior for improving content delivery (Doctoral thesis, Carnegie Mellon University, 2014). Carnegie Mellon University.

Berry, L. J. (2017). Using learning analytics to predict academic success in online and face-to-face learning environments (Dissertation, Doctor of Education in Educational Technology, Boise State University, 2017) [Boise State University]. In Boise State University (Issue May). https://doi.org/10.1038/132817a0

Bolliger, D. U., Inan, F. A., & Wasilik, O. (2014). Development and Validation of the Online Instructor Satisfaction Measure (OISM). Educational Technology & Society, 17(2), 183–195. https://doi.org/10.1080/01587910902845949; Bourne, J., Moore, J.C., (2005) Elements of quality online education: Engaging communities., http://sloanconsortium.org/publications/books/vol6_summary.pdf, Needham, MA: Sloan Consortium. Retrieved from (Eds.); Bower, B.L., Distance education: Facing the faculty challenge (2001) Online Journal of Distance Learning Administration, 4 (2). , http://wwwwestga.edu/~distance/ojdla/summer42/bower42.html, Retrieved from; Burden, K., Atkinson, S., Evaluating pedagogical affo

Bravo-Agapito, J., Romero, S. J., & Pamplona, S. (2021). Early prediction of undergraduate Student’s academic performance in completely online learning: A five-year study. Computers in Human Behavior, 115(02), 106595. https://doi.org/10.1016/j.chb.2020.106595

Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning. https://doi.org/10.1504/IJTEL.2012.051815

Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction (J. Johnston (ed.); Eighth). Pearson Education, Inc., www.ablongman.com.

Dietric, D., Heller, B., & Yang, B. (2015). Data science and big data analytics: Discovering, analyzing, visualizing, and presenting data (C. Long (ed.); 1st ed.). John Wiley & Sons, Inc. www.wiley.com

Figueira, Á. (2017). Mining Moodle Logs for Grade Prediction. TEEM 2017, October 18–20, 2017, Cádiz, Spain, 1–8. https://doi.org/10.1145/3144826.3145394

Hussain, S., Çİfçİ, M. A., Tamayo, J. D., & Safdar, A. (2018). Big Data and Learning Analytics Model. International Journal of Computer Sciences and Engineering, 6(7), 654–3. https://doi.org/https://doi.org/10.26438/ijcse/v6i7.654663

Leedy, P. D., & Ormrod, J. E. (2015). Practical research: planning and design (J. W. Johnston (ed.); Eleventh). Pearson Education Limited.

Moodle Statistics. (2023). Moodle Statistics. Retrieved from https://stats.moodle.org/

Morris, L. V., Finnegan, C., & Wu, S. S. (2005). Tracking student behavior, persistence, and achievement in online courses. Internet and Higher Education, 8(3), 221–231. https://doi.org/10.1016/j.iheduc.2005.06.009

Mubarak, A., Cao, H., & Zhanga, W. (2022). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 30(8), 1414–1433. https://doi.org/10.1080/10494820.2020.1727529

Purwoningsih, T., Santoso, H. B., & Hasibuan, Z. A. (2020). Data analytics of students’ profiles and activities in a full online learning context. 5th International Conference on Informatics and Computing, ICIC 2020, 1–8. https://doi.org/10.1109/ICIC50835.2020.9288540

Purwoningsih, T., Santoso, H. B., Puspitasari, K. A., & Hasibuan, Z. A. (2021). Early prediction of students’ academic achievement: categorical data from fully online learning on machine-learning classification algorithms. Journal of Hunan University Natural Sciences, 48(9), 131–141. https://doi.org/http://jonuns.com/index.php/journal/article/view/713/710

Ren, Z. (2019). Academic Performance Prediction with Machine Learning Techniques.

Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30. http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

Yang, J., Rahardja, S., & Fränti, P. (2019). Outlier detection: How to threshold outlier scores? ACM International Conference Proceeding Series, AIIPCC2019, December 2019, Sanya, China, 1–6. https://doi.org/10.1145/3371425.3371427

Downloads

Published

29-12-2023

How to Cite

Purwoningsih, T., Inayanto, W., & Yunus, M. (2023). Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior. Jurnal Pendidikan Terbuka Dan Jarak Jauh, 24(2), 50–74. https://doi.org/10.33830/ptjj.v24i2.5055.2023

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.