TY - GEN
T1 - Predictive learning analytics for video-watching behavior in MOOCs
AU - Shridharan, Madhumitha
AU - Willingham, Ashley
AU - Spencer, Jonathan
AU - Yang, Tsung Yen
AU - Brinton, Christopher
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/21
Y1 - 2018/5/21
N2 - In this paper, we develop Predictive Learning Analytics (PLA) methodology for learner video-watching behavior in Massive Open Online Courses (MOOCs). After defining features to summarize such behavior from clickstream measurements, we perform a statistical analysis of a real-world MOOC dataset and uncover several interesting relationships between the different features. Motivated by this analysis, we propose three algorithms for predicting future video-watching behavior, which incorporate biases for learners and videos, collaborative filtering across videos, and regularization to reduce overfitting. Through evaluation on our dataset, we find that the predictors obtain low RMSE overall, and that augmenting the bias predictor with either collaborative filtering or regularization improves prediction quality in eight out of nine cases.
AB - In this paper, we develop Predictive Learning Analytics (PLA) methodology for learner video-watching behavior in Massive Open Online Courses (MOOCs). After defining features to summarize such behavior from clickstream measurements, we perform a statistical analysis of a real-world MOOC dataset and uncover several interesting relationships between the different features. Motivated by this analysis, we propose three algorithms for predicting future video-watching behavior, which incorporate biases for learners and videos, collaborative filtering across videos, and regularization to reduce overfitting. Through evaluation on our dataset, we find that the predictors obtain low RMSE overall, and that augmenting the bias predictor with either collaborative filtering or regularization improves prediction quality in eight out of nine cases.
UR - http://www.scopus.com/inward/record.url?scp=85048528195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048528195&partnerID=8YFLogxK
U2 - 10.1109/CISS.2018.8362323
DO - 10.1109/CISS.2018.8362323
M3 - Conference contribution
AN - SCOPUS:85048528195
T3 - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
SP - 1
EP - 6
BT - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd Annual Conference on Information Sciences and Systems, CISS 2018
Y2 - 21 March 2018 through 23 March 2018
ER -