Learning analytics for motivating self-regulated learning and fostering the improvement of digital MOOC resources

Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of MOOCs (Massive Open Online courses) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real world conventional education setting. The educational pedagogy are tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issue in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. Firstly, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Secondly, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course

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

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 can -- and arguably, should -- be 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 non-completers. 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.Comment: Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science, vol 1214

Design Science MOOC : a framework of good practice pedagogy in a novel E-Learning platform eLDa

Massive open online courses (MOOCs) have taken higher educational establishments of the western world by storm with large amounts of funding diverted into developing and delivering a wide variety of online, massparticipation courses. Many claims have been made relating to their potential for providing free, high quality education to everyone, no matter what their situation or geographical position. In practice however, there is little evidence as yet of the desired democratisation of education, with lack of support for students, an absence of pedagogy and very high drop out rates. This project is concerned with MOOC evaluation. It aims to understand the reasons why students drop out and to implement and assess the effectiveness of measures to address specific areas relating to attrition. The theoretical framework of this study is applying design science research methodology (DSRM) in creating and developing a learning tool as an instrument for the research investigation. My research goal is on designing, implementing and evaluating solutions to mitigate these problems. Enabled by developing network and Cloud technologies, MOOCs are credited with the potential to provide free, open, high quality (yet low cost) education for large classes. However, current efforts are lacking in the necessary pedagogy and framework necessary to provide suitable materials for different learners and supporting individuals in their different learning paths. This is one of the major contributory factors to the extremely high drop out rates currently observed. This research exposes the learners’ choice of studies from the perspective of analytics and survey responses. It further described the features in the tool and the good practice to be considered while developing an online learning syste

Dropout Predictions of Ideological and Political MOOC Learners Based on Big Data

The massive open online course (MOOC) has expanded rapidly, providing users with a low-cost, high-quality learning experience. High dropout rate is a serious obstacle that restricts the development of ideological and political MOOC. One of the ways to solve this obstacle is to use the rich data resources in MOOC to explore the relevant factors of dropout. Reduce dropout rates by building drop-out prediction models and establishing early-warning mechanisms. However, the ideological MOOC data is huge and complex, which is prone to problems such as loss of data value, mismatch between data and models, and poor research reproducibility. This paper uses a more mature logistic regression method of machine learning to transfer it to the field of education, providing a new path for data-driven MOOC dropout prediction research