Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks-Reference-Cited by-同舟云学术

Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks

Published:2019 Issue: Volume: Page:26-36
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Al-Luhaybi Mashael,Yousefi Leila,Swift Stephen,Counsell Steve,Tucker Allan

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-23204-7_3

Reference22 articles.

1. The Higher Education Statistics Agency (HESA) Website, HE student enrolments by level of study. https://www.hesa.ac.uk/data-and-analysis/sfr247/figure-3 . Accessed 30 Jan 2019

2. Romero, C., López, M.I., Luna, J.M., Ventura, S.: Predicting students final performance from participation in on-line discussion forums. Comput. Educ. 68, 458–472 (2013)

3. Bhardwaj, B.K. and Pal, S.: Data mining: a prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418 (2012)

4. Araque, F., Roldán, C., Salguero, A.: Factors influencing university drop out rates. Comput. Educ. 53(3), 563–574 (2009)

5. Seffrin, H.M., Rubi, G.L. Jaques, P.A.: A dynamic bayesian network for inference of learners’ algebraic knowledge. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 235–240. ACM (2014)

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