CoChat
Sign Up
Cited by 41Intelligent Tutoring Systems and Adaptive LearningOnline Learning and AnalyticsLearning Styles and Cognitive Differences

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A proposed architectural learner model for a personalized learning environment

Open access
Bellarhmouch, Youssra, Jeghal, Adil, Tairi, Hamid, Benjelloun, Nadia·2023·Education and Information Technologies
DOI 10.1007/s10639-022-11392-yPMC PMC9568945
Open this paper in CoChat — free accountOpen PDF

Abstract

Nowadays, the need for e-learning is amplified, especially after the Covid-19 pandemic. E-learning platforms present a solution for the continuity of the learning process. Learners are using different platforms and tools for learning. For this, it is necessary to model the learner for the personalization of the learning environment according to his needs, and characteristics, which will allow having a more effective and efficient environment. The existing literature maintains that the learner model represents the basis and the key to adaptation. To achieve this goal, we propose a new adaptation aspect of the learner model by integrating relevant information such as learning style, domain-related data, assessment-related data, and affective data. It has advantages in terms of precision as it solves the problem of management uncertainty of some parameters. Our approach suggests that the combination of stereotype method, fuzzy logic, and similarity techniques is an appropriate approach for initializing and updating the learner model for learning personalization.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10639-022-11392-y

