Abstract
Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates of occurrence. Therefore, the early prediction of learner withdrawal has become a major challenge, as well as identifying the factors, which contribute to this increasingly occurring phenomenon. In that regard, this manuscript presents a framework for withdrawal prediction model for the data from The Open University, one of the largest distance learning institutions. For that purpose, we start by pre-processing the dataset and tackling the challenge of discretization process and unbalanced data. Secondly, this paper identifies the semantical issues of raw data by introducing new behavioural indicators. Finally, we reckon on machine learning algorithms for withdrawal prediction model to understand the lack of learners' commitment at an early stage.
Highlights
With an increasing interest in open educational resources, the web-based learning has become a commonplace in higher education institutions and organisations
There is plethora of different terms used in literature to describe the online learning delivery platforms like Virtual Learning Environment (VLE), or Learning Management Systems (LMS), or Massive Open Online Courses (MOOC)
We propose our own framework for a withdrawal prediction model for the OULAD dataset (Open University Learning Analytics Dataset) of the Open University, one of the largest distance learning institutions in United Kington (Kuzilek, et al, 2017)
Summary
With an increasing interest in open educational resources, the web-based learning has become a commonplace in higher education institutions and organisations. These tools often use the VLE learners’ characteristics as an input and the predicting the learners’ course withdrawal as an output In this context, various Machine Learning techniques have been successfully applied to obtain statistically high dropout prediction accuracy. Many behavioral indicators are proposed in literature like navigation type (Bousbia, et al, 2013), disorientation (Adda, et al, 2016), concentration rate (Ammor, et al, 2013), collaborative level (Bouzayane & Saad, 2017), contribution rate (Wong, et al, 2015) and effort level (Papanikolaou, 2015) These indicators offered a considerable representative power; they provided new semantically coherent features’ set, which is efficient for optimizing the predictability of learners’ dropout and for understanding the impact of the indicators on the learners’ commitment to the course completion. We recall the main contributions and identify some future research in short terms and others in the long terms
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