A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs-Reference-Cited by-同舟云学术

A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs

Published:2021-05-31 Issue:21 Volume:33 Page:14823-14835
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Rawat Seema^ORCID,Kumar Deepak^ORCID,Kumar Praveen^ORCID,Khattri Chhaya

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06122-3.pdf

Reference33 articles.

1. Allione G, Stein RM (2016) Mass attrition: an analysis of drop out from principles of microeconomics MOOC. J Econ Educ 47(2):174–186

2. Balakrishnan G, Coetzee D (2013) Predicting student retention in massive open online courses using hidden Markov models. Electr Eng Comput Sci Univ Calif Berkeley 53:57–58

3. Banerjee AV, Duflo E (2014) (Dis) organization and success in an economics MOOC. Am Econ Rev 104(5):514–518

4. Boyer S, Veeramachaneni K (2015) Transfer learning for predictive models in massive open online courses. In: International conference on artificial intelligence in education. Springer, Cham, pp 54–63

5. Cohen A, Baruth O (2017) Personality, learning, and satisfaction in fully online academic courses. Comput Hum Behav 72:1–12

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