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 3 references

Research works printed but not linked: 70

  1. Abyaa, A., Khalidi Idrissi, M., & Bennani, S. (2019). Learner Modelling: Systematic Review of the Literature from the Last 5 Years. Educational Technology Research and Development, 67(5), 1105–1143. https://doi.org/10.1007/S11423-018-09644-1 · DOI
  2. Adel, N., Latham, A., Crockett, K. A. (2016). 2016 Intl IEEE conferences on, and undefined 2016. Towards socially intelligent automated tutors: Predicting learning style dimensions from conversational dialogue. In: In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp. 315–320. · DOI
  3. Aryal, S., Porawagama, A. S., Hasith, M. G. S., Thoradeniya, S. C., Kodagoda, N., Suriyawansa, K. (2019). MoocRec: Learning styles-oriented MOOC recommender and search engine. In: 2019 IEEE Global Engineering Education Conference (EDUCON), pp. 1167–1172. IEEE. · DOI
  4. Benmesbah, O., Lamia, M., & Hafidi, M. (2021). An Improved Constrained Learning Path Adaptation Problem Based on Genetic Algorithm. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1937659 · DOI
  5. Biswas, G., Rajendran, R., Mohammed, N., Goldberg, B. S., Sottilare, R. A., Brawner, K., & Hoffman, M. (2019). Multilevel Learner Modeling in Training Environments for Complex Decision Making. IEEE Transactions on Learning Technologies, 13(1), 172–185. · DOI
  6. Blakemore, T. F., McCray, P. M., & Coker, C. (1984). A Guide to Learning Style Assessment. Stout Vocational Rehabilitation Institute, University of Wisconsin-Stout.
  7. Brusilovsky, Peter, Millán, Eva. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4321 LNCS:3–53. https://doi.org/10.1007/978-3-540-72079-9_1. · DOI
  8. Carmona, C., Castillo, G., Millán, E. (2008). Designing a Dynamic Bayesian Network for Modeling Students’ Learning Styles. In 2008 eighth IEEE international conference on advanced learning technologies, (Pp. 346–350). IEEE. · DOI
  9. Carver, C. A., Howard, R. A., & Lane, W. D. (1999). Enhancing Student Learning through Hypermedia Courseware and Incorporation of Student Learning Styles. IEEE Transactions on Education, 42(1), 33–38. · DOI
  10. Castillo, G., Gama, J., Breda, A. M. (2006). Advances in Web-based education, and undefined 2006. An Adaptive Predictive Model for Student Modeling. In: Advances in Web-Based Education: Personalized Learning Environments 70–92. https://doi.org/10.4018/978-1-59140-690-7.ch004. · DOI
  11. Chang, Yi Chun, Li, Jian Wei, De Yao Huang. (2022). A Personalized Learning Service Compatible with Moodle E-Learning Management System. Applied Sciences, 12, 3562 12(7):3562. https://doi.org/10.3390/APP12073562. · DOI
  12. Chrysafiadi, K., & Virvou, M. (2013). Student Modeling Approaches: A Literature Review for the Last Decade. Expert Systems with Applications, 40(11), 4715–4729. https://doi.org/10.1016/J.ESWA.2013.02.007 · DOI
  13. Chrysafiadi, K., & Virvou, M. (2012). Evaluating the Integration of Fuzzy Logic into the Student Model of a Web-Based Learning Environment. Expert Systems with Applications, 39(18), 13127–13134. · DOI
  14. Clemente, J., Ramírez, J., & de Antonio, A. (2011). A Proposal for Student Modeling Based on Ontologies and Diagnosis Rules. Expert Systems with Applications, 38(7), 8066–8078. · DOI
  15. Clemente, J., Ramírez, J., & de Antonio, A. (2014). Applying a Student Modeling with Non-Monotonic Diagnosis to Intelligent Virtual Environment for Training/Instruction. Expert Systems with Applications, 41(2), 508–520. · DOI
  16. Conati, C., & MacLaren, H. (2009). Empirically Building and Evaluating a Probabilistic Model of User Affect. User Modeling and User-Adapted Interaction, 19(3), 267–303. https://doi.org/10.1007/S11257-009-9062-8 · DOI · Paper page
  17. De Koch, N. P. (2001). Software Engineering for Adaptive Hypermedia Systems. Verlag Uni-Druck.
  18. Faraco, R. A., Rosatelli, M. C., Gauthier, F. A. (2004). An Approach of Student Modelling in a Learning Companion System. In: Ibero-American Conference on Artificial Intelligence, (Pp. 891–900) .Springer. · DOI
