TY - GEN
T1 - Heterogeneous Information Enhanced Prerequisite Learning in Massive Open Online Courses
AU - Wang, Tianqi
AU - Ma, Fenglong
AU - Wang, Yaqing
AU - Gao, Jing
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The knowledge concept prerequisites describing the dependencies are critical for fundamental tasks such as material recommendations and there are a huge amount of concepts in Massive Open Online Courses (MOOCs). Thus it is necessary to develop automatic prerequisite relation annotation methods. Recently, a few methods have shown their effectiveness in discovering knowledge concept prerequisites in Moocs automatically. However, they suffer from two common issues, i.e., knowledge concepts are not thoroughly learnt, and informative supervision sources are ignored. To overcome these issues, we propose an end-to-end framework to incorporate the rich heterogeneous information in MOOCs, including the semantic, contextual and structural information of the learning materials as well as student video watching behaviors. Such useful information is not only used to derive entity representations but also as supervision to improve the prerequisite learning task. Experimental results on two public datasets show that the proposed framework outperforms state-of-the-art baselines in terms of precision, recall and F1 values and improves up to 9% in terms of F1 metrics. Besides, ablation study demonstrates the effectiveness of the proposed framework.
AB - The knowledge concept prerequisites describing the dependencies are critical for fundamental tasks such as material recommendations and there are a huge amount of concepts in Massive Open Online Courses (MOOCs). Thus it is necessary to develop automatic prerequisite relation annotation methods. Recently, a few methods have shown their effectiveness in discovering knowledge concept prerequisites in Moocs automatically. However, they suffer from two common issues, i.e., knowledge concepts are not thoroughly learnt, and informative supervision sources are ignored. To overcome these issues, we propose an end-to-end framework to incorporate the rich heterogeneous information in MOOCs, including the semantic, contextual and structural information of the learning materials as well as student video watching behaviors. Such useful information is not only used to derive entity representations but also as supervision to improve the prerequisite learning task. Experimental results on two public datasets show that the proposed framework outperforms state-of-the-art baselines in terms of precision, recall and F1 values and improves up to 9% in terms of F1 metrics. Besides, ablation study demonstrates the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85147736017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147736017&partnerID=8YFLogxK
U2 - 10.1109/ICDM54844.2022.00155
DO - 10.1109/ICDM54844.2022.00155
M3 - Conference contribution
AN - SCOPUS:85147736017
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1203
EP - 1208
BT - Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
A2 - Zhu, Xingquan
A2 - Ranka, Sanjay
A2 - Thai, My T.
A2 - Washio, Takashi
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Data Mining, ICDM 2022
Y2 - 28 November 2022 through 1 December 2022
ER -