Identification of influencing factors for enhancing online learning usage model: evidence from an Indian University

Identification of influencing factors for enhancing online learning usage model: evidence from an Indian University

Автор: Sachin Ahuja, Puninder Kaur, S. N. Panda

Журнал: International Journal of Education and Management Engineering @ijeme

Статья в выпуске: 2 vol.9, 2019 года.

Бесплатный доступ

With advent of technology online education has become the core of educational settings worldwide. This paper aims to identify the factors that contribute in enhancing the online learning usage model in context of India, an emerging leader in Educational settings across the globe. In this study, data mining techniques were applied on the data collected from the log files of online courses. The initial investigations supported the use of custom build framework for teaching online courses. Data Structure course was taught using online platform and the data was collected using the log files. The data collected was further analysed using data mining techniques using Rapid miner tool. Although the results from three different data mining techniques showed some variations but the inferences from the results identified few common factors that have influence on enhancing the online learning usage model. Clustering techniques revealed that factors related to timely checking of online contents and posting have positive impact on online learning however decision trees supported that timely completing the online assignments along with checking of online contents and posting of messages played an important role in terms of enhancing the academic performance. This paper identifies three factors for teachers teaching online courses to improve overall performance of the students by learning from Indian University Success.

Еще

Online Course, Online Learning, Data Mining, Academic Performance

Короткий адрес: https://sciup.org/15015797

IDR: 15015797   |   DOI: 10.5815/ijeme.2019.02.02

Список литературы Identification of influencing factors for enhancing online learning usage model: evidence from an Indian University

