DOI : https://doi.org/10.18517/ijaseit.12.6.14702

Incremental Knowledge-based System for Recommending Content Adaptation in Dynamic Learning Environment

Alva Hendi Muhammad (1), Dhani Ariatmanto (2), - Yuhefizar (3)
(1) Magister of Informatics Engineering, University of Amikom Yogyakarta, Yogyakarta, 55128, Indonesia
(2) Magister of Informatics Engineering, University of Amikom Yogyakarta, Yogyakarta, 55128, Indonesia
(3) Department of Information Technology, Politeknik Negeri Padang, Padang, 25166, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Muhammad, Alva Hendi, et al. “Incremental Knowledge-Based System for Recommending Content Adaptation in Dynamic Learning Environment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 6, Dec. 2022, pp. 2297-06, doi:10.18517/ijaseit.12.6.14702.
Citation Format :
Dynamic learning environment (DLE) provides an opportunity for students with a remarkable learning experience in a limited time and specific situations. In this condition, adaptation and personalization have been key issues to accommodate differences between students. Both paradigms emphasize tailoring learning activities to students’ understanding and interest through learning objectives, instructional approaches, and learning pathways. In addition, the students will learn optimized instructional activities at their own pace. This paper presents an incremental knowledge-based system to facilitate learning content adaptation in DLE. To be specific, the knowledge base contains a set of rules incrementally constructed using Ripple Down Rules (RDR) after evaluating a series of test cases. The test cases are generated automatically by analyzing the attributes that reflect the learning situation. Since it is impossible to perform thorough testing involving all input parameters, the selection criteria using pairwise testing are applied to minimize the refinement. Therefore, the evaluation of the theoretical concept is then carried out on a real case. The selected case study for the analysis is the subject of Computer Networking for an undergraduate course. Several adaptive scenarios are presented based on some criteria. An education expert is involved in recommending suitable content for adaptation during the evaluation phase. However, the knowledge base development is automatically constructed from the incremental knowledge acquisition process. As the evaluation progresses, the knowledge base is validated for its accuracy in predicting learning content recommendations.

L. Tetzlaff, F. Schmiedek, and G. Brod, “Developing Personalized Education: A Dynamic Framework,” Educ. Psychol. Rev., vol. 33, no. 3, pp. 863-882, 2021, doi: 10.1007/s10648-020-09570-w.

A. H. Muhammad and D. Ariatmanto, “Understanding the role of individual learner in adaptive and personalized e-learning system,” Bull. Electr. Eng. Informatics, vol. 10, no. 6, 2021.

A. Dhakshinamoorthy and K. Dhakshinamoorthy, “KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style,” Comput. Appl. Eng. Educ., vol. 27, no. 2, pp. 319-331, 2019, doi: https://doi.org/10.1002/cae.22076.

S. Sarwar, Z. U. Qayyum, R. Garcí­a-Castro, M. Safyan, and R. F. Munir, “Ontology based E-learning framework: A personalized, adaptive and context aware model,” Multimed. Tools Appl., vol. 78, no. 24, pp. 34745-34771, 2019, doi: 10.1007/s11042-019-08125-8.

A. Muhammad, J. Shen, G. Beydoun, and D. Xu, “SBAR: A Framework to Support Learning Path Adaptation in Mobile Learning BT - Frontier Computing,” 2018, pp. 655-665.

F. Cena, S. Likavec, and A. Rapp, “Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing,” Inf. Syst. Front., vol. 21, no. 5, pp. 1085-1110, 2019.

H. Yago, J. Clemente, and D. Rodriguez, “Competence-based recommender systems: a systematic literature review,” Behav. Inf. Technol., vol. 37, no. 10-11, pp. 958-977, 2018.

J. Gardner and C. Brooks, “Student Success Prediction in MOOCs,” User Model. User-adapt. Interact., vol. 28, no. 2, pp. 127-203, 2018.

E. Mousavinasab, N. Zarifsanaiey, S. R. Niakan Kalhori, M. Rakhshan, L. Keikha, and M. Ghazi Saeedi, “Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods,” Interact. Learn. Environ., vol. 29, no. 1, pp. 142-163, 2021, doi: 10.1080/10494820.2018.1558257.

