TY - GEN
T1 - Learner behavioral feature refinement and augmentation using GANs
AU - Cao, Da
AU - Lan, Andrew S.
AU - Chen, Weiyu
AU - Brinton, Christopher G.
AU - Chiang, Mung
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Learner behavioral data (e.g., clickstream activity logs) collected by online education platforms contains rich information about learners and content, but is often highly redundant. In this paper, we study the problem of learning low-dimensional, interpretable features from this type of raw, high-dimensional behavioral data. Based on the premise of generative adversarial networks (GANs), our method refines a small set of human-crafted features while also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.
AB - Learner behavioral data (e.g., clickstream activity logs) collected by online education platforms contains rich information about learners and content, but is often highly redundant. In this paper, we study the problem of learning low-dimensional, interpretable features from this type of raw, high-dimensional behavioral data. Based on the premise of generative adversarial networks (GANs), our method refines a small set of human-crafted features while also generating a set of additional, complementary features that better summarize the raw data. Through experimental validation on a real-world dataset that we collected from an online course, we demonstrate that our method leads to features that are both predictive of learner quiz scores and closely related to human-crafted features.
UR - http://www.scopus.com/inward/record.url?scp=85049379330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049379330&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93846-2_8
DO - 10.1007/978-3-319-93846-2_8
M3 - Conference contribution
AN - SCOPUS:85049379330
SN - 9783319938455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 46
BT - Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
A2 - Luckin, Rose
A2 - Porayska-Pomsta, Kaska
A2 - du Boulay, Benedict
A2 - Mavrikis, Manolis
A2 - Penstein Rosé, Carolyn
A2 - McLaren, Bruce
A2 - Martinez-Maldonado, Roberto
A2 - Hoppe, H. Ulrich
PB - Springer Verlag
T2 - 19th International Conference on Artificial Intelligence in Education, AIED 2018
Y2 - 27 June 2018 through 30 June 2018
ER -