Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

Chu, Yun-Wei; Tenorio, Elizabeth; Cruz, Laura; Douglas, Kerrie A.; Lan, Andrew S.; Brinton, Christopher G.

doi:10.1109/bigdata52589.2021.9671729

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…The behavioral data in our problem setting is measured by activities (e.g., clickstream data on different activities) on the online learning platform per day during the semester, which is widely used in student performance prediction frameworks (Adnan et al 2021;Qiu et al 2022;Karimi et al 2020;Chu et al 2021). The online activities indicate a student's effort and engagement from various aspects (e.g., reviewing course materials, completing course quizzes, participating in topic forums and collaborative activities) in an online course (Kuzilek, Hlosta, and Zdrahal 2017).…”

Section: Problem Formulationmentioning

confidence: 99%

“…The online activities indicate a student's effort and engagement from various aspects (e.g., reviewing course materials, completing course quizzes, participating in topic forums and collaborative activities) in an online course (Kuzilek, Hlosta, and Zdrahal 2017). Specifically, we leverage clickstream data to measure online activities as it is both easily collectible and effective in representing a student's learning trajectory across different activities in online education (Park et al 2017;Chu et al 2021). Moreover, clickstream data is demonstrated to effectively capture a student's behavioral pattern (e.g., conflict, confusion, and motivation in decision-making), which provides important information in predicting a student's performance (Rhim and Gweon 2022;Park et al 2017).…”

Section: Problem Formulationmentioning

confidence: 99%

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A Crowd–AI Collaborative Approach to Address Demographic Bias for Student Performance Prediction in Online Education

Zong,

Zhang,

Stinar

et al. 2023

HCOMP

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Recent advances in artificial intelligence (AI) and crowdsourcing have shown success in enhancing learning experiences and outcomes in online education. This paper studies a student performance prediction problem where the objective is to predict students' outcomes in online courses based on their behavioral data. In particular, we focus on addressing the limitation of current student performance prediction solutions that often make inaccurate predictions for students from underrepresented demographic groups due to the lack of training data and differences in behavioral patterns across groups. We develop DebiasEdu, a crowd–AI collaborative debias framework that melds the AI and crowd intelligence through 1) a novel gradient-based bias identification mechanism and 2) a bias-aware crowdsourcing interface and bias calibration design to achieve an accurate and fair student performance prediction. Evaluation results on two online courses demonstrate that DebiasEdu consistently outperforms state-of-the-art AI, fair AI, and crowd–AI baselines by achieving an optimized student performance prediction in terms of both accuracy and fairness.

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