N. Idris, S. Z. M. Hashim, R. Samsudin, and N. B. H. Ahmad, “Intelligent learning model based on significant weight of domain knowledge concept for adaptive e-learning,” Int. J. Adv. Sci. Eng. Inf. Technol, vol. 7, no. 4-2, p. 1486, 2017.

D. Shawky and A. Badawi, “Towards a Personalized Learning Experience Using Reinforcement Learning,” in Machine Learning Paradigms: Theory and Application, A. E. Hassanien, Ed. Cham: Springer International Publishing, 2019, pp. 169-187.

S. Ennouamani, Z. Mahani, and L. Akharraz, “A context-aware mobile learning system for adapting learning content and format of presentation: design, validation and evaluation,” Educ. Inf. Technol., vol. 25, no. 5, pp. 3919-3955, 2020, doi: 10.1007/s10639-020-10149-9.

T. Duan, “A new idea for the optimization of MOOC-based teaching,” Educ. Inf. Technol., 2021, doi: 10.1007/s10639-021-10755-1.

H. Zhang, T. Huang, Z. Lv, S. Liu, and H. Yang, “MOOCRC: A Highly Accurate Resource Recommendation Model for Use in MOOC Environments,” Mob. Networks Appl., vol. 24, no. 1, pp. 34-46, 2019, doi: 10.1007/s11036-018-1131-y.

V. Slavuj, A. MeÅ¡trović, and B. KovaÄić, “Adaptivity in educational systems for language learning: a review,” Comput. Assist. Lang. Learn., vol. 30, no. 1-2, pp. 64-90, 2017, doi: 10.1080/09588221.2016.1242502.

S. V Kolekar, R. M. Pai, and M. P. M. M., “Rule based adaptive user interface for adaptive E-learning system,” Educ. Inf. Technol., vol. 24, no. 1, pp. 613-641, 2019, doi: 10.1007/s10639-018-9788-1.

S. S. Khanal, P. W. C. Prasad, A. Alsadoon, and A. Maag, “A systematic review: machine learning based recommendation systems for e-learning,” Educ. Inf. Technol., pp. 1-30, 2019.

D. Herbert and B. H. Kang, “Intelligent conversation system using multiple classification ripple down rules and conversational context,” Expert Syst. Appl., vol. 112, pp. 342-352, 2018, doi: https://doi.org/10.1016/j.eswa.2018.06.049.

I. U. Haq, I. Gondal, P. Vamplew, S. Wen, A. Zomaya, and L. T. Yang, “Enhancing Model Performance for Fraud Detection by Feature Engineering and Compact Unified Expressions,” 2020, pp. 399-409.

Y. Li et al., “IoT-CANE: A unified knowledge management system for data-centric Internet of Things application systems,” J. Parallel Distrib. Comput., vol. 131, pp. 161-172, 2019, doi: https://doi.org/10.1016/j.jpdc.2019.04.016.

A. Maedche, S. Gregor, S. Morana, and J. Feine, “Conceptualization of the problem space in design science research,” in International Conference on Design Science Research in Information Systems and Technology, 2019, pp. 18-31.

R. Baskerville, A. Baiyere, S. Gregor, A. Hevner, and M. Rossi, “Design science research contributions: Finding a balance between artifact and theory,” J. Assoc. Inf. Syst., vol. 19, no. 5, p. 3, 2018.

S. Gregor and A. R. Hevner, “Positioning and presenting design science research for maximum impact,” MIS Q., pp. 337-355, 2013.

A. K. Alazzawi, A. A. Ba Homaid, A. A. Alomoush, and A. R. A. Alsewari, “Artificial Bee Colony algorithm for pairwise test generation,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 1-2, pp. 103-108, 2017.

S. Sabharwal and M. Aggarwal, “Test set generation for pairwise testing using genetic algorithms,” J. Inf. Process. Syst., vol. 13, no. 5, pp. 1089-1102, 2017, doi: 10.3745/JIPS.04.0019.

D. Gupta, A. Rana, and S. Tyagi, “Sequence generation of test case using pairwise approach methodology,” Advances in Intelligent Systems and Computing, vol. 554. pp. 79-85, 2018, doi: 10.1007/978-981-10-3773-3_9.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).