An In-Depth Methodology to Predict At-Risk Learners-Reference-Cited by-同舟云学术

An In-Depth Methodology to Predict At-Risk Learners

Published:2021 Issue: Volume: Page:193-206
ISSN:0302-9743
Container-title:Technology-Enhanced Learning for a Free, Safe, and Sustainable World
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Author:

Ben Soussia Amal,Roussanaly Azim,Boyer Anne

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86436-1_15

Reference13 articles.

1. Akub, K., Martin, H., Drahomira, H., Zdenek, Z., Jonas, V., Wolff, A.: OU analyse: analysing at-risk students at the open university. LAK (2015)

2. Alfred, E., Hanan, A.: Improving student success using predictive models and data visualisations. Research in Learning Technology (2012)

3. Alfred, E., Hanan, A.: Student success system: risk analytics and data visualization using ensembles of predictive models. LAK (2012)

4. Alice, Z., Amanda, C.: Feature Engineering for Machine Learning. O’REILLY (2018)

5. David, M.O., Du, Q.H., Mark, R., Martin, D., Damyon, W.: A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC. J. Comput. High. Educ. (2020)

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