With the undergoing technological revolution in education, adapting recommender systems to the personalized e-learning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.
| Published in | International Journal of Intelligent Information Systems (Volume 8, Issue 1) |
| DOI | 10.11648/j.ijiis.20190801.13 |
| Page(s) | 12-17 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
E-Learning, Recommender, Personalized
| [1] | Aher, S. B. & Lobo, L. (2013), ‘Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data’, Knowledge- Based Systems 51, 1–14. |
| [2] | Al-Aubidy, K. M. (2005), ‘Applying fuzzy logic for learner modeling and decision support in online learning systems’, Journal of Educational Technology 2(3), 76–85. |
| [3] | Almohammadi, K., Hagras, H., Yao, B., Alzahrani, A., Alghazzawi, D. & Aldabbagh, G. (2017), ‘A type-2 fuzzy logic recommendation system for adaptive teaching’, Soft Computing 21(4), 965–979. |
| [4] | Almohammadi, K., Yao, B. & Hagras, H. (2014), An in- terval type-2 fuzzy logic based system with user en- gagement feedback for customized knowledge deliv- ery within intelligent e-learning platforms, in ‘Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Con- ference on’, IEEE, pp. 808–817. |
| [5] | Amra, I. A. A. & Maghari, A. Y. (2017), Students per- formance prediction using knn and naïve bayesian, in ‘Information Technology (ICIT), 2017 8th International Conference on’, IEEE, pp. 909–913. |
| [6] | Bailey, C. P. (2002), An Agent-Based Framework to Sup- port Adaptive Hypermedia, PhD thesis, University of Southampton. |
| [7] | Bernard, J., Chang, T.-W., Popescu, E. & Graf, S. (2015), Using artificial neural networks to identify learning styles, in ‘International Conference on Artificial Intelli- gence in Education’, Springer, pp. 541–544. |
| [8] | Bernard, J., Chang, T.-W., Popescu, E. & Graf, S. (2016), Improving learning style identification by considering different weights of behavior patterns using particle swarm optimization, in ‘State-of-the-Art and Future Directions of Smart Learning’, Springer, pp. 39–49. |
| [9] | Bohl, O., Scheuhase, J., Sengler, R. & Winand, U. (2002), The sharable content object reference model (scorm)-a critical review, in ‘Computers in education, 2002. pro- ceedings. international conference on’, IEEE, pp. 950– 951. |
| [10] | Bourkoukou, O., El Bachari, E. & El Adnani, M. (2016), ‘A personalized e-learning based on recommender sys- tem’, International Journal of Learning and Teaching 2(2), 99–103. |
| [11] | Brezavšček, A., Bach, M. P. & Baggia, A. (2017), ‘Markov analysis of students’ performance and aca- demic progress in higher education’, Organizacija 50(2), 83–95. |
| [12] | Brusilovsky, P. (2001), Adaptive educational hypermedia, in ‘International PEG Conference’, Vol. 10, pp. 8–12. |
| [13] | Capraro, R. M. & Capraro, M. M. (2002), ‘Myers-briggs type indicator score reliability across: Studies a meta- analytic reliability generalization study’, Educational and Psychological Measurement 62(4), 590–602. |
| [14] | Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M. & Lee, J.-H. (2006), Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system, in ‘International Conference on Intelligent Tutoring Systems’, Springer, pp. 513–524. |
| [15] | Chen, L. & Sycara, K. (1998), Webmate: A personal agent for browsing and searching, in ‘Proceedings of the second international conference on Autonomous agents’, ACM, pp. 132–139. |
| [16] | Cheung, K.-W., Kwok, J. T., Law, M. H. & Tsui, K.-C. (2003), ‘Mining customer product ratings for personal- ized marketing’, Decision Support Systems 35(2), 231– 243. |
| [17] | d Baker, R. S., Corbett, A. T. & Aleven, V. (2008), More accurate student modeling through contextual esti- mation of slip and guess probabilities in bayesian knowledge tracing, in ‘Intelligent Tutoring Systems’, Springer, pp. 406–415. |
