Adaptive learning-based content management tool for online education platforms

Abstract

The ways of accessing information are highly developed today. Online education platforms are widely used directly or indirectly in the education of students. Instructors create their course content on these platforms and teach their courses to their students. With the developing internet technologies, the variety of visual, audio and textual course content is also increasing. However, students' learning tendencies differ while studying these course contents. While some of the students can learn more easily from course content consisting only of texts, others can learn more easily from course content supported by audio-visual materials. Identifying these learning differences among students has become important today. In order to enrich learning activities, it would be useful to create content in accordance with the learning tendencies of each student. In this study, we developed an adaptive learning-based help tool for instructors to create course content. This tool analyzes the learning styles of the students and provides recommendations to the instructor for creating the course content. Thus, all the course content prepared by the instructor will be selected and created according to the learning tendencies of the students. It will contribute to the improvement of learning activities.

Keywords

• Adaptive Learning
• Adaptive Hypermedia
• e-Learning
• Random Forest

Çevrimiçi eğitim platformları için adaptif öğrenme tabanlı içerik yönetim aracı

Abstract

Bilgiye erişim yolları günümüzde oldukça gelişmiş durumdadır. Online eğitim platformları yaygın bir şekilde öğrencilerin eğitimlerinde doğrudan veya dolaylı olarak kullanılmaktadır. Eğitmenler ders içeriklerini bu platformlar içinde oluşturup öğrencilerine derslerini anlatmaktadırlar. Gelişen internet teknolojileri ile görsel, işitsel ve metin olarak ders içeriklerinin çeşitliliği de artmaktadır. Ancak öğrencilerin bu ders içeriklerini çalışırlarken öğrenme eğilimleri farklılaşmaktadır. Öğrencilerin bir kısmı sadece metinlerden oluşan ders içeriklerinden daha rahat öğrenebiliyorken, diğer bir kısmı görsel ve işitsel materyallerle desteklenmiş ders içeriklerinden daha rahat öğrenebilmektedir. Öğrencilerin arasındaki bu öğrenme farklılıklarını tespit edebilmek günümüzde önemli hale gelmiştir. Öğrenme faaliyetlerini zenginleştirmek için her öğrencinin öğrenme eğilimine uygun olarak içerik oluşturulması faydalı olacaktır. Bu çalışmada ders içeriklerini oluşturacak eğitmenler için adaptif öğrenme tabanlı bir yardım aracı geliştirilmiştir. Bu yardımcı araç öğrencilerin öğrenme yöntemlerini analiz ederek eğitmene ders içeriğini oluşturma konusunda tavsiyeler vermektedir. Böylece eğitmenin hazırladığı tüm ders içeriği öğrencilerin öğrenme eğilimlerine göre seçilip oluşturulacaktır. Öğrenme faaliyetlerinin iyileştirilmesinde katkısı olacaktır.

Keywords

• Adaptif Öğrenme
• Adaptif Hipermedya
• e-Öğrenme
• Rastgele Orman

References

1. Khaldi A, Bouzidi R, Nader F Gamification of e-learning in higher education: a systematic literature review, Smart Learn. Environ., vol. 10, no. 1, p. 10, Jan. 2023.
2. Kofuji S, Ed. E-Learning - Engineering, On-Job Training and Interactive Teaching. InTech, 2012.
3. Delungahawatta T et al. Advances in e-learning in undergraduate clinical medicine: a systematic review, BMC Med. Educ., vol. 22, no. 1, p. 711, Oct. 2022.
4. Yarandi M, Jahankhani H, Tawil ARH An adaptive e-learning Decision support system, in 2012 15th International Conference on Interactive Collaborative Learning (ICL), Sep. 2012, pp. 1–5.
5. Ghislandi P, Ed., eLearning - Theories, Design, Software and Applications. InTech, 2012.
6. Ferreira HNM, Brant-Ribeiro T, Araújo RD, Dorça FA, Cattelan RG An Automatic and Dynamic Student Modeling Approach for Adaptive and Intelligent Educational Systems Using Ontologies and Bayesian Networks, in 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), Nov. 2016, pp. 738–745.
7. Yarandi M, Jahankhani H, Tawil ARH A personalized adaptive e-learning approach based on semantic web technology. http://www.webology.org/2013/v10n2/a111.pdf (Accessed: 10.07.2023).
8. De Bra P Adaptive Hypermedia, in Handbook on Information Technologies for Education and Training, Adelsberger HH, Kinshuk, Pawlowski JM, Sampson DG, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 29–46.

9. Elghouch N, En-Naimi EM, Seghroucheni YZ, El Mohajir BE, Achhab MA ALS_CORR[LP]: An adaptive learning system based on the learning styles of Felder-Silverman and a Bayesian network, in 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), Oct. 2016, pp. 494–499.
10. Yasuda K, Kawashima H, Hata Y, Kimura H Implementation Of An Adaptive Learning System Using A Bayesian Network, 11th International Conference Mobile Learning 2015.
11. Vagale V, Niedrite L, Ignatjeva S Implementation of Personalized Adaptive E-learning System, Balt. J. Mod. Comput., vol. 8, no. 2, 2020.
12. Chen Y, Li X, Liu J, Ying Z Recommendation System for Adaptive Learning, Appl. Psychol. Meas., vol. 42, no. 1, pp. 24–41, Jan. 2018.
13. Onah DFO, Sinclair JE Massive Open Online Courses – An Adaptive Learning Framework, ResearchGate, Mar. 02, 2015.
14. Tseng JCR, Chu HC, Hwang GJ, Tsai CC Development of an adaptive learning system with two sources of personalization information, Comput. Educ., vol. 51, no. 2, pp. 776–786, Sep. 2008.
15. Rimsha S, Moosa FA, Zaheer F, Kamal MT, Majid A What Does the Future Hold for a Surgical Trainee? This Lockdown Is Not a Letdown Yet: A Survey on Moodle Learning Management System as a Part of Blended Learning During COVID-19 Pandemic, Cureus, Jul. 2021.
16. Morze N, Varchenko-Trotsenko L, Terletska T, Smyrnova-Trybulska E, Implementation of adaptive learning at higher education institutions by means of Moodle LMS, J. Phys. Conf. Ser., vol. 1840, no. 1, p. 012062, Mar. 2021.
17. Moodle Web Service APIs, DEV Community, https://dev.to/udarajayawardena/moodle-web-service-apis-51k4 (Accessed: 19.07.2023).
18. Kumral CD, Topal A, Ersoy M, Çolak R,Yiğit T Random Forest Algoritmasının FPGA Üzerinde Gerçekleştirilerek Performans Analizinin Yapılması , El-Cezeri Fen ve Mühendis. Derg., Dec. 2022. Schonlau M, Zou RY The random forest algorithm for statistical learning, Stata J. Promot. Commun. Stat. Stata, vol. 20, no. 1, pp. 3–29, Mar. 2020.
19. Çayır A Performance Comparison of Locality Sensitive Hashing and Random Forest Algorithms for Handwritten Digits Recognition. Master Thesis, Kadir Has University, Graduate School of Science and Engineering,İstanbul, Türkiye,2014.
20. López AC What algorithms are used for adaptive learning?, https://canopylab.com/what-algorithms-are-used/ (Accessed: 20.07.2023).
21. Son J, Kim SB Content-based filtering for recommendation systems using multiattribute networks, Expert Syst. Appl., vol. 89, pp. 404–412, Dec. 2017.