TY - GEN
T1 - Interactive Unknowns Recommendation in E-Learning Systems
AU - Teng, Shan Yun
AU - Li, Jundong
AU - Ting, Lo Pang Yun
AU - Chuang, Kun Ta
AU - Liu, Huan
N1 - Funding Information:
VI. CONCLUSIONS In this paper, we investigate if the concept graphs can be leveraged to enhance the performance of interactive unknowns recommendation on E-learning systems. To effectively discover user unknowns, we incorporate concept-aware graph embedding into a multi-armed bandit model, which leads to a novel framework CagMab. Moreover, we implement our method for the case studies on real E-learning dataset. The data analysis and experimental results show some particular phenomenon and demonstrate that the proposed system frame-work is effective and practical. Acknowledgement: This work was supported in part by Junyi Academy and by Ministry of Science and Technology, R.O.C., under Contract 107-2221-E-006 -165-MY2 and 107-2218-E-006-040.
Funding Information:
This work was supported in part by Junyi Academy and by Ministry of Science and Technology, R.O.C., under Contract 107-2221-E-006-165-MY2 and 107-2218-E-006-040
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - The arise of E-learning systems has led to an anytime-anywhere-learning environment for everyone by providing various online courses and tests. However, due to the lack of teacher-student interaction, such ubiquitous learning is generally not as effective as offline classes. In traditional offline courses, teachers facilitate real-time interaction to teach students in accordance with personal aptitude from students' feedback in classes. Without the interruption of instructors, it is difficult for users to be aware of personal unknowns. In this paper, we address an important issue on the exploration of 'user unknowns' from an interactive question-answering process in E-learning systems. A novel interactive learning system, called CagMab, is devised to interactively recommend questions with a round-by-round strategy, which contributes to applications such as a conversational bot for self-evaluation. The flow enables users to discover their weakness and further helps them to progress. In fact, despite its importance, discovering personal unknowns remains a challenging problem in E-learning systems. Even though formulating the problem with the multi-armed bandit framework provides a solution, it often leads to suboptimal results for interactive unknowns recommendation as it simply relies on the contextual features of answered questions. Note that each question is associated with concepts and similar concepts are likely to be linked manually or systematically, which naturally forms the concept graphs. Mining the rich relationships among users, questions and concepts could be potentially helpful in providing better unknowns recommendation. To this end, in this paper, we develop a novel interactive learning framework by borrowing strengths from concept-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in an interactive fashion for the recommendation in E-learning systems.
AB - The arise of E-learning systems has led to an anytime-anywhere-learning environment for everyone by providing various online courses and tests. However, due to the lack of teacher-student interaction, such ubiquitous learning is generally not as effective as offline classes. In traditional offline courses, teachers facilitate real-time interaction to teach students in accordance with personal aptitude from students' feedback in classes. Without the interruption of instructors, it is difficult for users to be aware of personal unknowns. In this paper, we address an important issue on the exploration of 'user unknowns' from an interactive question-answering process in E-learning systems. A novel interactive learning system, called CagMab, is devised to interactively recommend questions with a round-by-round strategy, which contributes to applications such as a conversational bot for self-evaluation. The flow enables users to discover their weakness and further helps them to progress. In fact, despite its importance, discovering personal unknowns remains a challenging problem in E-learning systems. Even though formulating the problem with the multi-armed bandit framework provides a solution, it often leads to suboptimal results for interactive unknowns recommendation as it simply relies on the contextual features of answered questions. Note that each question is associated with concepts and similar concepts are likely to be linked manually or systematically, which naturally forms the concept graphs. Mining the rich relationships among users, questions and concepts could be potentially helpful in providing better unknowns recommendation. To this end, in this paper, we develop a novel interactive learning framework by borrowing strengths from concept-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in an interactive fashion for the recommendation in E-learning systems.
UR - http://www.scopus.com/inward/record.url?scp=85061386027&partnerID=8YFLogxK
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U2 - 10.1109/ICDM.2018.00065
DO - 10.1109/ICDM.2018.00065
M3 - Conference contribution
AN - SCOPUS:85061386027
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 497
EP - 506
BT - 2018 IEEE International Conference on Data Mining, ICDM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Data Mining, ICDM 2018
Y2 - 17 November 2018 through 20 November 2018
ER -