Designing an E-Learning Application with Diagnostic Learning Style Testing for Enhancing Personalization in Educational Technology Courses

Denny Kurniadi; Wagino Wagino; Lise Asnur; Rahmadona Safitri; Rahmat Desman Koto; Wakhinuddin Wakhinuddin

doi:10.24036/jtip.v18i1.946

Denny Kurniadi Universitas Negeri Padang
Wagino Wagino Universitas Negeri Padang
Lise Asnur Universitas Negeri Padang
Rahmadona Safitri Universitas Negeri Padang
Rahmat Desman Koto Universitas Negeri Padang
Wakhinuddin Wakhinuddin Universitas Negeri Padang

DOI: https://doi.org/10.24036/jtip.v18i1.946

Keywords: E-learning Application, Diagnostic Learning Style Testing, Engineering education

Abstract

Teaching the importance of personalized learning in educational technology courses encourages students to understand how tailored educational approaches can enhance learning outcomes. Diagnostic learning style testing, including visual, auditory, kinesthetic (VAK), and technology usage dimensions, has been widely recognized as an effective method for addressing diverse student needs. The purpose of this paper is to design an e-learning application with diagnostic learning style testing to enhance personalization in educational technology courses. This system evaluates learning styles based on five dimensions and uses diagnostic results to recommend personalized learning strategies. The application is implemented as a web-based platform, providing both theoretical insights and practical application. By utilizing this system, educators and learners can gain actionable insights into individual learning preferences, ensuring better alignment between teaching methods and student needs. Additionally, the e-learning application can be accessed via mobile devices, allowing for flexible and adaptive learning experiences anytime and anywhere.

References

M. Sharif and D. Uckelmann, “Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach,” IEEE Access, vol. 12, pp. 54049–54065, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:269179466

Y. Walter, “Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education,” Int. J. Educ. Technol. High. Educ., vol. 21, no. 1, 2024, doi: 10.1186/s41239-024-00448-3.

M. Radovan and D. M. Radovan, “Harmonizing Pedagogy and Technology: Insights into Teaching Approaches That Foster Sustainable Motivation and Efficiency in Blended Learning,” Sustain. , vol. 16, no. 7, 2024, doi: 10.3390/su16072704.

F. Su, D. Zou, L. Wang, and L. Kohnke, “Student engagement and teaching presence in blended learning and emergency remote teaching,” J. Comput. Educ., pp. 1–26, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:257327104

A. R. Sayed, M. H. Khafagy, M. Ali, and M. H. Mohamed, “Predict student learning styles and suitable assessment methods using click stream,” Egypt. Informatics J., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:268864327

V. T. Lokare and P. M. Jadhav, “An AI-Based Learning Style Prediction Model for Personalized and Effective Learning,” Think. Ski. Creat., 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:265133400

S. Jawed, I. Faye, and A. S. Malik, “Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 32, pp. 378–390, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:266901867

E. G. Yotta, “Accommodating students’ learning styles differences in English language classroom,” Heliyon, vol. 9, no. 6, p. e17497, 2023, doi: 10.1016/j.heliyon.2023.e17497.

G. Brod, “There Are Multiple Paths to Personalized Education, and They Should Be Combined,” Curr. Dir. Psychol. Sci., vol. 33, no. 3, pp. 153–158, 2024, doi: 10.1177/09637214241242459.

H. S. R. Nejad, “Differentiated instruction for English learners: Teachers’ understanding and practices,” TESOL J., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:268815723

X. jingyi and C. Pamintuan, “Intelligent Enhancements in Differentiated Language Education: A Case Study Focused on Learner Needs,” Sage Open, vol. 14, no. 4, p. 21582440241287584, Oct. 2024, doi: 10.1177/21582440241287585.

B. N. Langelaan, L. Gaikhorst, W. Smets, and R. J. Oostdam, “Differentiating instruction: Understanding the key elements for successful teacher preparation and development,” Teach. Teach. Educ., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:266824654

Y. H. Lin, H. C. K. Lin, T. H. Wang, and C. H. Wu, “Integrating the STEAM-6E Model with Virtual Reality Instruction: The Contribution to Motivation, Effectiveness, Satisfaction, and Creativity of Learners with Diverse Cognitive Styles,” Sustain., vol. 15, no. 7, 2023, doi: 10.3390/su15076269.

M. Zhang, P. I. Gómez, Q. A. Xu, and T. Dragivcević, “Review of online learning for control and diagnostics of power converters and drives: Algorithms, implementations and applications,” Renew. Sustain. Energy Rev., 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:261420828

S. G. Essa, T. Celik, and N. E. Human-hendricks, “Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles : A Systematic Literature Review,” IEEE Access, vol. 11, no. May, pp. 48392–48409, 2023, doi: 10.1109/ACCESS.2023.3276439.

H. Y. Ayyoub and O. S. Al-Kadi, “Learning Style Identification Using Semisupervised Self-Taught Labeling,” IEEE Trans. Learn. Technol., vol. 17, pp. 1093–1106, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:267299916

F. Naseer, M. N. Khan, M. Tahir, A. Addas, and S. M. H. Aejaz, “Integrating deep learning techniques for personalized learning pathways in higher education,” Heliyon, vol. 10, no. 11, p. e32628, 2024, doi: 10.1016/j.heliyon.2024.e32628.

P. Patidar et al., “Edulyze: Learning Analytics for Real-World Classrooms at Scale,” J. Learn. Anal., vol. 11, no. 2, pp. 297–313, 2024, doi: 10.18608/jla.2024.8367.

