Advisor-Oriented Course Recommendation System Using Student Grades
Main Article Content
Abstract
In some universities, student advisors are often hired to enhance students’ retention rate. Having some students in mind, these advisors may find some difficulties in guiding the students in terms of selecting relevant courses. This paper proposes an advisor-oriented course recommendation system. Using this system, the advisors may suggest relevant courses to their students easier and more accurate. This system relies on student grades and comprehensive course data. Further, it utilises content-based and collaborative filtering for predicting relevant courses. According to our evaluation, the system is considerably effective; the accuracy of content-based filtering is about 66% while the accuracy of collaborative filtering is about 58%. Further, some parameters may be potential for enhancing accuracy while the others may be not.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Please find the rights and licenses in the Journal of Information Technology and Computer Engineering (JITCE).
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)’ Warranties
The author(s) warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary permissions to quote from other sources have been obtained by the author(s).
3. User Rights
JITCE adopts the spirit of open access and open science, which disseminates articles published as free as possible under the Creative Commons license. JITCE permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and JITCE on distributing works in the journal.
4. Rights of Authors
Authors retain the following rights:
- Copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in future own works, including lectures and books,
- the right to reproduce the article for own purposes,
- the right to self-archive the article.
- the right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Journal of Information Technology and Computer Engineering).
5. Co-Authorship
If the article was jointly prepared by other authors; upon submitting the article, the author is agreed on this form and warrants that he/she has been authorized by all co-authors on their behalf, and agrees to inform his/her co-authors. JITCE will be freed on any disputes that will occur regarding this issue.
7. Royalties
By submitting the articles, the authors agreed that no fees are payable from JITCE.
8. Miscellaneous
JITCE will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed and JITCE or its sublicensee has become obligated to have the article published. JITCE may adjust the article to a style of punctuation, spelling, capitalization, referencing and usage that it deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers.
References
[2] K. S. Hone and G. R. El Said, “Exploring the factors affecting MOOC retention: A survey study,” Computers & Education, vol. 98, pp. 157–168, Jul. 2016, doi: 10.1016/J.COMPEDU.2016.03.016.
[3] S. de Freitas et al., “Foundations of dynamic learning analytics: Using university student data to increase retention,” British Journal of Educational Technology, vol. 46, no. 6, pp. 1175–1188, Nov. 2015, doi: 10.1111/bjet.12212.
[4] J. D. Cochran, S. M. Campbell, H. M. Baker, and E. M. Leeds, “The Role of Student Characteristics in Predicting Retention in Online Courses,” Research in Higher Education, vol. 55, no. 1, pp. 27–48, Feb. 2014, doi: 10.1007/s11162-013-9305-8.
[5] K. Kimbark, M. L. Peters, and T. Richardson, “Effectiveness of the Student Success Course on Persistence, Retention, Academic Achievement, and Student Engagement,” Community College Journal of Research and Practice, vol. 41, no. 2, pp. 124–138, Feb. 2017, doi: 10.1080/10668926.2016.1166352.
[6] S. Zheng, K. Han, M. B. Rosson, and J. M. Carroll, “The Role of Social Media in MOOCs: How to Use Social Media to Enhance Student Retention,” in Proceedings of the Third (2016) ACM Conference on Learning @ Scale - L@S ’16, 2016, pp. 419–428, doi: 10.1145/2876034.2876047.
[7] E. Elvina, O. Karnalim, M. Ayub, and M. C. Wijanto, “Combining program visualization with programming workspace to assist students for completing programming laboratory task,” Journal of Technology and Science Education, vol. 8, no. 4, p. 268, Jun. 2018, doi: 10.3926/jotse.420.
[8] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, pp. 109–132, Jul. 2013, doi: 10.1016/J.KNOSYS.2013.03.012.
[9] W. Carrer-Neto, M. L. Hernández-Alcaraz, R. Valencia-García, and F. García-Sánchez, “Social knowledge-based recommender system. Application to the movies domain,” Expert Systems with Applications, vol. 39, no. 12, pp. 10990–11000, Sep. 2012, doi: 10.1016/J.ESWA.2012.03.025.
