7,146 research outputs found
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
pandemicâs 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 MOOCsâ 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
Elearning, Communication and Open-data: Massive Mobile, Ubiquitous and Open Learning
ABSTRACT: In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided
ECO D2.5 Learning analytics requirements and metrics report
In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided.Part of the work carried out has been funded with support from the European Commission, under the ICT Policy Support Programme, as part of the Competitiveness and Innovation Framework Programme (CIP) in the ECO project under grant agreement n° 21127
Systematic mapping review on studentâs performance analysis using big data predictive model
This paper classify the various existing predicting models that are used for monitoring andimproving studentsâ performance at schools and higher learning institutions. It analyses all theareas within the educational data mining methodology. Two databases were chosen for thisstudy and a systematic mapping study was performed. Due to the very infant stage of thisresearch area, only 114 articles published from 2012 till 2016 were identified. Within this, atotal of 59 articles were reviewed and classified. There is an increased interest and research inthe area of educational data mining, particularly in improving studentsâ performance withvarious predictive and prescriptive models. Most of the models are devised for pedagogicalimprovements ultimately. It is a huge scarcity in producing portable predictive models that fitsinto any educational environment. There is more research needed in the educational big data.Keywords: predictive analysis; studentâs performance; big data; big data analytics; datamining; systematic mapping study
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