  19. Faria, A. R., Almeida, A., Martins, C., Gonçalves, R., Martins, J., & Branco, F. (2017). A Global Perspective on an Emotional Learning Model Proposal. Telematics and Informatics, 34(6), 824–837. https://doi.org/10.1016/J.TELE.2016.08.007 · DOI
  20. Fasihuddin, H. A., Skinner, G. D., Athauda, R. I. (2014). Personalizing Open Learning Environments through the Adaptation to Learning Styles. ICITA. · DOI
  21. Felder, R. M., & Silverman, L. K. (1988). Learning and Teaching Styles in Engineering Education. Engineering Education, 78(7), 674–681.
  22. García, P., Schiaffino, S., & Amandi, A. (2008). An Enhanced Bayesian Model to Detect Students’ Learning Styles in Web-Based Courses: Original Article. Journal of Computer Assisted Learning, 24(4), 305–315. https://doi.org/10.1111/J.1365-2729.2007.00262.X · DOI
  23. Gaudioso, E., Montero, M., & Hernandez-del-Olmo, F. (2012). Supporting Teachers in Adaptive Educational Systems through Predictive Models: A Proof of Concept. Expert Systems with Applications, 39(1), 621–625. · DOI
  24. Grawemeyer, Beate, Mavrikis, Manolis, Holmes, Wayne, Gutierrez-Santos, Sergio. (2015) Adapting Feedback Types According to Students’ Affective States. In: International Conference on artificial intelligence in Education, (Vol. 9112, Pp. 586–590). Springer. · DOI
  25. Hammad, R., Odeh, M., Khan, Z. (2017). eLEM: A Novel e-Learner Experience Model. International Arab Journal of Information Technology (IAJIT) 14.
  26. Hernández, Yasmín, Cervantes-Salgado, Marilú, Pérez-Ramírez, Miguel, Mejía-Lavalle, Manuel. (2017) Data-Driven Construction of a Student Model Using Bayesian Networks in an Electrical Domain. In: Mexican International Conference on Artificial Intelligence, (10062 LNAI:481–490). https://doi.org/10.1007/978-3-319-62428-0_39. · DOI
  27. Huang, Y., Guerra-Hollstein, J., Barria-Pineda, J., Brusilovsky, P. (2017). Learner Modeling for Integration Skills. In: Proceedings of the 25th Conference on user modeling, (Pp. 85–93). Association for Computing Machinery, Inc. · DOI
  28. Imani, M., & Montazer, G. A. (2019). A Survey of Emotion Recognition Methods with Emphasis on E-Learning Environments. Journal of Network and Computer Applications, 147, 102423. https://doi.org/10.1016/J.JNCA.2019.102423 · DOI
  29. Javanbakht, A., Duval, E. R., Cisneros, M. E., Taylor, S. F., Kessler, D., & Liberzon, I. (2017). Instructed Fear Learning, Extinction, and Recall: Additive Effects of Cognitive Information on Emotional Learning of Fear. Cognition and Emotion, 31(5), 980–987. https://doi.org/10.1080/02699931.2016.1169997 · DOI
  30. Jeghal, A., Oughdir, L., Tairi, H., el Affar, A. N. A. S. (2013). A Model of the Reuse of the Profiles of Learners. Journal of Theoretical & Applied Information Technology 57(2).
  31. Jeremić, Z., Jovanović, J., & Gašević, D. (2012). Student Modeling and Assessment in Intelligent Tutoring of Software Patterns. Expert Systems with Applications, 39(1), 210–222. https://doi.org/10.1016/J.ESWA.2011.07.010 · DOI
  32. Kaya, Galip, Altun, Arif. (2011) A Learner Model for Learning Object Based Personalized Learning Environments. Communications in Computer and Information Science, 240CCIS, 349–355. https://doi.org/10.1007/978-3-642-24731-6_35. · DOI
  33. Kolb, D. A. (2014). Experiential Learning: Experience as the Source of Learning and Development. FT Press.
  34. Lee, Y., & Choi, J. (2011). A Review of Online Course Dropout Research: Implications for Practice and Future Research. Educational Technology Research and Development, 59(5), 593–618. https://doi.org/10.1007/S11423-010-9177-Y · DOI · Paper page
  35. Mawas, Nour el, Ghergulescu, Ioana, Moldovan, Arghir-Nicolae, Muntean, Cristina, Muntean, Cristina Hava. (2018). Pedagogical Based Learner Model Characteristics. In: Ireland International Conference on Education.
  36. Millán, E., Loboda, T., & Pérez-De-La-Cruz, J. L. (2010). Bayesian Networks for Student Model Engineering. Computers & Education, 55(4), 1663–1683. · DOI
  37. Murad, Dina Fitria, Heryadi, Yaya, Muhamad Isa, Sani, Budiharto, Widodo. (2020) Personalization of Study Material Based on Predicted Final Grades Using Multi-Criteria User-Collaborative Filtering Recommender System. Education and Information Technologies, 25:6 25(6):5655–5668. https://doi.org/10.1007/S10639-020-10238-9. · DOI
