Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/22077
Title: 
Authors: 
Keywords: 
Learning analytics
Data mining classification
Learners’ success
Learning behaviour
Digital learning
Issue Date: 
2-Dec-2019
Citation: 
Khor, E. T., & Looi, C. K. (2019). A learning analytics approach to model and predict learners’ success in digital learning. In S. Chew Yi Wei, C. Kah Mun, & A. Alphonso (Eds.), Australasian Society for Computers in Learning in Tertiary Education (ASCILITE) 2019 Conference Proceedings (Vol. 36, pp. 476-480). ASCILITE.
Abstract: 
Learning analytics methods are widely applied in the educational field to gain insights on hidden patterns from educational data. Methods like predictive learning analytics are used to identify and measure patterns in learning data and extrapolate future behaviours. It can be used to enable the learners to be more self-aware of their learning behaviours and to enable the instructor to take appropriate actions informed by the trace of data. Thus such methods can empower learners as they progress through online training, and allows them to be self-regulated in order to solidify their learning and develop positive habits that will enhance their learning experiences. This paper reports on the use of a popular decision tree classification algorithm using behavioural features from a public domain dataset to develop a predictive model for predicting learning performance. Among the five behavioural features, we find that the measure of visited resources provides the most discriminating rules in the classifier.
Description: 
This paper was published in the 2019 proceedings of the 36TH International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, ASCILITE 2019: Personalised Learning. Diverse Goals. One Heart, held at the Singapore University of Social Sciences, 2 - 5 December 2019.
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Appears in Collections:Conference Papers

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