Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors

Mirzaei, Mehrdad; Sahebi, Shaghayegh; Brusilovsky, Peter

doi:10.1109/wiiat50758.2020.00041

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…For example, the student's knowledge grows significantly after watching W4 V0, but the next two attempts of watching W4 V1 and W4 V2 only improve the student's knowledge slightly. This conclusion is in line with previous research that shows assessed activities could be more helpful than repeating non-assessed ones [39], [40].…”

Section: F Student Knowledge State Visualizationsupporting

confidence: 93%

Transition-Aware Multi-Activity Knowledge Tracing

Zhao¹,

Wang²,

Sahebi³

2023

Preprint

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Section: F Student Knowledge State Visualizationsupporting

confidence: 93%

Transition-Aware Multi-Activity Knowledge Tracing

Zhao¹,

Wang²,

Sahebi³

2023

Preprint

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“…In education context, different methods such as machine learning [7][8][9] ,stochastic process [10,11] and matrix factorization based models [12,13] are used to evaluate students' academic performance. Studies have shown that factors such as student behavior is an indicator of performance [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Institution Study of Student Demographics and Stickiness of Computing Majors in the USA

Zahedi,

EbrahimNejad,

Ross

et al.

2021 CoNECD Proceedings

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Monique Ross earned a doctoral degree in Engineering Education from Purdue University. She has a Bachelor's degree in Computer Engineering from Elizabethtown College, a Master's degree in Computer Science and Software Engineering from Auburn University, eleven years of experience in industry as a software engineer, and three years as a full-time faculty in the departments of computer science and engineering. Her interests focus on broadening participation in engineering through the exploration of: 1) race, gender, and identity in the engineering workplace; 2) discipline-based education research (with a focus on computer science and computer engineering courses) in order to inform pedagogical practices that garner interest and retain women and minorities in computer-related engineering fields.

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