  38. Nguyen, L., & Do, P. (2008). Learner Model in Adaptive Learning. World Academy of Science, Engineering and Technology, 45(70), 395–400.
  39. Nguyen, L., & Do, P. (2009). Combination of Bayesian Network and Overlay Model in User Modeling. Springer, 5545, 5–14.
  40. Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., & Magoulas, G. D. (2003). Personalizing the Interaction in a Web-Based Educational Hypermedia System: The Case of INSPIRE. User Modelling and User-Adapted Interaction, 13(3), 213–267. https://doi.org/10.1023/A:1024746731130 · DOI
  41. Pelánek, R., Papoušek, J., Řihák, J., Stanislav, V., & Nižnan, J. (2017). Elo-Based Learner Modeling for the Adaptive Practice of Facts. User Modeling and User-Adapted Interaction, 27(1), 89–118. https://doi.org/10.1007/S11257-016-9185-7 · DOI
  42. Peña-Ayala, Alejandro, Sossa, Humberto. (2013). Proactive Sequencing Based on a Causal and Fuzzy Student Model. Intelligent and Adaptive Educational-Learning Systems49–76. https://doi.org/10.1007/978-3-642-30171-1_3. · DOI
  43. Popescu, E., Badica, C., & Moraret, L. (2009). WELSA: An Intelligent and Adaptive Web-Based Educational System. Intelligent Distributed Computing, III, 175–185. https://doi.org/10.1007/978-3-642-03214-1_17 · DOI
  44. Raj, Nisha S., Renumol, V. G. (2021) A Systematic Literature Review on Adaptive Content Recommenders in Personalized Learning Environments from 2015 to 2020. Journal of Computers in Education 1–36. · DOI · Paper page
  45. Reddy, P. D., Sasikumar, M. (2014) Student Model for an Intelligent Language Tutoring System. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies, (Pp. 441–443). IEEE. · DOI
  46. Riyahi, M., & Sohrabi, M. K. (2020). Providing Effective Recommendations in Discussion Groups Using a New Hybrid Recommender System Based on Implicit Ratings and Semantic Similarity. Electronic Commerce Research and Applications, 40, 100938. https://doi.org/10.1016/J.ELERAP.2020.100938 · DOI
  47. Sawyer, Robert, Smith, Andy, Rowe, Jonathan, Azevedo, Roger, Lester, James. (2017). Enhancing Student Models in Game-Based Learning with Facial Expression Recognition. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, (Pp. 192–201). · DOI
  48. Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2022). AI-Based Adaptive Personalized Content Presentation and Exercises Navigation for an Effective and Engaging E-Learning Platform. Multimedia Tools and Applications, 1–31,. https://doi.org/10.1007/S11042-022-13076-8 · DOI
  49. Schiaffino, S., Garcia, P., & Amandi, A. (2008). ETeacher: Providing Personalized Assistance to e-Learning Students. Computers & Education, 51(4), 1744–1754. · DOI
  50. Shemshack, Atikah, Kinshuk, Spector, Jonathan Michael. (2021). A Comprehensive Analysis of Personalized Learning Components. Journal of Computers in Education 8(4):485–503.https://doi.org/10.1007/S40692-021-00188-7. · DOI
  51. Shephard, Kerry. (2008) Higher Education for Sustainability: Seeking Affective Learning Outcomes. International Journal of Sustainability in Higher Education. · DOI
  52. Snow, R. E, Farr, M. J. (2021). Cognitive-Conative-Affective Processes in Aptitude, Learning, and .nstruction An Introduction. Aptitude, Learning, and Instruction, 1–10. · DOI
  53. Soloman, B. A., Felder, R. M. (1999. “Index of Learning Styles Questionnaire.” NC State University. Retrieved April 28, 2022 (https://www.researchgate.net/profile/Richard-Felder/publication/228403640_Index_of_Learning_Styles_Questionnaire/links/00463527bb8b1190be000000/Index-of-Learning-Styles-Questionnaire.pdf).
  54. Sosnovsky, S., & Brusilovsky, P. (2015). Evaluation of Topic-Based Adaptation and Student Modeling in QuizGuide. User Modeling and User-Adapted Interaction, 25(4), 371–424. https://doi.org/10.1007/S11257-015-9164-4 · DOI
  55. Suleman, R. M., Mizoguchi, R., Ikeda, M. (2014). Negotiation Driven Learning: A New Perspective of Learning Using Negotiation In: International Conference on Intelligent Tutoring Systems, (Pp. 689–691). · DOI
  56. Surjono, Herman D., Maltby, John R. (2003). Adaptive Educational Hypermedia Based on Multiple Student Characteristics. In: International Conference on Web-Based Learning, (Pp. 442–449). Springer. · DOI