B. Mexhuani, “Adopting Digital Tools in Higher Education: Opportunities, Challenges and Theoretical Insights,” Eur. J. Educ., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:273651792

K. Sharma, A. Nguyen, and Y. Hong, “Self-regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies,” Br. J. Educ. Technol., vol. 55, no. 4, pp. 1398–1436, 2024, doi: 10.1111/bjet.13459.

A. Lachner, I. Backfisch, and U. Franke, “Towards an Integrated Perspective of Teachers’ Technology Integration: A Preliminary Model and Future Research Directions,” Front. Learn. Res., vol. 12, no. 1, pp. 1–15, 2024, doi: 10.14786/flr.v12i1.1179.

K. R. Vareberg and C. A. Platt, “Harnessing the wisdom of YouTube: how self-directed learners achieve personalized learning through technological affordances,” Interact. Learn. Environ., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:267264468

L. A. Shoaib, S. H. Safii, N. Idris, R. Hussin, and M. A. H. Sazali, “Correction: Utilizing decision tree machine learning model to map dental students’ preferred learning styles with suitable instructional strategies (BMC Medical Education, (2024), 24, 1, (58), 10.1186/s12909-023-05022-5),” BMC Med. Educ., vol. 24, no. 1, pp. 1–13, 2024, doi: 10.1186/s12909-024-05162-2.

V. Thongchotchat, Y. Kudo, Y. Okada, and K. Sato, “Educational Recommendation System Utilizing Learning Styles: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 8988–8999, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:256178188

W. E. Villegas-Ch., J. V. Garcia-Ortiz, and S. Sánchez-Viteri, “Personalization of Learning: Machine Learning Models for Adapting Educational Content to Individual Learning Styles,” IEEE Access, vol. 12, pp. 121114–121130, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:272298546

V. Thongchotchat, Y. Kudo, Y. Okada, and K. Sato, “Educational Recommendation System Utilizing Learning Styles : A Systematic Literature Review,” IEEE Access, vol. 11, no. November 2022, pp. 8988–8999, 2023, doi: 10.1109/ACCESS.2023.3238417.

R. Chugh, D. Turnbull, M. A. Cowling, R. Vanderburg, and M. A. Vanderburg, “Implementing educational technology in Higher Education Institutions: A review of technologies, stakeholder perceptions, frameworks and metrics,” Educ. Inf. Technol., vol. 28, no. 12, pp. 16403–16429, 2023, doi: 10.1007/s10639-023-11846-x.

M. Mejeh and M. Rehm, “Taking adaptive learning in educational settings to the next level: leveraging natural language processing for improved personalization,” Educ. Technol. Res. Dev., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:268025735

K. Aulakh, R. K. Roul, and M. Kaushal, “E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review,” Int. J. Educ. Dev., vol. 101, p. 102814, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:258807497

N. Nouman, Z. A. Shaikh, and S. Wasi, “A Novel Personalized Learning Framework With Interactive e-Mentoring,” IEEE Access, vol. 12, pp. 10428–10458, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:267006096

H. Robles, M. Jimeno, K. Villalba, I. Mardini, C. Viloria-Nuñez, and W. Florian, “Design of a micro-learning framework and mobile application using design-based research,” PeerJ Comput. Sci., vol. 9, pp. 1–31, 2023, doi: 10.7717/PEERJ-CS.1223.

N. H. A. Ngadiman, S. Sulaiman, N. Idris, M. R. Samingan, and H. Mohamed, “Checklist Approach for the Development of Educational Applications by Novice Software Developers,” IEEE Access, vol. 11, pp. 900–918, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:255253230

D. Kurniadi, “Designing and Developing of Learning Class Grouping Applications Base on Genetic Algorithms,” J. Teknol. Inf. dan Pendidik., no. Vol 16 No 1 (2023): Jurnal Teknologi Informasi dan Pendidikan, pp. 109–126, 2023, [Online]. Available: http://tip.ppj.unp.ac.id/index.php/tip/article/view/694/307

D. Kurniadi, R. Safitri, D. Irfan, and ..., “Determining Study Groups Based on Student Profile Criteria Using K-Means Method,” J. Teknol. Inf. dan Pendidik., vol. 14, no. 501, pp. 272–277, 2021, [Online]. Available: http://tip.ppj.unp.ac.id/index.php/tip/article/view/501%0Ahttp://tip.ppj.unp.ac.id/index.php/tip/article/download/501/210

S. Syukhri and P. Gusmayeni, “Design of Web-Based Archive Management Information System,” J. Teknol. Inf. dan Pendidik., vol. 14, no. 2, pp. 92–98, 2021, doi: 10.24036/tip.v14i2.429.

C. Halkiopoulos and E. Gkintoni, “Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,” Electron., vol. 13, no. 18, 2024, doi: 10.3390/electronics13183762.

A. Ezzaim, A. Dahbi, A. Aqqal, and A. Haidine, “AI-based learning style detection in adaptive learning systems: a systematic literature review,” J. Comput. Educ., 2024, doi: 10.1007/s40692-024-00328-9.

S. G. Essa, T. Celik, and N. E. Human-Hendricks, “Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 48392–48409, 2023, doi: 10.1109/ACCESS.2023.3276439.

Designing an E-Learning Application with Diagnostic Learning Style Testing for Enhancing Personalization in Educational Technology Courses

Abstract

References

Most read articles by the same author(s)