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A Study on the Prediction of MOOC Learners' Academic Performance Based on Deep Neural Network

https://doi.org/10.2139/ssrn.4671150
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-55705-2_25openalex-extracted
What Decides the Dropout in MOOCs?
inferredcites →https://doi.org/10.1007/s00500-018-3581-3openalex-extracted
Student dropout prediction in massive open online courses by convolutional neural networks
inferredcites →https://doi.org/10.1007/s10639-019-09934-yopenalex-extracted
A predictive approach based on efficient feature selection and learning algorithms’ competition: Case of learners’ dropout in MOOCs
inferredcites →https://doi.org/10.1007/s10639-021-10512-4openalex-extracted
Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining
inferredcites →https://doi.org/10.1007/s11257-018-9203-zopenalex-extracted
Student success prediction in MOOCs
inferredcites →https://doi.org/10.1109/iccse.2016.7581554openalex-extracted
Machine learning application in MOOCs: Dropout prediction
inferredcites →https://doi.org/10.1145/3210713.3210730openalex-extracted
Temporal models for personalized grade prediction in massive open online courses
inferredcites →https://doi.org/10.1561/2200000006openalex-extracted
Learning Deep Architectures for AI
inferredcites →https://doi.org/10.3390/educsci9020104openalex-extracted
Predicting Learning Outcomes with MOOC Clickstreams
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