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Li, Chenglu; Xing, Wanli; Leite, Walter – Grantee Submission, 2021
To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a…
Descriptors: Learning Analytics, Prediction, Models, Electronic Learning
Li, Chenglu; Xing, Wanli; Leite, Walter L. – Grantee Submission, 2022
Help-seeking is a valuable practice in online discussion forums. However, the asynchronicity and information overload of online discussion forums have made it challenging for help seekers and providers to connect effectively. This study formulated a new method to provide fair and accurate insights toward building a peer recommender to support…
Descriptors: Peer Relationship, Help Seeking, Electronic Learning, Distance Education
Li, Chenglu; Xing, Wanli; Leite, Walter L. – Grantee Submission, 2021
There has been a long-standing issue of sparse discussion forums participation in online learning, which can impede students' help seeking practices. Researchers have examined AI techniques such as link prediction with network analysis to connect help seekers with help providers. However, little is known whether these AI systems will treat…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Online Courses