Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming Courses
Author:
Affiliation:
1. Technological University Dublin, Ireland
2. Universitat Jaume I, Spain
3. Maynooth University, Ireland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3633083.3633190
Reference72 articles.
1. Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6 (2018), 52138–52160.
2. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance
3. Predicting Students Final GPA Using Decision Trees: A Case Study
4. Chaudhry Muhammad Ali and Kazim Emre. 2022. Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021. AI Ethics 2(1) (2022), 157–165.
5. European AI Alliance. 2021. ALTAI - The Assessment List on Trustworthy Artificial Intelligence. Retrieved January 19, 2022 from https://futurium.ec.europa.eu/en/european-ai-alliance/pages/altai-assessment-list-trustworthy-artificial-intelligence
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