Self-construction and interactive simulations to support the learning of drawing graphs and reasoning in mathematics

In mathematics, sciences and economics, understanding and working with graphs are important skills. However, developing these skills has been shown to be a challenge in secondary and higher education as it involves high order thinking processes such as analysis, reflection and creativity. In this study, we present Interactive Virtual Math, a tool that supports the learning of a specific kind of graphs: dynamic graphs which represent the relation between at least two quantities that covary. The tool supports learners in visualizing abstract relations through enabling them to draw, move and modify graphs, and by combining graphs with other representations, especially interactive animations and textual explanations. This paper reports a design experiment about students’ learning graphs with this tool. Results show that students with difficulty in generating acceptable graphs improve their ability while working with the tool

Learning analytics dashboard for motivation and performance

Deploying Learning Analytics that significantly improve learning outcomes remains a challenge. Motivation has been found to be related to academic achievement and is argued to play an essential role in efficient learning. We developed a Learning Analytics dashboard and designed an intervention that relies on goal orientation and social comparison. Subjects can see a prediction of their final grade in a course as well as how they perform in comparison to classmates with similar goal grades. Those with access to the dashboard ended up more motivated than those without access, outperformed their peers as the course progressed and achieved higher final grades. Our results indicate that learner-oriented dashboards are technically feasible and may have tangible benefits for learners

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic’s impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has revolutionised extensive Learning Analytics (LA) tasks. More recently, the associated deep learning techniques are increasingly deployed to address the dropout prediction problem. This survey gives a timely and succinct overview of deep learning techniques for MOOCs’ learning analytics. We mainly analyse the trends of feature processing and the model design in dropout prediction, respectively. Moreover, the recent incremental improvements over existing deep learning techniques and the commonly used public data sets have been presented. Finally, the paper proposes three future research directions in the field: knowledge graphs with learning analytics, comprehensive social network analysis, composite behavioural analysis