  • Ahuja, S. (2017), “Using the Flipped Classroom to Improve Knowledge Creation of Master’s-Level Students in Engineering”, in Handbook of Research on Instructional Systems and Educational Technology, IGI Global, pp 326-339.
  • Asarta, C. J. and Schmidt, J R. (2017), "Comparing student performance in blended and traditional courses: Does prior academic achievement matter?", The Internet and Higher Education, Vol. 32, pp 29-38.
  • Baradwaj, B. K. and Pal S. (2012), “Mining Educational Data to Analyze Students' Performance”, International Journal of Advanced Computer Science and Applications, Vol. 2(6), pp 63-69.
  • Baker, R. SJD. and Yacef, K. (2009), “The State of Educational Data Mining in 2009: A Review and Future Visions”, JEDM- Journal of Educational Data Mining, Vol.1(1), pp 3-17.
  • Daradoumis, T., Bassi, R., Xhafa, F. and Caballe, S. (2013), “A Review on Massive e-learning (MOOC) Design, Delivery and Assessment”, In Eighth International Conference on P2P, Parallel,Grid, Cloud and Internet Computing (3PGCIC). IEEE. Compiegne, France, pp 208-213.
  • Doan, T.A., Zhang, J., TJHI, W.C. and Lee B. S. (2011) “Analyzing Students Usage of e-Learning Systems in the Cloud for Course Management”, In Proceedings of the 19th International Conference on Computers in Education (ICCE'11).Chiang Mai, Thailand: Asia-Pacific Society for Computers in Education.
  • Guo, G., Erdt, H. M. and Lee, S, B. (2013) “A Hybrid Recommender System based on Material Concepts with Difficulty Levels”, In Proceedings of the 21st International Conference on Computers in Education, Indonesia: Asia-Pacific Society for Computers in Education,pp9.
  • Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdenek, Z. and Wolff, A. (2014), “Modelling Student Online Behaviour in a Virtual Learning Environment”, In Machine Learning and Learning Analytics workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14), Indianapolis, Indiana, USA.
  • Han, Y., Wei, S. and Zhang, S. (2014), “An Analysis of Online Learning Behaviour from a Tutor Perspectives: Reflections on Interactive Teaching and Learning in the Big Data era”. Advancing Open and Distance Learning: Research and Practices, Vol. 10(2), pp 29-48.
  • He, W. (2013), “Examining Students Online Interaction in a Live Video Streaming Environment using Data Mining and Text Mining” , Computers in Human Behaviour, Vol.29(1),pp 90-102.
  • Jovanovic, M., Vukicevic, M., Milovanovic, M. and Minovic, M. (2012), “Using Data Mining on Student Behavior and Cognitive Style Data for Improving e-Learning Systems: A Case Study”, International Journal of Computational Intelligence Systems, Vol.5, (3), pp 597-610.
  • Liao, S. H., Chu, P. H. and Hsiao P. Y. (2012), “Data Mining Techniques and Applications–A Decade Review from 2000 to 2011” Expert Systems with Applications, Vol.39 (12), pp 11303-11311.
  • Mazza, R., Bettoni, M., Fare, M. and Mazzola, L. (2012), "Moclog–Monitoring Online Courses with Log Data", In 1st Moodle Research Conference Heraklion, Crete-Greece September, 14 - 15, pp 132-139.
  • Miguel, J., Caballe, S., Xhafa, F. and Prieto, J. (2014), “A Massive Data Processing Approach for Effective Trustworthiness in Online Learning Groups”, Concurrency and Computation: Practice and Experience, Vol. 27(8), pp 1988-2003.
  • Mukala, P., Buijs, J., Leemans, M. and Aalst, W. (2015), “Exploring Student’s Learning Behaviour in MOOCs using Process Mining Techniques”, Tech. rep., Eindhoven University of Technology, BPM Center Report BPM-15-10, BPM center. Org, pp-26.
  • Pardos, Z. A., Baker, R. S., San Pedro, M. O. C. Z., Gowda, S. M. and Gowda, S. M. (2014), “Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes”, Journal of Learning Analytics ,Vol. 1(1),pp 107-128.
  • Ramamohan, Y., Vasantharao, K., Chakravarti, C. K. and Ratnam, A. S. K. (2012), “A Study of Data Mining Tools in Knowledge Discovery Process”, International Journal of Soft Computing and Engineering (IJSCE) ISSN, pp 2231-2307.
  • Rathamani, M. and Sivaprakasam, P. (2012), “Cloud Mining: Web Usage Mining and User Behavior Analysis using Fuzzy C-means Clustering”, IOSR Journal of Computer Engineering (IOSRJCE), Vol. 7, pp 09-15.
  • Ratnapala, I. P., Ragel, R, G. and Deegalla, S. (2014), “Students Behavioural Analysis in an Online Learning Environment using Data Mining”, In 7th International Conference on Information and Automation for Sustainability, pp 1-7, IEEE.
  • Raval, K. M. (2012), “Data Mining Techniques”. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2(10), pp 439-442.
  • Richardson, J. C., Maeda, Y., Lv, J. and Caskurlu, S. (2017), “Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis”, Computers in Human Behavior, Vol. 71, pp 402-417.
  • Romiszowski, A. (2018), “The future of E-learning as an educational innovation: Factors influencing project success and failure”, Revista Brasileira de Aprendizagem Aberta e a Distância, Vol.2, pp-1-14.
  • Romero, C. and Ventura, S. (2010), “Educational data mining: a review of the State of the art”. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 40(6), pp 601-618.
  • Romero, C., Lopez M. I., Luna J, M. and Ventura S. (2013), “Predicting Student’s Final Performance from Participation in On-line Discussion forums”, Computers & Education, Vol. 68, pp 458-472.
  • Shukor, N. A., Tasir, Z. and Meijden, H. (2015), “An Examination of Online Learning Effectiveness using Data Mining”, Elsevier Procedia-Social and Behavioral Sciences, Vol. 172, pp 555-562.
  • Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y. and Yeh, D. (2008), “What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction”, Computers & education, Vol.50 (4), pp.1183-1202.
  • Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J. and Hlosta, M.(2014), “Developing Predictive Models for Early Detection of at-risk Students on Distance Learning Modules”, In Machine Learning and Learning Analytics Workshop at The 4th International Conference on Learning Analytics and Knowledge (LAK14), Indianapolis, Indiana, USA, p243.
  • Wolff, A., Zdrahal, Z., Nikolov, A. and Pantucek, M. (2013), “Improving Retention: Predicting at-risk Students by Analysing Clicking Behaviour in a Virtual Learning Environment”, In Proceedings of the third international conference on learning analytics and knowledge, Leuven, Belgium, pp 145-149.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp