The effects and effectiveness of an adaptive e-learning system on the learning process and performance of students

Автор: Igor Ristić, Marija Runić-Ristić, Tijana Savić Tot, Vilmos Tot, Momčilo Bajac

Журнал: International Journal of Cognitive Research in Science, Engineering and Education @ijcrsee

Рубрика: Original research

Статья в выпуске: 1 vol.11, 2023 года.

Бесплатный доступ

Students acquire learning material in different ways. Some prefer to read, some prefer to listen, others use the third type of sense. Traditional teaching uses only one of these teaching strategies since it is impossible to use all of them in the classroom. However, these days, adaptive e-learning systems enable learning material to be customized to the individual needs of learners. For the purpose of this paper, the researchers designed a model of the adaptive learning management system and implemented it in Moodle. The system was evaluated on 228 students. The incorporation of learning styles in Moodle is based on the VAK learning style model. The authors analysed the effects and effectiveness of an adaptive e-learning system. It was discovered that there are significant differences in learning effectiveness, satisfaction and motivation when students use an adaptive e-learning module in comparison to a standard e-learning module. Moreover, we investigated the durability of knowledge acquired with an adaptive e-learning system by comparing the performance of students not only after the completion of the course but also a month after the course. The results of the research confirmed the authors’ expectations and showed that an adaptive e-learning system can increase students’ learning results. So far, to our knowledge, no study has evaluated the performance between a control and experiment group a few months after the completion of the course, i.e. by analysing the durability of knowledge acquired through an adaptive e-learning system. Moreover, the motivation of students to continue using an adaptive e-learning system hasn’t been analysed until now.

Еще

Adaptive learning system, e-learning, learning style, мoodle

Короткий адрес: https://sciup.org/170198704

IDR: 170198704   |   DOI: 10.23947/2334-8496-2023-11-1-77-92

Список литературы The effects and effectiveness of an adaptive e-learning system on the learning process and performance of students

  • Alkhuraiji, S., Cheetham, B., & Bamasak, O. (2011, July). Dynamic adaptive mechanism in learning management system based on learning styles. In 2011 IEEE 11th International Conference on Advanced Learning Technologies (pp. 215- 217). IEEE. https://doi.org/10.1109/ICALT.2011.69
  • Bajraktarevic, N., Hall, W., & Fullick, P. (2003, August). Incorporating learning styles in hypermedia environment: Empirical evaluation. In Proceedings of the workshop on adaptive hypermedia and adaptive web-based systems (pp. 41-52).
  • Baldiris, S., Santos, O. C., Barrera, C., Boticario, J., Velez, J., & Fabregat, R. (2008). Integration of educational specifications and standards to support adaptive learning scenarios in ADAPTAPlan. International Journal of Computer & Applications, 5(1), 88-107. Retrieved from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=251ea4bd6274ea17f8 3ee80ecde3596062647dab
  • Brown, E. (2007). The use of learning styles in adaptive hypermedia (Doctoral dissertation, University of Nottingham). Retrieved from https://core.ac.uk/download/pdf/33564051.pdf
  • Brown, E., Brailsford, T., Fisher, T., Moore, A. and Ashman, H. (2006). Reappraising cognitive styles in adaptive web applications. In Proceedings of the 15th international conference on World Wide Web (WWW `06), ACM Press, New York, NY, 327- 335. http://dx.doi.org/10.1145/1135777.1135827
  • Cabada, R. Z., Estrada, M. L. B., & García, C. A. R. (2011). EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Systems with Applications, 38(8), 9522-9529. https://doi. org/10.1016/j.eswa.2011.01.145
  • Cabada, R., Estrada, M., Sanchez, L., Sandoval, G., Velazquez, J., & Barrientos, J. (2009). Modeling student’s learning styles in web 2.0 learning systems. World Journal on Educational Technology, 1(2), 75-88. https://doi.org/10.1007/978-3-642- 05258-3_45
  • Cakir, O., Teker, E., & Can Aybek, E. (2015). The effect of adaptive learning environment in teaching the number concept to students with intellectual disabilities. Croatian Journal of Education: Hrvatski časopis za odgoj i obrazovanje, 17(Sp. Ed. 4), 199-221. https://doi.org/10.15516/cje.v17i0.1122.
  • Coffield, F., Moseley, D., Hall, E., Ecclestone, K., Coffield, F., Moseley, D., ... & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London: Learning and Skills Research Centre
  • Del Corso, D., Ovcin, E., & Morrone, G. (2005). A teacher friendly environment to foster learner-centered customization in the development of interactive educational packages. IEEE Transactions on Education, 48(4), 574-579. https://doi. org/10.1109/TE.2005.850709
  • DeVellis, R. F. (2003). Scale development: Theory and applications, Thousand Oaks, CA: Sage.
  • Dwivedi, P., & Bharadwaj, K. K. (2013). Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Journal of Educational Technology & Society, 16(4), 201-216. Retrieved from https://scholar.alaqsa. edu.ps/9701/1/Educational%20Technology%20%26%20Society%20Educational%20Technology%20%20%28%20 PDFDrive%20%29.pdf#page=206
  • El Bachari, E., Abelwahed, E. H., & El Adnani, M. (2011). E-Learning personalization based on Dynamic learners’ preference. International Journal of Computer Science & Information Technology, 3(3). Retrieved from http://citeseerx.ist.psu.edu/ viewdoc/summary?doi=10.1.1.207.5482
  • Feldman, J., Monteserin, A., & Amandi, A. (2014). Detecting students’ perception style by using games. Computers & Education, 71, 14-22. https://doi.org/10.1016/j.compedu.2013.09.007
  • Fleming, N. D., & Mills, C. (1992). Not another inventory, rather a catalyst for reflection. To improve the academy, 11(1), 137- 155. https://doi.org/10.1002/j.2334-4822.1992.tb00213.x
  • Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation, and learning: an empirical study. Journal of educational multimedia and hypermedia, 9(4), 281-311. Retrieved from https://www.learntechlib.org/primary/p/9546/
  • Franzoni, A. L., Assar, S., Defude, B., & Rojas, J. (2008, July). Student learning styles adaptation method based on teaching strategies and electronic media. In 2008 Eighth IEEE International Conference on Advanced Learning Technologies (pp. 778-782). IEEE. https://doi.org/10.1109/ICALT.2008.149.
  • García, P., Schiaffino, S., & Amandi, A. (2008). An enhanced Bayesian model to detect students’ learning styles in Web-based courses. Journal of Computer Assisted Learning, 24(4), 305-315. https://doi.org/10.1111/j.1365-2729.2007.00262.x
  • Germanakos, P., Tsianos, N., Lekkas, Z., Mourlas, C., Belk, M., & Samaras, G. (2007, December). A semantic approach of an adaptive and personalized web-based learning content-The case of AdaptiveWeb. In Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007) (pp. 68-73). IEEE. http://dx.doi.org/10.1109/ SMAP.2007.44
  • Graf, S. (2007) Adaptivity in Learning Management Systems Focussing on Learning Styles. PhD Thesis, Vienna University of Technology, Austria
  • Graf, S., Kinshuk, & Liu, T. C. (2009). Supporting teachers in identifying students’ learning styles in learning management systems: An automatic student modelling approach. Journal of Educational Technology & Society, 12(4), 3-14. Retrieved from https://www.jstor.org/stable/jeductechsoci.12.4.3
  • Jovanović, J., Gašević, D., & Devedžić, V. (2009). TANGRAM for personalized learning using the semantic web technologies. Journal of emerging technologies in web intelligence, 1(1), 6-21.
  • Kamardeen, I. (2014). Adaptive e-tutorial for enhancing student learning in construction education. International Journal of Construction Education and Research, 10(2), 79-95. https://doi.org/10.1080/15578771.2012.756437.
  • Kanaksabee, P., Odit, M. P., & Ramdoyal, A. (2011). A Standard-Based Model For Adaptive E-Learning Platform For Mauritian Academic Institutions. Journal of International Education Research (JIER), 7(1), 109-118. https://doi.org/10.19030/jier. v7i1.3541.
  • Kelly, D., & Tangney, B. (2005, July). ‘First Aid for You’: getting to know your learning style using machine learning. In Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05) (pp. 1-3). IEEE. https://doi.org/10.1109/ ICALT.2005.1.
  • Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & education, 56(3), 885-899. https://doi.org/10.1016/j. compedu.2010.11.001
  • Kline, R. B. (2005). Principles and practice of structural equation modelling. New York: Guildford. https://doi. org/10.1177/1049731509336986.
  • Kurilovas, E., Kubilinskiene, S., & Dagiene, V. (2014). Web 3.0–Based personalisation of learning objects in virtual learning environments. Computers in Human Behavior, 30, 654-662. https://doi.org/10.1016/j.chb.2013.07.039.
  • Latham, A., Crockett, K., & McLean, D. (2014). An adaptation algorithm for an intelligent natural language tutoring system. Computers & Education, 71, 97-110. https://doi.org/10.1016/j.compedu.2013.09.014.
  • Latham, A., Crockett, K., McLean, D., & Edmonds, B. (2012). A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education, 59(1), 95-109. https://doi.org/10.1016/j.compedu.2011.11.001
  • Limongelli, C., Sciarrone, F., Temperini, M., & Vaste, G. (2009). Adaptive learning with the LS-plan system: a field evaluation. IEEE Transactions on Learning Technologies, 2(3), 203-215. https://doi.org/ 10.1109/TLT.2009.25.
  • Limongelli, C., Sciarrone, F., Temperini, M., & Vaste, G. (2011). The Lecomps5 framework for personalized web-based learning: A teacher’s satisfaction perspective. Computers in Human Behavior, 27(4), 1310-1320. https://doi.org/10.1016/j. chb.2010.07.026.
  • Lin, C. F., Yeh, Y. C., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199-210. https://doi.org/10.1016/j.compedu.2013.05.009
  • Mustafa, Y. E. A., & Sharif, S. M. (2011). An approach to adaptive e-learning hypermedia system based on learning styles (AEHS-LS): Implementation and evaluation. International Journal of Library and Information Science, 3(1), 15-28. Retrieved from https://academicjournals.org/journal/IJLIS/article-full-text-pdf/75161B52666.pdf
  • Özyurt, Ö., Özyurt, H., & Baki, A. (2013). Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT. Expert Systems with Applications, 40(8), 2914-2940. https://doi.org/10.1016/j.eswa.2012.12.008.
  • Popescu, E., Badica, C., & Moraret, L. (2010). Accommodating learning styles in an adaptive educational system. Informatica, 34(4). Retrieved from https://informatica.si/index.php/informatica/article/viewFile/319/318
