Pengembangan Media Pembelajaran Adaptif Berbasis Fuzzy Expert System untuk Meningkatkan Prestasi Belajar Siswa

Sumarlin Sumarlin

Abstract


Beberapa peneliti telah menunjukkan pentingnya memberikan bimbingan dan dukungan untuk belajar secara individu. Dalam beberapa dekade terakhir, sebagian besar penelitian tentang bagaimana cara mengembangkan sistem pembelajaran adaptif untuk mengatasi masalah ini terutama berdasarkan status kognitif siswa, seperti prestasi belajar. Namun, beberapa pendidik telah menunjukkan perlunya mempertimbangkan status afektif peserta didik. Oleh karena itu, penelitian ini mengusulkan pendekatan sistem pakar fuzzy dengan mempertimbangkan status afektif dan kognitif individu peserta didik. Sistem pembelajaran adaptif dilaksanakan berdasarkan pendekatan yang diusulkan. Selain itu, perlu dilakukan percobaan pada salah satu mata pelajaran untuk membandingkan prestasi belajar dan persepsi siswa yang melakukan pembelajaran dengan sistem pembelajaran adaptif dengan analisis status afektif dan kognitif.  Selain itu, berdasarkan hasil penelitian ditemukan bahwa aplikasi yang dikembangkan membantu siswa yang berprestasi rendah berhasil menyelesaikan tugas-tugas pembelajaran, sementara mereka yang belajar dengan pendekatan berbasis faktor kognitif konvensional lebih cenderung menyerah dalam menyelesaikan beberapa tugas pembelajaran, dan lebih bergantung pada versi rinci dari bahan ajar.

Keywords


Pembelajaran Adaptif, Sistim Pakar, Fuzzy, Afektif, Kognitif

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References


Akerkar, R. (2014). Introduction to artificial intelligence. PHI Learning Pvt. Ltd.

Daghestani, L. F., Ibrahim, L. F., Al‐Towirgi, R. S., & Salman, H. A. (2020). Adapting gamified learning systems using educational data mining techniques. Computer Applications in Engineering Education, 28(3), 568–589.

Ehrman, M. E., Leaver, B. L., & Oxford, R. L. (2003). A brief overview of individual differences in second language learning. System, 31(3), 313–330.

Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S. J., & Lim, H. (2016). Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Computers & Education, 94(March), 18–36.

Islam, N., Beer, M., & Slack, F. (2015). E-learning challenges faced by academics in higher education. Journal of Education and Training Studies, 3(5), 102–112.

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.

Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalized hypermedia presentation techniques for improving online customer relationships. Knowledge Engineering Review, 16(2), 111–156.

Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives: the classification of educational goals; handbook.... 2. Affective domain. David McKay Company New York.

Looi, C. K., Wong, L. H., So, H. J., Seow, P., Toh, Y., Chen, W., & Soloway, E. (2009). Anatomy of a mobilized lesson: Learning my way. Computers & Education, 53(4), 1120–1132.

Monova-Zheleva, M. (2005). Adaptive learning in Web-based educational environments. Cybernetics and Information Technologies, 5(1), 44–55.

Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459–489.

Pai, K. C., Kuo, B. C., Liao, C. H., & Liu, Y. M. (2020). An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning. Educational Psychology, 1–16.

Sampayo-Vargas, S., Cope, C. J., He, Z., & Byrne, G. J. (2013). The effectiveness of adaptive difficulty adjustments on students’ motivation and learning in an educational computer game. Computers & Education, 69(November), 452–462.

Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55(February), 1185–1193.

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.

Van Rosmalen, P., Vogten, H., Van Es, R., Passier, H., Poelmans, P., & Koper, R. (2006). Authoring a full life cycle model in standards-based, adaptive e-learning. Journal of Educational Technology & Society, 9(1), 72–83.

Xiao, M., & Yi, H. (2020). Building an efficient artificial intelligence model for personalized training in colleges and universities. Computer Applications in Engineering Education.

Yau, J. Y. K., & Joy, M. (2010). An adaptive context-aware mobile learning framework based on the usability perspective. International Journal of Mobile Learning and Organisation, 4(4), 378–390.

Zhao, X., & Okamoto, T. (2011). Adaptive multimedia content delivery for contextaware u-learning. International Journal of Mobile Learning and Organisation, 5(1), 46–63.




DOI: https://doi.org/10.47213/bp.v4i1.105

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