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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach
Creator	Shi, Lei Alamri, Ahmed Cristea, Alexandra Senthilnathan, Gautham Stewart, Craig Sun, Zhongtian
Source	ArXiv; PMC
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 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.
has issue date	2020-06-03(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49663-0_42
has license	no-cc
sha1sum (hex)	6c6f2f86f65396f4cbe6b5bbf0b4402ebc5a7373
schema:url	https://doi.org/10.1007/978-3-030-49663-0_42
resource representing a document's title	Is MOOC Learning Different for Dropouts? A Visually-Driven, Multi-granularity Explanatory ML Approach
has PubMed Central identifier	PMC7266654
schema:publication	Intelligent Tutoring Systems
resource representing a document's body	covid:6c6f2f86f65396f4cbe6b5bbf0b4402ebc5a7373#body_text
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