  • Rukanuddin, M., Hafiz, K., & Asfia, R. (2016). Knowledge of individual differences of the learners of second language enriches second language teaching. Journal of Literature, Languages and Linguistics. An International Peer-Reviewed Journal, 19. 11-15. Retrieved from https://core.ac.uk/download/pdf/234693181.pdf
  • Sancho, P., Martínez, I., & Fernández-Manjón, B. (2005). Semantic Web Technologies Applied to e-learning Personalization in e-aula. Journal of Universal Computer Science, 11(9), 1470-1481. Retrieved from https://d1wqtxts1xzle7. cloudfront.net/30848308/jucs_11_9_1470_1481_sancho-libre.pdf?1392103428=&response-content-disposition=i nline%3B+filename%3DSemantic_Web_Technologies_Applied_to_e_l.pdf&Expires=1680098083&Signature=Iyis EgFqLruiN3giCpKbWmJYgrWTnWOm7DZBUzNMGWQ52HgjLERhyNsHMkrrO1VhBuTprRNMy4Jy4XgQD3JpDI UnJpITfEMD9p-6xspV40dqVwahtH5~3Ei0F8GViJNm2lQ6fmP4rqI7c82yle0ImFZFAJyIhBeQm8EOflT6Ru5hauzx1 PsOvWNe7ydkhnRIq~yfGmalVHfPIDJ1RO5QHNVRlj0B4~1bniA3Icjg1f-i5dt1WLYlEMIelB5nvWpcTxNDMFMK~b-vjS09c6vDVzn-ryk7BwsDW1QSoaMy-pPVoIvq0Fa3TmBNftPEFVXdTTb~2wyDjs-46LvkEFSuVw__&Key-Pair- Id=APKAJLOHF5GGSLRBV4ZA
  • Sangineto, E., Capuano, N., Gaeta, M., & Micarelli, A. (2008). Adaptive course generation through learning styles representation. Universal Access in the Information Society, 7, 1-23. https://doi.org/10.1007/s10209-007-0101-0.
  • Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744-1754. https://doi.org/10.1016/j.compedu.2008.05.008.
  • Sevarac, Z., Devedzic, V., & Jovanovic, J. (2012). Adaptive neuro-fuzzy pedagogical recommender. Expert Systems with Applications, 39(10), 9797-9806. https://doi.org/10.1016/j.eswa.2012.02.174.
  • Siadaty, M., & Taghiyareh, F. (2007, July). PALS2: Pedagogically adaptive learning system based on learning styles. In Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007) (pp. 616-618). IEEE. https://doi. org/10.1109/ICALT.2007.198
  • Smith-Jentsch, K. A., Jentsch, F. G., Payne, S. C., & Salas, E. (1996). Can pretraining experiences explain individual differences in learning?. Journal of applied Psychology, 81(1), 110. https://doi.org/10.1037/0021-9010.81.1.110.
  • Sterbini, A., & Temperini, M. (2009, October). Adaptive construction and delivery of web-based learning paths. In 2009 39th IEEE Frontiers in Education Conference (pp. 1-6). IEEE. https://doi.org/10.1109/FIE.2009.5350579
  • Sun, S., Joy, M., & Griffiths, N. (2007). The use of learning objects and learning styles in a multi-agent education system. Journal of Interactive Learning Research, 18(3), 381-398. Retrieved from https://www.learntechlib.org/primary/p/21053/
  • Tamura, Y., Yamamuro, T., & Okamoto, T. (2006, July). Distributed and Learner Adaptive E-Learning Environment with Use of Web Services. In Sixth IEEE International Conference on Advanced Learning Technologies (ICALT’06) (pp. 451-155). IEEE. https://doi.org/10.1109/ICALT.2006.1652469
  • Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in human behavior, 55, 1185-1193. https://doi.org/10.1016/j.chb.2015.02.014.
  • Tseng, J. C., 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.
  • Wang, T. I., Wang, K. T., & Huang, Y. M. (2008). Using a style-based ant colony system for adaptive learning. Expert Systems with applications, 34(4), 2449-2464. https://doi.org/10.1016/j.eswa.2007.04.014
  • Wen, D., Graf, S., Lan, C. H., Anderson, T., & Dickson, K. (2007). Supporting web-based learning through adaptive assessment. FormaMente Journal, 2(1-2), 45-79.
  • Wolf, C. (2007). Construction of an adaptive e-learning environment to address learning styles and an investigation of the effect of media choice (Doctoral dissertation, RMIT University). Retreived from http://hdl.handle.net/20.500.12424/828594
  • Yang, T. C., Hwang, G. J., & Yang, S. J. H. (2013). Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Journal of Educational Technology & Society, 16(4), 185- 200. Retrieved from https://scholar.alaqsa.edu.ps/9701/1/Educational%20Technology%20%26%20Society%20 Educational%20Technology%20%20%28%20PDFDrive%20%29.pdf#page=190
  • Zulfiani, Z., Suwarna, I. P., & Miranto, S. (2018). Science education adaptive learning system as a computer-based science learning with learning style variations. Journal of Baltic Science Education, 17(4), 711-727. Retrieved from http://www. scientiasocialis.lt/jbse/files/pdf/vol17/711-727.Zulfiani_JBSE_Vol.17_No.4.pdf
Еще
Статья научная