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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2021 - 8

Continuous Student Knowledge Tracing Using SVD and Concept Maps

TEODORESCU, O. M. See more information about TEODORESCU, O. M. on SCOPUS See more information about TEODORESCU, O. M. on IEEExplore See more information about TEODORESCU, O. M. on Web of Science, POPESCU, P. S. See more information about  POPESCU, P. S. on SCOPUS See more information about  POPESCU, P. S. on SCOPUS See more information about POPESCU, P. S. on Web of Science, MOCANU, L. M. See more information about  MOCANU, L. M. on SCOPUS See more information about  MOCANU, L. M. on SCOPUS See more information about MOCANU, L. M. on Web of Science, MIHAESCU, M. C. See more information about MIHAESCU, M. C. on SCOPUS See more information about MIHAESCU, M. C. on SCOPUS See more information about MIHAESCU, M. C. on Web of Science
 
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Download PDF pdficon (1,420 KB) | Citation | Downloads: 790 | Views: 1,862

Author keywords
data preprocessing, distance learning, knowledge representation, human computer interaction, recommender systems

References keywords
knowledge(14), systems(12), learning(11), response(6), education(6), data(6), theory(5), recommender(5), item(5), tracing(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-02-28
Volume 21, Issue 1, Year 2021, On page(s): 75 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.01008
Web of Science Accession Number: 000624018800008
SCOPUS ID: 85102791484

Abstract
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One of the critical aspects of building intelligent tutoring systems regards proper monitoring of student's activity and academic performance. This paper presents a continuous student knowledge tracing method implemented for Tesys e-Learning platform at the Faculty of Automation, Computers and Electronics in the University of Craiova. The student's knowledge level is continuously monitored and, after each recommended test by the SVD-based mechanism, a new set of knowledge weights are computed. We aim to achieve a comprehensive monitoring environment which can provide an accurate insight upon the student's knowledge level at any moment. In our approach, we added weights for both students and tests to improve the student's evolution monitoring and provide more accurate feedback. The setup for validation consisted of ten tests with eight questions per test and we used both current and past year tests data. Results revealed that assigning weights to questions, tests and students and using them in the recommendation process offers a better view of the student's evolution along with more accurate recommendations. Progress in this direction will provide more insight into available teaching materials and SVD-based recommender system such that the e-learning platform that integrates the presented mechanism will provide a better learning experience.


References | Cited By  «-- Click to see who has cited this paper

[1] O. Teodorescu, S. P. Popescu, M. Mocanu and M. C. Mihaescu, "Custom Validation Procedure for Tesys Recommender System," International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, pp. 1-6, 2019.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[2] C. M. Mihaescu, O. M. Teodorescu, P. S. Popescu and M. L. Mocanu, "Learning analytics solution for building personalized quiz sessions," 18th International Carpathian Control Conference (ICCC), Sinaia, 2017, pp. 140-145, 2017.
[CrossRef] [SCOPUS Times Cited 5]


[3] O. Teodorescu, P. Popescu, C. Mihaescu, "Taking e-assessment quizzes - A case study with an SVD based recommender system," 19th International Conference, Proceedings, Part I. Madrid, Spain, November 21-23, 2018.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 42]


[6] K. Pliakos, S. H. Joo, Y. Y. Park, F. Cornillie, C. Vens, W. Van den Noortgate, "Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems," Computers & Education, pp. 91-103, 2019.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 72]


[7] W. J. van der Linden, R. K. Hambleton, "Handbook of Modern Item Response Theory," Springer Science & Business Media, pp. 51-65, 2013.
[CrossRef]


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[16] D. Ben-Shimon, L. Rokach, B. Shapira, "An ensemble method for top-N recommendations from the SVD," Expert Systems with Applications, pp. 84-92, 2016.
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[17] A. N. Nikolakopoulos, V. Kalanzis, E. Gallopoulos, J. D. Garofalakis "EigenRec: generalizing PureSVD for effective and efficient top-N recommendations," Knowledge and Information Systems, Vol. 58, pp. 59-81, 2019.
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[18] P. Matuszyk, J. Vinagre, M. Spiliopoulou, A. Jorge, J. Gama, "Forgetting techniques for stream-based matrix factorization in recommender systems," Knowledge and Information Systems, pp. 275-304, 2017.
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[19] L. Oscar, J. Diez, A. Bahamonde, "A peer assessment method to provide feedback, consistent grading and reduce students' burden in massive teaching settings," Computers & Education, pp. 283-295, 2018.
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[24] R. S. J. Baker, A. T. Corbett, V. Aleven, "More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing," International Conference on Intelligent Tutoring Systems. Springer, Berlin, Heidelberg, 2008.
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[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 27]




References Weight

Web of Science® Citations for all references: 1,347 TCR
SCOPUS® Citations for all references: 3,617 TCR

Web of Science® Average Citations per reference: 42 ACR
SCOPUS® Average Citations per reference: 113 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-17 23:59 in 188 seconds.




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