Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach

Abstract

Millions of people have enrolled and enrol (especially in the Covid-19 pandemic world) in MOOCs. However, the retention rate of learners is notoriously low. The majority of the research work on this issue focuses on predicting the dropout rate, but very few use explainable learning patterns as part of this analysis. However, visual representation of learning patterns could provide deeper insights into learners' behaviour across different courses, whilst numerical analyses canand arguably, shouldbe used to confirm the latter. Thus, this paper proposes and compares different granularity visualisations for learning patterns (based on clickstream data) for both course completers and noncompleters. In the large-scale MOOCs we analysed, across various domains, our fine-grained, fish-eye visualisation approach showed that non-completers are more likely to jump forward in their learning sessions, often on a 'catch-up' path, whilst completers exhibit linear behaviour. For coarser, bird-eye granularity visualisation, we observed learners' transition between types of learning activity, obtaining typed transition graphs. The results, backed up by statistical significance analysis and machine learning, provide insights for course instructors to maintain engagement of learners by adapting the course design to not just 'dry' predicted values, but explainable, visually viable paths extracted.

E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?

“…For the (RQ3), on the one hand, some studies are interested in analysing learners' behaviour [38], learners' specific demands, learning styles [39], preferences and the context [40]. At the same time, these studies look at collaborative activities [41], data acquisition [37], assessment and resource management [42].…”

“…2020 was a challenging year for all the educational systems around the world. Despite the efforts invested in implementing those platforms to share courses and guarantee pedagogical continuity, these systems still experienced high dropout rates [37]. These rates made, MOOC providers think, once again, about improving the solutions offered, by reinforcing connectivity, collaboration, and adaptation.…”

Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study

“…Some of them, such as [5], [6] use statistics, or traditional machine learning algorithms (e.g., Decision Trees, Logistic Regression, Random Forest, Support Vector Machines) [7]- [10], while others, such as [11], [12], used more advanced algorithms (e.g. Deep Learning ), or even visualisation [13]. There are also a few studies [3], [4], that used both traditional machine learning algorithms and more advanced.…”

MOOC Next Week Dropout Prediction: Weekly Assessing Time and Learning Patterns

“…To the best of the authors' knowledge, this study is the first to consider participants' learning paths and associated behaviours in weekly dropout prediction. According to [1], a learning path is an insightful dropout prediction feature as successful learners will follow the instructed path and exhibit the so-called catch-up learning behaviours. Conversely, learners may jump forward and backward in their learning sessions [7], defined as exhibiting jumping behaviour and they are more likely to quit in the process.…”