| [18] | De Bra, P., Santic, T. & Brusilovsky, P. (2003), Aha! meets interbook, and more..., in ‘E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education’, Association for the Advancement of Computing in Education (AACE), pp. 57–64. |
| [19] | Ding, L., Liu, B. & Tao, Q. (2010), Hybrid filtering recommendation in e-learning environment, in ‘2010 Second International Workshop on Education Technology and Computer Science’, IEEE, pp. 177–180. |
| [20] | Dunn, R. (1990), ‘Rita dunn answers questions on learning styles’, Educational Leadership 48(2), 15–19. |
| [21] | Eklund, J. & Brusilovsky, P. (1999), ‘Interbook: an adaptive tutoring system’, UniServe Science News 12(3), 8– 13. |
| [22] | Felder, R. M., Silverman, L. K. et al. (1988), ‘Learning and teaching styles in engineering education’, Engineering education 78(7), 674–681. |
| [23] | Ferrandino, S., Negro, A. & Scarano, V. (1997), Cheops: Adaptive hypermedia on world wide web, in ‘International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services’, Springer, pp. 210–219. |
| [24] | Graf, S., Viola, S. R., Leo, T. & Kinshuk (2007), ‘In-depth analysis of the felder-silverman learning style dimen- sions’, Journal of Research on Technology in Education 40(1), 79–93. |
| [25] | Hmedna, B., El Mezouary, A., Baz, O. & Mammass, D. (2017), ‘Identifying and tracking learning styles in moocs: A neural networks approach’, International Journal of Innovation and Applied Studies 19(2), 267. |
| [26] | Hohl, H., Boecker, H.-D. & Gunzenhaeuser, R. (1998), Hypadapter: An adaptive hypertext system for ex- ploratory learning and programming, in ‘Adaptive Hy- pertext and Hypermedia’, Springer, pp. 117–142. |
| [27] | Howard, R. A., Carver, C. A. & Lane, W. D. (1996), Felder’s learning styles, bloom’s taxonomy, and the kolb learning cycle: tying it all together in the cs2 course, in ‘ACM SIGCSE Bulletin’, Vol. 28, ACM, pp. 227–231. |
| [28] | Kazanidis, I. & Satratzemi, M. (2007), Combine adaptive education hypermedia and scorm compliant learning management systems, in ‘Advanced Learning Technolo- gies, 2007. ICALT 2007. Seventh IEEE International Conference on’, IEEE, pp. 330–331. |
| [29] | Lu, J. (2004), A personalized e-learning material rec- ommender system, in ‘International Conference on Information Technology and Applications’, Macquarie Scientific Publishing. |
| [30] | Mladenic, D. & Webwatcher, B. A. P. (1999), ‘Machine learning used by personal webwatcher’. |
| [31] | Nguyen, H. & Liew, C. W. (n.d.), ‘Using student logs to build bayesian models of student knowledge and skills’. |
| [32] | Nichols, M. (2003), ‘A theory for elearning’, Educational technology & society 6(2), 1–10. |
| [33] | Nolan, J. R. (1998), ‘A prototype application of fuzzy logic and expert systemsin education assessment’, IAAI-98 Proceedings 1134. |
| [34] | Ogwoka, T. M., Cheruiyot, W. & Okeyo, G. (2015), ‘A model for predicting students’ academic performance using a hybrid k-means and decision tree algorithms’, International Journal of Computer Applications Technol- ogy and Research 4(9), 693–697. |
| [35] | Özpolat, E. & Akar, G. B. (2009), ‘Automatic detection of learning styles for an e-learning system’, Computers & Education 53(2), 355–367. |
| [36] | Pikrakis, A., Bitsikas, T., Sfakianakis, S., Hatzopoulos, M., DeRoure, D., Hall, W., Reich, S., Hill, G. & Stairmand, M. (1998), Memoir-software agents for finding similar users by trails, in ‘PAAM98-The Third International Conference and Exhibition on The Practical Application of Intelligent Agents and Multi-Agents. Nwana HS and. Ndumu D. T (Eds.), London, UK’, pp. 453–466. |
