1. Home
  2. Vol 4, No 4 (2023)
  3. Hasibuan

Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach

M. S. Hasibuan, R. Z. Abdul Aziz, Deshinta Arrova Dewi, Tri Basuki Kurniawan, Nasywa Aliyah Syafira

Abstract


The biggest obstacle that students have when participating in a virtual learning environment (e-learning) is discovering a platform that has functionalities that can be customized to fit their needs. This is usually accomplished in several ways using educational resources such as learning materials and virtual classroom design elements. Our research has tried to meet this demand by suggesting an extra element in the virtual classroom design, i.e., classifying the students’ learning styles through machine-learning techniques based on information gathered from questionnaires. This feature allows teachers or instructors to modify their lesson plans to better suit the learning preferences of their students. Additionally, this feature aids in the creation of a learning path that serves as a guide for students as they choose their course materials. In this study, we have selected the Felder-Silverman Learning Style Model (FSLSM) in the questionnaire design, which focuses on identifying the students' learning styles. After that, we employ several machine learning algorithms to create a prediction model for the students’ learning styles. The algorithms include Decision Tree, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Linear Discriminant Analysis, Random Forest, and Logistic Regression. The best prediction model from this exercise contributes to the recommendation model that was created using a collaborative filtering algorithm. We have carried out a pre-test and post-test method to evaluate our suggestions. There were 138 learners who were following a learning path and participated in this study. The findings of the pretest and post-test indicated a notable increase in students' motivation to study. This is confirmed by the fact that learners' satisfaction with online learning climbed to 87% when the learning style was considered, from 60% when it wasn't.

 

Doi: 10.28991/HIJ-2023-04-04-010

Full Text: PDF


Keywords


Education Quality; Education Environment; Learning Style; Recommendation Model; Personalization.

References


Kusumawardani, S. S., Prakoso, R. S., & Santosa, P. I. (2014). Using Ontology for Providing Content Recommendation Based on Learning Styles inside E-learning. Proceedings - 2nd International Conference on Artificial Intelligence, Modelling, and Simulation, AIMS 2014, September 2015, 276–281. doi:10.1109/AIMS.2014.40.

Selviandro, N., Suryani, M., & Hasibuan, Z. A. (2014). Open learning optimization based on cloud technology: Case study implementation in personalization e-learning. International Conference on Advanced Communication Technology, ICACT, 541–546. doi:10.1109/ICACT.2014.6779019.

Hasibuan, M. S., & Purbo, O. W. (2018). Learning Motivation increased due to a Relaxed Assessment in a Competitivee-Learning Environment. Proceeding of the Electrical Engineering Computer Science and Informatics, 5(5), 7–10. doi:10.11591/eecsi.v5i5.1616.

Shih, C.-C., & Gamon, J. A. (2001). Web-Based Learning: Relationships among Student Motivation, Attitude, Learning Styles, and Achievement. Journal of Agricultural Education, 42(4), 12–20. doi:10.5032/jae.2001.04012.

Larkin, T., & Budny, D. (2005). Learning styles in the classroom: Approaches to enhance student motivation and learning. ITHET 2005: 6th International Conference on Information Technology Based Higher Education and Training, Santo Domingo, Dominican Republic. doi:10.1109/ITHET.2005.1560310.

Wei, X., & Yan, J. (2009). Learner profile design for personalized E-learning systems. Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, 1–4. doi:10.1109/CISE.2009.5363560.

Santos, M., Andrade, F., Da Silva, J. M. C., & Imran, H. (2016). Learning object recommendation system evaluation. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016, 412–413. doi:10.1109/ICALT.2016.89.

Yang, Y. J., & Wu, C. (2009). An attribute-based ant colony system for adaptive learning object recommendation. Expert Systems with Applications, 36(2 PART 2), 3034–3047. doi:10.1016/j.eswa.2008.01.066.

Notargiacomo Mustaro, P., & Frango Silveira, I. (2006). Learning Objects: Adaptive Retrieval through Learning Styles. Interdisciplinary Journal of E-Skills and Lifelong Learning, 2(October), 035–046. doi:10.28945/619.

Kapadia, R. J. (2008). Teaching and learning styles in engineering education. In Proceedings - Frontiers in Education Conference, FIE T4B-1. doi:10.1109/FIE.2008.4720326.

Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674-681.

Mayer, R. E. (2011). Does styles research have useful implications for educational practice? Learning and Individual Differences, 21(3), 319–320. doi:10.1016/j.lindif.2010.11.016.

Raleiras, M., Nabizadeh, A. H., & Costa, F. A. (2022). Automatic learning styles prediction: a survey of the State-of-the-Art (2006–2021). Journal of Computers in Education, 9(4), 587–679. doi:10.1007/s40692-021-00215-7.

Bernard, J., Chang, T. W., Popescu, E., & Graf, S. (2017). Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Systems with Applications, 75, 94–108. doi:10.1016/j.eswa.2017.01.021.

Kolb, A. Y. (2005). The Kolb learning style inventory-version 3.1 2005 technical specifications. Boston, Massachusetts: Hay Resource Direct, 200(72), 166-171.

Kolb, D. A. (2007). The Kolb learning style inventory. Hay Resources Direct, Boston, Massachusetts, United States.

Honey, P., & Mumford, A. (2000). The learning styles helper's guide. Peter Honey Publications, Maidenhead, United States.

Cohen, J. J. (2008). Learning Styles of Myers-Briggs Type Indicators. Master of Science Degree: Thesis, Indiana State University Terre Haute, Indiana, United States.

Kim, J., Lee, A., & Ryu, H. (2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43(5), 450–461. doi:10.1016/j.ergon.2013.03.001.