[10] E. R. Núñez-Valdéz, J. M. Cueva Lovelle, O. Sanjuán Martínez, V. García-Díaz, P. Ordoñez de Pablos, and C. E. Montenegro Marín, “Implicit feedback techniques on recommender systems applied to electronic books,” Computers in Human Behavior, vol. 28, no. 4, pp. 1186–1193, Jul. 2012, doi: 10.1016/J.CHB.2012.02.001.
[11] J. J. Castro-Schez, R. Miguel, D. Vallejo, and L. M. López-López, “A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals,” Expert Systems with Applications, vol. 38, no. 3, pp. 2441–2454, Mar. 2011, doi: 10.1016/J.ESWA.2010.08.033.
[12] D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, “A content-based recommender system for computer science publications,” Knowledge-Based Systems, vol. 157, pp. 1–9, Oct. 2018, doi: 10.1016/J.KNOSYS.2018.05.001.
[13] J. Bobadilla, F. Serradilla, and A. Hernando, “Collaborative filtering adapted to recommender systems of e-learning,” Knowledge-Based Systems, vol. 22, no. 4, pp. 261–265, May 2009, doi: 10.1016/J.KNOSYS.2009.01.008.
[14] J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decision Support Systems, vol. 74, pp. 12–32, Jun. 2015, doi: 10.1016/J.DSS.2015.03.008.
[15] C. Porcel, A. Tejeda-Lorente, M. A. Martínez, and E. Herrera-Viedma, “A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office,” Information Sciences, vol. 184, no. 1, pp. 1–19, Feb. 2012, doi: 10.1016/J.INS.2011.08.026.
[16] J. Salter and N. Antonopoulos, “CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering,” IEEE Intelligent Systems, vol. 21, no. 1, pp. 35–41, Jan. 2006, doi: 10.1109/MIS.2006.4.
[17] X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, vol. 2009, pp. 1–19, Oct. 2009, doi: 10.1155/2009/421425.
[18] A. B. Barragáns-Martínez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-López, F. A. Mikic-Fonte, and A. Peleteiro, “A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition,” Information Sciences, vol. 180, no. 22, pp. 4290–4311, Nov. 2010, doi: 10.1016/J.INS.2010.07.024.
[19] M. G. Vozalis and K. G. Margaritis, “Using SVD and demographic data for the enhancement of generalized Collaborative Filtering,” Information Sciences, vol. 177, no. 15, pp. 3017–3037, Aug. 2007, doi: 10.1016/J.INS.2007.02.036.
[20] T. Lee, J. Chun, J. Shim, and S. Lee, “An Ontology-Based Product Recommender System for B2B Marketplaces,” International Journal of Electronic Commerce, vol. 11, no. 2, pp. 125–155, Dec. 2006, doi: 10.2753/JEC1086-4415110206.
[21] A. Nanopoulos, D. Rafailidis, P. Symeonidis, and Y. Manolopoulos, “MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 2, pp. 407–412, Feb. 2010, doi: 10.1109/TASL.2009.2033973.
[22] L. Terán and A. Meier, “A Fuzzy Recommender System for eElections,” in International Conference on Electronic Government and the Information Systems Perspective, 2010, pp. 62–76, doi: 10.1007/978-3-642-15172-9_6.
[23] C. Cobos et al., “A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes,” Information Processing & Management, vol. 49, no. 3, pp. 607–625, May 2013, doi: 10.1016/J.IPM.2012.12.002.
[24] M. C. Wijanto, R. Rachmadiany, and O. Karnalim, “Thesis Supervisor Recommendation with Representative Content and Information Retrieval,” Journal of Information Systems Engineering and Business Intelligence, vol. 6, no. 2, 2020, doi: http://dx.doi.org/10.20473/jisebi.6.2.143-150.
[25] C. Porcel and E. Herrera-Viedma, “Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries,” Knowledge-Based Systems, vol. 23, no. 1, pp. 32–39, Feb. 2010, doi: 10.1016/J.KNOSYS.2009.07.007.
[26] S. Schiaffino and A. Amandi, “Building an expert travel agent as a software agent,” Expert Systems with Applications, vol. 36, no. 2, pp. 1291–1299, Mar. 2009, doi: 10.1016/J.ESWA.2007.11.032.
