Lightweight, Early Identification of At-Risk CS1 Students-Reference-Cited by-同舟云学术

Lightweight, Early Identification of At-Risk CS1 Students

Published:2016-08-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 ACM Conference on International Computing Education Research
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Author:

Liao Soohyun Nam¹,Zingaro Daniel²,Laurenzano Michael A.³,Griswold William G.¹,Porter Leo¹

Affiliation:

1. University of California at San Diego, San Diego, CA, USA

2. University of Toronto at Mississauga, Mississauga, ON, Canada

3. University of Michigan, Ann Arbor, MI, USA

Funder

NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2960310.2960315

Reference19 articles.

1. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance

2. Using machine learning techniques to predict introductory programming performance;Bergin S.;International Journal of Computer Science and Software,2015

3. Stay on These Roads

4. Approaches to learning in computer programming students and their effect on success. Higher Education in a changing world;de Raadt M.;Research and Development in Higher Education,2005

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