Fleming, N. D. (1995). I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. Research and Development in Higher Education, Proceedings of the Annual Conference of the Higher Education and Research Development Society of Australasia, 18, 308–313.

Rita, S., Graf, S., & Kinshuk. (2002). Detecting Learners’ Profiles based on the Index of Learning Styles Data. In International Workshop on Intelligent and Adaptive Web-based Educational Systems.

El-Bishouty, M. M., Chang, T.-W., Graf, S., Kinshuk, & Chen, N.-S. (2014). Smart e-course recommender based on learning styles. Journal of Computers in Education, 1(1), 99–111. doi:10.1007/s40692-014-0003-0.

Imran, H., Belghis-Zadeh, M., Chang, T.-W., Kinshuk, & Graf, S. (2016). PLORS: a personalized learning object recommender system. Vietnam Journal of Computer Science, 3(1), 3–13. doi:10.1007/s40595-015-0049-6.

Zapata, A., Menéndez, V. H., Prieto, M. E., & Romero, C. (2015). Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. International Journal of Human Computer Studies, 76, 22–39. doi:10.1016/j.ijhcs.2014.12.002.

Durand, G., Belacel, N., & Laplante, F. (2013). Graph theory based model for learning path recommendation. Information Sciences, 251, 10–21. doi:10.1016/j.ins.2013.04.017.

Krauss, C., Salzmann, A., & Merceron, A. (2018). Branched learning paths for the recommendation of personalized sequences of course items. CEUR Workshop Proceedings, DeLFI, 1-10.

Klement, M., Dostál, J., & Marešová, H. (2014). Elements of Electronic Teaching Materials with Respect to Student’s Cognitive Learning Styles. Procedia - Social and Behavioral Sciences, 112(ICEEPSY 2013), 437–446. doi:10.1016/j.sbspro.2014.01.1186.

Qiyan, H., Feng, G., & Hu, W. (2010). Ontology-based learning object recommendation for cognitive considerations. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 60921003, 2746–2750. doi:10.1109/WCICA.2010.5554857.

Verhoeven, L., Schnotz, W., & Paas, F. (2009). Cognitive load in interactive knowledge construction. Learning and Instruction, 19(5), 369–375. doi:10.1016/j.learninstruc.2009.02.002.

Hasibuan, M. S., Nugroho, L. E., & Santosa, I. P. (2017). Learning style model detection based on prior knowledge in E-learning system. Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017, 2018-January (Aptikom), 1–5. doi:10.1109/IAC.2017.8280537.

Rasheed, F., & Wahid, A. (2021). Learning style detection in E-learning systems using machine learning techniques. Expert Systems with Applications, 174(February), 114774. doi:10.1016/j.eswa.2021.114774.

El Aissaoui, O., El Madani El Alami, Y., Oughdir, L., & El Allioui, Y. (2018). Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach. 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, 2018-May, 1–6. doi:10.1109/ISACV.2018.8354021.

Feldman, J., Monteserin, A., & Amandi, A. (2014). Detecting students’ perception style by using games. Computers and Education, 71, 14–22. doi:10.1016/j.compedu.2013.09.007.

Ghauth, K. I., & Abdullah, N. A. (2010). Learning materials recommendation using good learners’ ratings and content-based filtering. Educational Technology Research and Development, 58(6), 711–727. doi:10.1007/s11423-010-9155-4.

Poorni, G. (2015). A Personalized E-Learning Recommender System Using the Concept of Fuzzy Tree Matching. International Journal of Advanced Research in Computer Engineering & Technology, 4(11), 4039–4043.

Chen, H., Yin, C., Li, R., Rong, W., Xiong, Z., & David, B. (2020). Enhanced learning resource recommendation based on online learning style model. Tsinghua Science and Technology, 25(3), 348–356. doi:10.26599/TST.2019.9010014.

Miquélez, T., Bengoetxea, E., & Larrañaga, P. (2004). Evolutionary computation based on Bayesian classifiers. International Journal of Applied Mathematics and Computer Science, 14(3), 335–349.

Vermeulen, A. F. (2018). Practical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets. Apress, London, United Kingdom.

Fletcher, T. (2009). Support Vector Machines Explained. University College London, London, United Kingdom.

Ye, N. (2013). Data mining: theories, algorithms, and examples. CRC press, Florida, United States

Wu, X., Kumar, V., Ross, Q. J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D. J., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. doi:10.1007/s10115-007-0114-2.

Kolekar, S. V., Sanjeevi, S. G., & Bormane, D. S. (2010). Learning style recognition using Artificial Neural Network for adaptive user interface in e-learning. 2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010, 245–249. doi:10.1109/ICCIC.2010.5705768.

Siri, A. (2014). Predicting students’ academic dropout using artificial neural networks. Predicting Students’ Academic Dropout Using Artificial Neural Networks, 185(19), 1–159.

Ramanathan, L., Geetha, A., Khalid, M., & Swarnalatha, P. (2017). Student performance prediction model based on lion-wolf neural network. International Journal of Intelligent Engineering and Systems, 10(1), 114–123. doi:10.22266/ijies2017.0228.13.

Sun, J., De, X., & Zheng, H. (2023). The Prediction of Douyin Live Sales based on Neural Network Algorithms. HighTech and Innovation Journal, 4(2), 364–374. doi:10.28991/HIJ-2023-04-02-09.


Full Text: PDF

DOI: 10.28991/HIJ-2023-04-04-010

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Muhammad said hasibuan, Muhammad Said Hasibuan, RZ Abdul Aziz, Deshinta Arrova Dewi, Tri Basuki Kurniawan, nasywa aliyah syafira