|1/2021 - 8|
Continuous Student Knowledge Tracing Using SVD and Concept MapsTEODORESCU, O. M. , POPESCU, P. S. , MOCANU, L. M. , MIHAESCU, M. C.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,420 KB) | Citation | Downloads: 47 | Views: 52|
data preprocessing, distance learning, knowledge representation, human computer interaction, recommender systems
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
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|
| 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] [SCOPUS Times Cited 1]
 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 4]
 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 2] [SCOPUS Times Cited 3]
 D. D. Burdescu, M.C. Mihaescu, "TESYS: e-Learning Application Built on a Web Platform," ICE-B, 315-318, 2006.
 P. P. M. de Rijk, "A one-sided Jacobi algorithm for computing the singular value decomposition on a vector computer," SIAM Journal on Scientific and Statistical Computing, pp. 359-371, 1989.
[CrossRef] [Web of Science Times Cited 33]
 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 10] [SCOPUS Times Cited 15]
 W. J. van der Linden, R. K. Hambleton, "Handbook of Modern Item Response Theory," Springer Science & Business Media, pp. 51-65, 2013.
 T. Z. H. T. Petra and J. A. A. Moh, "Investigating reliability and validity of student performance assessment in Higher Education using Rasch Model," In Journal of Physics: Conference Series, vol. 1529, no. 4, IOP Publishing, 2020.
[CrossRef] [SCOPUS Times Cited 1]
 F. MartÃnez-Plumed, R. B. Prudencio, A. MartÃnez-Uso, J. Hernandez-Orallo, "Making sense of item response theory in machine learning," In Proceedings of the Twenty-second European Conference on Artificial Intelligence, pp. 1140-1148, 2016.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 19]
 Z. Ren, X. Ning, H. Rangwala, "Grade prediction with temporal course-wise influence," Proceedings of the 10th International Conference on Educational Data Mining, arXiv: 1709.05433, 2017
 A. Segal, K. Gal, G. Shani, B. Shapira, "A difficulty ranking approach to personalization in E-learning," International Journal of Human-Computer Studies, pp. 261-272, 2019.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 10]
 T. Barnes, "The Q-matrix method: Mining student response data for knowledge," In American Association for Artificial Intelligence 2005 Educational Data Mining Workshop, Pittsburgh, PA: AAAI Press, pp. 1-8, 2005
 K. K. Tatsuoka, "Rule space: An approach for dealing with misconceptions based on item response theory," Journal of educational measurement, vol. 20, no. 4, pp. 345-354, 1983.
[CrossRef] [Web of Science Times Cited 354] [SCOPUS Times Cited 455]
 A. K. Menon, E. Charles, "Fast algorithms for approximating the singular value decomposition," ACM Transactions on Knowledge Discovery from Data, 2011, pp. 1-36,
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 31]
 X. Yuan, L. Han, S. Qian, G. Xu, H. Yan, "Singular value decomposition based recommendation using imputed data," Knowledge-Based Systems, Vol. 163, pp. 485-494, 2019.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 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.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]
 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.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10]
 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.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 13]
 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.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10]
 S. Cooper, S. Mehran, "Reflections on stanford's moocs," Communications of the ACM, pp. 28-30, 2013.
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 112]
 M. Singh, "Scalability and sparsity issues in recommender datasets: a survey," Knowledge and Information Systems, pp. 1-43, 2020.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 11]
 Y. Tai, J. Yang, L. Luo, F. Zhang, J. Qian, "Learning discriminative singular value decomposition representation for face recognition," Pattern Recognition, pp. 1-16, 2016.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 12]
 A. T. Corbett, J. R. Anderson, "Knowledge tracing: Modeling the acquisition of procedural knowledge," User modeling and user-adapted interaction, pp. 253-278, 1994.
[CrossRef] [SCOPUS Times Cited 841]
 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.
[CrossRef] [SCOPUS Times Cited 189]
 W. J. Hawkins, N. T. Heffernan, R. S. J. D. Baker, "Learning Bayesian knowledge tracing parameters with a knowledge heuristic and empirical probabilities," International Conference on Intelligent Tutoring Systems. Springer, Cham, 2014.
[CrossRef] [SCOPUS Times Cited 18]
 J. Beck, "Difficulties in inferring student knowledge from observations (and why you should care)," Educational Data Mining: Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education, 2007.
 J. D. Novak, D. B. Gowin, "Learning how to learn," Cambridge University Press, 1984.
 A. Grubisic, et al. "Knowledge tracking variables in intelligent tutoring systems," Proceedings of the 9th International Conference on Computer Supported Education-CSEDU, Vol. 1, 2017.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]
 A. Trifa, H. Aroua, L. C. Wided, "Knowledge tracing with an intelligent agent, in an e-learning platform," Education and Information Technologies, 2019.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]
 L. Blerina, K. Kolomvatsos, S. Hadjiefthymiades, "Facing the cold start problem in recommender systems," Expert Systems with Applications, pp. 2065-2073, 2014.
[CrossRef] [Web of Science Times Cited 211] [SCOPUS Times Cited 288]
 P. Jung Yeon, S.-H. Joo, F. Cornillie, H. L. J. van der Maas, W. van den Noortgate "An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments," Behavior Research Methods, no. 2, pp. 895-909, 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]
Web of Science® Citations for all references: 824 TCR
SCOPUS® Citations for all references: 2,088 TCR
Web of Science® Average Citations per reference: 26 ACR
SCOPUS® Average Citations per reference: 65 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 2021-05-01 22:14 in 173 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.