The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L’application généralisée des petits cours privés en ligne (SPOC) donne une impulsion puissante à la réforme de l’enseignement supérieur. Durant le processus d'enseignement, un enseignant a besoin de comprendre la difficulté des vidéos SPOC pour les étudiants en temps réel afin d'être davantage concentré sur les difficultés et les points clés du cours en classe inversée. Cependant, les techniques d'exploration de données éducatives existantes accordent peu d'attention au regroupement ou à la classification des difficultés vidéo SPOC. Dans cet article, nous proposons une approche pour regrouper les vidéos SPOC basée sur la difficulté d'utiliser les données de visionnage de vidéos dans un SPOC. Plus précisément, un graphique bipartite exprimant la relation d’apprentissage entre les étudiants et les vidéos est construit en fonction du nombre de fois où ils ont visionné des vidéos. Ensuite, l'algorithme SimRank++ est utilisé pour mesurer la similarité de difficulté entre deux vidéos quelconques. Enfin, l'algorithme de regroupement spectral est utilisé pour mettre en œuvre le regroupement vidéo en fonction de la similarité de difficulté obtenue. Des expériences sur un ensemble de données réelles dans un SPOC montrent que l'approche proposée a une meilleure précision de clustering que les autres approches existantes. Cette approche permet aux enseignants de découvrir en temps réel la difficulté globale d'une vidéo SPOC pour les élèves, et donc les points de connaissances peuvent être expliqués plus efficacement dans une classe inversée.
Feng ZHANG
Shandong University of Science and Technology
Di LIU
Shandong University of Science and Technology
Cong LIU
Shandong University of Technology
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Feng ZHANG, Di LIU, Cong LIU, "Difficulty-Based SPOC Video Clustering Using Video-Watching Data" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 3, pp. 430-440, March 2021, doi: 10.1587/transinf.2020EDP7106.
Abstract: The pervasive application of Small Private Online Course (SPOC) provides a powerful impetus for the reform of higher education. During the teaching process, a teacher needs to understand the difficulty of SPOC videos for students in real time to be more focused on the difficulties and key points of the course in a flipped classroom. However, existing educational data mining techniques pay little attention to the SPOC video difficulty clustering or classification. In this paper, we propose an approach to cluster SPOC videos based on the difficulty using video-watching data in a SPOC. Specifically, a bipartite graph that expresses the learning relationship between students and videos is constructed based on the number of video-watching times. Then, the SimRank++ algorithm is used to measure the similarity of the difficulty between any two videos. Finally, the spectral clustering algorithm is used to implement the video clustering based on the obtained similarity of difficulty. Experiments on a real data set in a SPOC show that the proposed approach has better clustering accuracy than other existing ones. This approach facilitates teachers learn about the overall difficulty of a SPOC video for students in real time, and therefore knowledge points can be explained more effectively in a flipped classroom.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7106/_p
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@ARTICLE{e104-d_3_430,
author={Feng ZHANG, Di LIU, Cong LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Difficulty-Based SPOC Video Clustering Using Video-Watching Data},
year={2021},
volume={E104-D},
number={3},
pages={430-440},
abstract={The pervasive application of Small Private Online Course (SPOC) provides a powerful impetus for the reform of higher education. During the teaching process, a teacher needs to understand the difficulty of SPOC videos for students in real time to be more focused on the difficulties and key points of the course in a flipped classroom. However, existing educational data mining techniques pay little attention to the SPOC video difficulty clustering or classification. In this paper, we propose an approach to cluster SPOC videos based on the difficulty using video-watching data in a SPOC. Specifically, a bipartite graph that expresses the learning relationship between students and videos is constructed based on the number of video-watching times. Then, the SimRank++ algorithm is used to measure the similarity of the difficulty between any two videos. Finally, the spectral clustering algorithm is used to implement the video clustering based on the obtained similarity of difficulty. Experiments on a real data set in a SPOC show that the proposed approach has better clustering accuracy than other existing ones. This approach facilitates teachers learn about the overall difficulty of a SPOC video for students in real time, and therefore knowledge points can be explained more effectively in a flipped classroom.},
keywords={},
doi={10.1587/transinf.2020EDP7106},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Difficulty-Based SPOC Video Clustering Using Video-Watching Data
T2 - IEICE TRANSACTIONS on Information
SP - 430
EP - 440
AU - Feng ZHANG
AU - Di LIU
AU - Cong LIU
PY - 2021
DO - 10.1587/transinf.2020EDP7106
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2021
AB - The pervasive application of Small Private Online Course (SPOC) provides a powerful impetus for the reform of higher education. During the teaching process, a teacher needs to understand the difficulty of SPOC videos for students in real time to be more focused on the difficulties and key points of the course in a flipped classroom. However, existing educational data mining techniques pay little attention to the SPOC video difficulty clustering or classification. In this paper, we propose an approach to cluster SPOC videos based on the difficulty using video-watching data in a SPOC. Specifically, a bipartite graph that expresses the learning relationship between students and videos is constructed based on the number of video-watching times. Then, the SimRank++ algorithm is used to measure the similarity of the difficulty between any two videos. Finally, the spectral clustering algorithm is used to implement the video clustering based on the obtained similarity of difficulty. Experiments on a real data set in a SPOC show that the proposed approach has better clustering accuracy than other existing ones. This approach facilitates teachers learn about the overall difficulty of a SPOC video for students in real time, and therefore knowledge points can be explained more effectively in a flipped classroom.
ER -