| [37] | Popescu, E. (2009), Diagnosing students’ learning style in an educational hypermedia system, in ‘Cognitive and emotional processes in Web-based education: Integrating human factors and personalization’, IGI Global, pp. 187–208. |
| [38] | PSLC DataShop (n. d.), https://pslcdatashop.web. cmu.edu. Accessed: 2018-09-22. |
| [39] | Qu, C. & Nejdl, W. (2002), Towards interoperability and reusability of learning resources: A scorm-conformant courseware for computer science education, in ‘Proc. of the 2nd IEEE International Conference on Advanced Learning Technologies (IEEE ICALT 2002), Kazan, Tatarstan, Russia’. |
| [40] | Shishehchi, S., Banihashem, S. Y., Zin, N. A. M. & Noah, S. A. M. (2011), Review of personalized recommendation techniques for learners in e-learning systems, in ‘Se- mantic Technology and Information Retrieval (STAIR), 2011 International Conference on’, IEEE, pp. 277–281. |
| [41] | Smith, P. & Dalton, J. (2005), Getting to Grips with Learn- ing Styles., ERIC. |
| [42] | Spaulding, S. & Breazeal, C. (2015), Affect and infer- ence in bayesian knowledge tracing with a robot tutor, in ‘Proceedings of the Tenth Annual ACM/IEEE In- ternational Conference on Human-Robot Interaction Extended Abstracts’, ACM, pp. 219–220. |
| [43] | Sugarman, L. (1985), ‘Kolb’s model of experiential learn- ing: Touchstone for trainers, students, counselors, and clients’, Journal of Counseling & Development 64(4), 264–268. |
| [44] | Wantz, L. J. & Miller, M. (1997), Toward user-centric navigation of the web: Cool links using spi, in ‘Pro- ceedings of the Sixth International World Wide Web Conference’. |
| [45] | Weber, G. & Brusilovsky, P. (2001), ‘Elm-art: An adap- tive versatile system for web-based instruction’, Inter- national Journal of Artificial Intelligence in Education (IJAIED) 12, 351–384. |
| [46] | Yang, F., Han, P., Shen, R. & Hu, Z. (2005), A novel resource recommendation system based on connecting to similar e-learners, in ‘International Conference on Web-Based Learning’, Springer, pp. 122–130. |
APA Style
Karim Moharm. (2019). A Framework for Adaptive Personalized E-learning Recommender Systems. International Journal of Intelligent Information Systems, 8(1), 12-17. https://doi.org/10.11648/j.ijiis.20190801.13
ACS Style
Karim Moharm. A Framework for Adaptive Personalized E-learning Recommender Systems. Int. J. Intell. Inf. Syst. 2019, 8(1), 12-17. doi: 10.11648/j.ijiis.20190801.13
AMA Style
Karim Moharm. A Framework for Adaptive Personalized E-learning Recommender Systems. Int J Intell Inf Syst. 2019;8(1):12-17. doi: 10.11648/j.ijiis.20190801.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijiis.20190801.13,
author = {Karim Moharm},
title = {A Framework for Adaptive Personalized E-learning Recommender Systems},
journal = {International Journal of Intelligent Information Systems},
volume = {8},
number = {1},
pages = {12-17},
doi = {10.11648/j.ijiis.20190801.13},
url = {https://doi.org/10.11648/j.ijiis.20190801.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20190801.13},
abstract = {With the undergoing technological revolution in education, adapting recommender systems to the personalized e-learning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.},
year = {2019}
}
TY - JOUR T1 - A Framework for Adaptive Personalized E-learning Recommender Systems AU - Karim Moharm Y1 - 2019/03/25 PY - 2019 N1 - https://doi.org/10.11648/j.ijiis.20190801.13 DO - 10.11648/j.ijiis.20190801.13 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 12 EP - 17 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20190801.13 AB - With the undergoing technological revolution in education, adapting recommender systems to the personalized e-learning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed. VL - 8 IS - 1 ER -
Information
Email: