research-article

Student Interactions in Online Discussion Forums: Visual Analysis with LDA Topic Models

Authors:

Taoufiq Zarra,

Raddouane Chiheb,

Rdouan Faizi,

Abdellatif El AfiaAuthors Info & Claims

LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

Article No.: 30, Pages 1 - 5

https://doi.org/10.1145/3230905.3230920

Published: 02 May 2018 Publication History

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Abstract

Virtual collaboration is intuitive and highly developed for most students attending today's schools. Our contribution aims to analysis, using the machine learning technique, some pedagogical benefits of discussions forums for the teaching staff, to help identify keys successes or weakness in courses assimilation. For this purpose, we use a variant of the probabilistic model of latent Dirichlet allocation (LDA) to ensure a better visual supervision of the topics discussed by the students. We will present our platform and discuss the obtained results.

References

[1]

Blei David M., NG Andrew Y., and Jordan Michael I. 2003. Latent dirichlet allocation. Journal of machine Learning research, 3 (Jan. 2003), 993--1022.

Digital Library

Google Scholar

[2]

Higgins Steve J.2003. Does ICT improve learning and teaching in schools?. BERA, British Educational Research Association.

Google Scholar

[3]

Friedman Philip, Friedman Victor, and Nevin James. 2002. Interactive online learning with student-to-tutor matching. U.S. Patent Application, (Jan. 2002). Patent No. 09/908,208.

Google Scholar

[4]

Mazza, Riccardo and Milani Christian. 2004. Gismo: a graphical interactive student monitoring tool for course management systems. In: International Conference on Technology Enhanced Learning, Milan, 1--8.

Google Scholar

[5]

Davies Jo and Graff Martin. 2005. Performance in elearning: online participation and student grades. British Journal of Educational Technology, 36(July 2005), 657--663.

Google Scholar

[6]

Bolliger Doris U. and Supanakorn Supawan. 2011. Learning styles and student perceptions of the use of interactive online tutorials. British Journal of Educational Technology, 42(April 2011), 470--481.

Google Scholar

[7]

Thomas Emily H. and Galambos Nora. 2004. What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45, 251--269.

Crossref

Google Scholar

[8]

Wen Miaomiao, Yang Diyi, and Rose Carolyn. 2014. Sentiment Analysis in MOOC Discussion Forums: What does it tell us?. In: Educational data mining. (Jan. 2014)

Google Scholar

[9]

Siemens George and D Baker Ryan SJ. 2012. Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, (May 2012), 252--254.

Digital Library

Google Scholar

[10]

Baker Ryan Sjd and Yacef Kalina. 2009. The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(Oct. 2009), 3--17.

Google Scholar

[11]

Romero Cristóbal and Ventura Sebastián. 2010. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(July 2010), 601--618.

Digital Library

Google Scholar

[12]

Nagori Rohit and Aghila G. 2011. LDA based integrated document recommendation model for e-learning systems. In: Emerging Trends in Networks and Computer Communications (ETNCC), 2011 International Conference on. IEEE, (April 2011). 230--233.

Google Scholar

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Sumikawa YTakada AIchinose AMurakami AToyono YIkejiri RSakasegawa MSekine KYamauchi Y(2022)Online Discussion Transition Analysis for Group Learning Support2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00043(249-255)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00043
Zhang GZhu ZZhu SLiang RSun G(2022)Towards a better understanding of the role of visualization in online learning: A reviewVisual Informatics10.1016/j.visinf.2022.09.0026:4(22-33)Online publication date: Dec-2022
https://doi.org/10.1016/j.visinf.2022.09.002
Ahmad MJunus KSantoso H(2022)Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature reviewEducation and Information Technologies10.1007/s10639-022-11065-w27:8(11355-11410)Online publication date: 6-May-2022
https://doi.org/10.1007/s10639-022-11065-w

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LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

May 2018

357 pages

ISBN:9781450353045

DOI:10.1145/3230905

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2018

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LOPAL '18: Theory and Applications

May 2 - 5, 2018

Rabat, Morocco

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LOPAL '18 Paper Acceptance Rate 61 of 141 submissions, 43%;

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Sumikawa YTakada AIchinose AMurakami AToyono YIkejiri RSakasegawa MSekine KYamauchi Y(2022)Online Discussion Transition Analysis for Group Learning Support2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00043(249-255)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00043
Zhang GZhu ZZhu SLiang RSun G(2022)Towards a better understanding of the role of visualization in online learning: A reviewVisual Informatics10.1016/j.visinf.2022.09.0026:4(22-33)Online publication date: Dec-2022
https://doi.org/10.1016/j.visinf.2022.09.002
Ahmad MJunus KSantoso H(2022)Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature reviewEducation and Information Technologies10.1007/s10639-022-11065-w27:8(11355-11410)Online publication date: 6-May-2022
https://doi.org/10.1007/s10639-022-11065-w

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LDA Analyzer: A Tool for Exploring Topic Models

Heterogeneous-Length Text Topic Modeling for Reader-Aware Multi-Document Summarization

A topic modeled unsupervised approach to single document extractive text summarization

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