[27] T. T. S. Nguyen, Hai Yan Lu, and Jie Lu, “Web-Page Recommendation Based on Web Usage and Domain Knowledge,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2574–2587, Oct. 2014, doi: 10.1109/TKDE.2013.78.
[28] J. A. Recio-Garcia, G. Jimenez-Diaz, A. A. Sanchez-Ruiz, and B. Diaz-Agudo, “Personality aware recommendations to groups,” in Proceedings of the third ACM conference on Recommender systems - RecSys ’09, 2009, p. 325, doi: 10.1145/1639714.1639779.
[29] R. Farzan and P. Brusilovsky, “Encouraging user participation in a course recommender system: An impact on user behavior,” Computers in Human Behavior, vol. 27, no. 1, pp. 276–284, Jan. 2011, doi: 10.1016/J.CHB.2010.08.005.
[30] R. Farzan and P. Brusilovsky, “Social Navigation Support in a Course Recommendation System,” in International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 2006, pp. 91–100, doi: 10.1007/11768012_11.
[31] C.-M. Chen, H.-M. Lee, and Y.-H. Chen, “Personalized e-learning system using Item Response Theory,” Computers & Education, vol. 44, no. 3, pp. 237–255, Apr. 2005, doi: 10.1016/J.COMPEDU.2004.01.006.
[32] X. Li, T. Wang, H. Wang, and J. Tang, “Understanding User Interests Acquisition in Personalized Online Course Recommendation,” in Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, Jul. 2018, pp. 230–242, doi: 10.1007/978-3-030-01298-4_20.
[33] J. Xiao, M. Wang, B. Jiang, and J. Li, “A personalized recommendation system with combinational algorithm for online learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 3, pp. 667–677, Jun. 2018, doi: 10.1007/s12652-017-0466-8.
[34] M. P. O’Mahony and B. Smyth, “A recommender system for on-line course enrolment: an initial study,” in Proceedings of the 2007 ACM conference on Recommender systems - RecSys ’07, 2007, p. 133, doi: 10.1145/1297231.1297254.
[35] M. K. Khribi, M. Jemni, and O. Nasraoui, “Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval,” in 2008 Eighth IEEE International Conference on Advanced Learning Technologies, 2008, pp. 241–245, doi: 10.1109/ICALT.2008.198.
[36] K. I. Bin Ghauth and N. A. Abdullah, “Building an E-learning Recommender System Using Vector Space Model and Good Learners Average Rating,” in 2009 Ninth IEEE International Conference on Advanced Learning Technologies, Jul. 2009, pp. 194–196, doi: 10.1109/ICALT.2009.161.
[37] A. Klašnja-Milićević, B. Vesin, M. Ivanović, and Z. Budimac, “E-Learning personalization based on hybrid recommendation strategy and learning style identification,” Computers & Education, vol. 56, no. 3, pp. 885–899, Apr. 2011, doi: 10.1016/J.COMPEDU.2010.11.001.
[38] D. Wen‐Shung Tai, H. Wu, and P. Li, “Effective e‐learning recommendation system based on self‐organizing maps and association mining,” The Electronic Library, vol. 26, no. 3, pp. 329–344, Jun. 2008, doi: 10.1108/02640470810879482.
[39] Y. Shinyama, “PDF Miner,” 2021. https://github.com/euske/pdfminer.
[40] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, no. Oct, pp. 2825–2830, 2011.
[41] H. A. Robbani, “PySastrawi,” 2021. https://github.com/har07/PySastrawi.
[42] C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval. Cambridge University Press, 2008.
[43] T. Mitchell, Machine Learning. McGraw-Hill Education, 1997.
[44] G. J. McLachlan, K.-A. Do, and C. Ambroise, Analyzing Microarray Gene Expression Data. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004.
[45] Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol. 12, no. 2, pp. 153–157, 1947.
[46] F. C. Jonathan and O. Karnalim, “Semi-Supervised Keyphrase Extraction on Scientific Article using Fact-based Sentiment,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 16, no. 4, pp. 1771–1778, Aug. 2018, doi: 10.12928/TELKOMNIKA.V16I3.5473.