  57. Swartout, William, Nye, Benjamin D., Hartholt, Arno, Reilly, Adam, Graesser, Arthur C., Vanlehn, Kurt, Wetzel, Jon, Liewer, Matt, Morbini, Fabrizio, Morgan, Brent, Wang, Lijia, Benn, Grace, Rosenberg, Milton. (2016). Designing a Personal Assistant for Life-Long Learning (PAL3). In: The Twenty-Ninth International Flairs Conference.
  58. Sweta, S., & Lal, K. (2017). Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System. International Journal of Fuzzy Systems, 19(4), 1249–1260. https://doi.org/10.1007/S40815-017-0309-Y · DOI
  59. Tadlaoui, M. A., Aammou, S., Khaldi, M., & Carvalho, R. N. (2016). Learner Modeling in Adaptive Educational Systems: A Comparative Study. International Journal of Modern Education and Computer Science, 8(3), 1. · DOI
  60. Tlili, A., Essalmi, F., Ayed, L. J. B., Jemni, M. (2017). A Smart Educational Game to Model Personality Using Learning Analytics. In: 2017 IEEE 17th International conference on advanced learning technologies (ICALT), (Pp. 131–135). · DOI
  61. Tourtoglou, K., & Virvou, M. (2008). User Stereotypes Concerning Cognitive, Personality and Performance Issues in a Collaborative Learning Environment for UML. New Directions in Intelligent Interactive Multimedia, 385–394,. https://doi.org/10.1007/978-3-540-68127-4_40 · DOI
  62. Tourtoglou, K., & Virvou, M. (2012). An Intelligent Recommender System for Trainers and Trainees in a Collaborative Learning Environment for UML. Intelligent Decision Technologies, 6(2), 79–95. https://doi.org/10.3233/IDT-2012-0125 · DOI
  63. Tsai, C. Y., Li, YuhYuh, & Cheng, Y. Y. (2017). The Relationships Among Adult Affective Factors, Engagement in Science, and Scientific Competencies. Adult Education Quarterly, 67(1), 30–47. https://doi.org/10.1177/0741713616673148 · DOI
  64. Tseng, J. C. R., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an Adaptive Learning System with Two Sources of Personalization Information. Computers & Education, 51(2), 776–786. https://doi.org/10.1016/J.COMPEDU.2007.08.002 · DOI
  65. Tsiriga, V., & Virvou, M. (2003). Modelling the Student to Individualise Tutoring in a Web-Based ICALL. International Journal of Continuing Engineering Education and Life-Long Learning, 13(3–4), 350–365. https://doi.org/10.1504/IJCEELL.2003.003269 · DOI
  66. Tsiriga, V., Virvou, M. (2002). Initializing the Student Model Using Stereotypes and Machine Learning. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.
  67. Wang, Jingjing, Haoran, et al. (2021). Top-N Personalized Recommendation with Graph Neural Networks in MOOCs. Computers and Education: Artificial Intelligence 2:100010 https://doi.org/10.1016/J.CAEAI.2021.100010. · DOI
  68. Willingham, et al. (2015). The Scientific Status of Learning Styles Theories. Teaching of Psychology, 42(3), 266–271. https://doi.org/10.1177/0098628315589505 · DOI
  69. Wininger, S. R., Redifer, J. L., Norman, A. D., & Ryle, M. K. (2019). Prevalence of Learning Styles in Educational Psychology and Introduction to Education Textbooks: A Content Analysis. Psychology Learning & Teaching, 18(3), 221–243. https://doi.org/10.1177/1475725719830301 · DOI
  70. Zhong, Jiemin, Xie, Haoran, Wang, Fu Lee. (2019). The Research Trends in Recommender Systems for E-Learning: A Systematic Review of SSCI Journal Articles from 2014 to 2018. Asian Association of Open Universities Journal. · DOI

Metadata

Authors
Bellarhmouch, Y., Jeghal, A., Tairi, H., & Benjelloun, N.
Affiliations
Sidi Mohamed Ben Abdellah University (MA)
Corresponding author
Bellarhmouch, Youssra
ORCID
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2022-10-14
Volume
28
Issue
4
Pages
4243-4263
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-022-11392-y
PMC
PMC9568945
OpenAlex
W4306179832
Cited by
41
References
3
Citations by year
2026: 9 · 2025: 15 · 2024: 11 · 2023: 6
FWCI
2.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Education and Information Technologies (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
Keywords
student model, personalized learning environments, online learning, learning styles, affective data, fuzzy logic, adaptive learning systems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review