SHS Web Conf.
Volume 102, 2021The 3rd ETLTC International Conference on Information and Communications Technology (ETLTC2021)
|Number of page(s)||8|
|Section||Applications in Computer Science|
|Published online||03 May 2021|
- N. Sclater, Learning Analytics Explained. Florence: Taylor and Francis, pp.35-43, New York: Routledge (2017). [Google Scholar]
- B. Wong, Learning analytics in higher education: an analysis of case studies, AAOUJ, pp. 21-40 (2017). [Google Scholar]
- H. Sokout, and T. Usagawa, Analyzing the Current Situation of E-learning at Kabul Polytechnic University. In Proceedings of the 2nd International Conference on Education and Multimedia Technology, ACM, Okinawa, Japan, (2018). DOI: 10.1145/3206129.3239428. [Google Scholar]
- H. Sokout, N. Ramaki, & T. Usagawa, Prospects of learning analytics for higher education in developing countries. A case of Kabul Polytechnic University, Afghanistan. In Proceedings of IEICE Technical Report, Educational Technology (pp. 43–48), Nagaoka, (2018). [Google Scholar]
- U. Upasana, How to handle Imbalanced classification problems in machine learning, [online] Analytics Vidhya. Retrieved from https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/, (2019). [Google Scholar]
- G. Lina, Data sampling improvement by developing SMOTE technique in SAS. Paper 3483 (2015). [Google Scholar]
- M. Qais Sherzai, Students’ General Results, Kabul, (2020). [Google Scholar]
- H. Owen, Y. Anna, C. Jeff, J. Q. Albert, H. Ogata, and J. H. Yang, Applying Learning Analytics for the Early Predicting of Students’ Academic Performance in Blended Learning, ET&S, 21 (2018). [Google Scholar]
- A. FUNGAI, and T. Usagawa, Isolating hidden recurring patterns in unlabeled access log data on LMS to identify dropout risk students, In The 11th International Student Conference on Advanced Science and Technology, Kumamoto, Japan, (2016). [Google Scholar]
- C. Romero, P.G. Espejo, A. Zafra, J.R. Romero, and S. Ventura, Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, Vol. 21, no. 5, pp. 135–146 (2013). [Google Scholar]
- O. Sukhbaatar and T. Usagawa, A prediction of failure-prone students in web-based educational environment, TCET, 119, pp. 17-20 (2019). [Google Scholar]
- J.D. Milne, L.M. Jeffrey, G. Suddaby, and A. Higgins. Early identification of students at risk of failing. In M. Brown, M. Hartnett, & T. Stewart (Eds.), Future challenges, sustainable futures. Proceedings Ascilite Wellington 2012, pp. 657–661 (2012). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.