Evaluating the Effectiveness of Online Learning Methods with a Probabilistic Naive Bayes Approach
Abstract
Online learning methods become an important element in supporting the flexibility and effectiveness of teaching and learning process, especially through approaches such as Video Tutorial, Virtual Discussion, and Self-paced Reading. This research aims to evaluate the effectiveness of the three methods in improving students' engagement, comprehension, and learning motivation by utilizing Naive Bayes algorithm. The dataset used includes student data taken through questionnaires and teacher evaluation results, with variables such as material suitability, engagement, ease of access, and student exam results.
Through this approach, the research is able to predict the learning method that best suits students' needs based on the analyzed variables. The results show that Video Tutorial is the most effective method in supporting students' understanding and motivation. The implementation of this research is expected to help the development of a better online learning system in improving students' learning experience, and provide practical recommendations for educators in choosing the right learning method.
References
Alruwais, N., & Zakariah, M. (2023). Student-Engagement Detection in Classroom Using Machine Learning Algorithm. Electronics, 12(3), 731. https://doi.org/10.3390/electronics12030731
Ananda, E. R., Irawan, W. H., & Abdussakir, A. (2024). Strategi Meningkatkan Partisipasi Siswa dalam Pembelajaran Berhitung Matematika Melalui Penggunaan Game Edukasi Kartu Pintar. Al-Madrasah: Jurnal Ilmiah Pendidikan Madrasah Ibtidaiyah, 8(3), Article 3. https://doi.org/10.35931/am.v8i3.3634
Ayyoub, H. Y., & Al-Kadi, O. S. (2024). Learning Style Identification Using Semisupervised Self-Taught Labeling. IEEE Transactions on Learning Technologies, 17, 1093–1106. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2024.3358864
Boscolo, P., & Mason, L. (2003). Topic Knowledge, Text Coherence, and Interest: How They Interact in Learning From Instructional Texts. The Journal of Experimental Education, 71(2), 126–148. https://doi.org/10.1080/00220970309602060
DARYONO, D., FUAT, F., SUCHAINA, FIRMANSYAH, M. B., AHSANA, A., ROKHMAWAN, T., NURAISAH, R., & HADI, S. (2020). PANDUAN PEMBELAJARAN VIA SIMULASI DIGITAL (SIMDIG). Lembaga Academic & Research Institute.
Eberharter, K., Kormos, J., Guggenbichler, E., Ebner, V. S., Suzuki, S., Moser-Frötscher, D., Konrad, E., & Kremmel, B. (2023). Investigating the impact of self-pacing on the L2 listening performance of young learner candidates with differing L1 literacy skills. Language Testing, 40(4), 960–983. https://doi.org/10.1177/02655322221149642
Eom, S. B., & Ashill, N. J. (2018). A System’s View of E-Learning Success Model. Decision Sciences Journal of Innovative Education, 16(1), 42–76. https://doi.org/10.1111/dsji.12144
Febrian, M. A., & Nasution, M. I. P. (2024). Efektivitas Penggunaan Google Sites Sebagai Media Pembelajaran Kolaboratif: Perspektif Teoritis dan Praktis. Jurnal Pendidikan Islam, 11(2).
Fitri, D. A., & Damayanti, D. (2024). KOMPARASI ALGORITMA RANDOM FOREST CLASSIFIER DAN SUPPORT VECTOR MACHINE UNTUK SENTIMEN MASYARAKAT TERHADAP PINJAMAN ONLINE DI MEDIA SOSIAL. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 9(4), Article 4. https://doi.org/10.29100/jipi.v9i4.5608
Husaini, A.-, Hariyanti, I., & Raharja, A. R. (2024). Perbandingan Algoritma Decision Tree dan Naive Bayes dalam Klasifikasi Data Pengaruh Media Sosial dan Jam Tidur Terhadap Prestasi Akademik Siswa. Technologia : Jurnal Ilmiah, 15(2), Article 2. https://doi.org/10.31602/tji.v15i2.14381
Husna, M. (2024). Strategi Pembelajaran Berbasis Digital dalam Meningkatkan Kualitas Pembelajaran. Al-Faizi : Politik, Hukum Dan Bisnis, 2(2), Article 2.
Ilmi, M. B., Simanjuntak, N. C., Husna, S. A., Tari, R. K., & Chairunisa, H. (2024). ANALISIS KESALAHAN BERBAHASA PADA BUKU PRAKTIS TEKNIK JARINGAN KOMPUTER DI SMKS CERDAS MURNI TEMBUNG. IdeBahasa, 6(2), Article 2. https://doi.org/10.37296/idebahasa.v6i2.189
Kee, T., Zhang, H., & King, R. B. (2024). An empirical study on immersive technology in synchronous hybrid learning in design education. International Journal of Technology and Design Education, 34(3), 1243–1273. https://doi.org/10.1007/s10798-023-09855-5
Kiritani, K., & Kayano, T. (2024). Mitigating Structural Hallucination in Large Language Models with Local Diffusion. Research Square. https://doi.org/10.21203/rs.3.rs-4678127/v1
Liu, X.-Q., Wang, X.-C., Tao, L., An, F.-X., & Jiang, G.-R. (2024). Alleviating conditional independence assumption of naive Bayes. Statistical Papers, 65(5), 2835–2863. https://doi.org/10.1007/s00362-023-01474-5
Luo, Y., Han, X., & Zhang, C. (2024). Prediction of learning outcomes with a machine learning algorithm based on online learning behavior data in blended courses. Asia Pacific Education Review, 25(2), 267–285. https://doi.org/10.1007/s12564-022-09749-6
Maulana, B. A., Fahmi, M. J., Imran, A. M., & Hidayati, N. (2024). Analisis Sentimen Terhadap Aplikasi Pluang Menggunakan Algoritma Naive Bayes dan Support Vector Machine (SVM): Sentiment Analysis of Pluang Applications With Naive Bayes and Support Vector Machine (SVM) Algorithm. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(2), Article 2. https://doi.org/10.57152/malcom.v4i2.1206
Nurhayani, N., Asiri, F. R., Simarmata, R., & Barella, Y. (2024). Strategi Belajar Mengajar. Dewantara : Jurnal Pendidikan Sosial Humaniora, 3(2), 255–266. https://doi.org/10.30640/dewantara.v3i2.2644
Santi, R. N., Situmorang, R., & Iriani, T. (2024). Potensi Model Microlearning sebagai Strategi Pembelajaran Inovatif untuk Bahan Pembelajaran: Systematic Review. Didaktika: Jurnal Kependidikan, 13(4 Nopember), Article 4 Nopember. https://doi.org/10.58230/27454312.1281
Student-Engagement Detection in Classroom Using Machine Learning Algorithm. (n.d.). Retrieved December 26, 2024, from https://www.mdpi.com/2079-9292/12/3/731
Wong, Z. Y., Liem, G. A. D., Chan, M., & Datu, J. A. D. (2024). Student engagement and its association with academic achievement and subjective well-being: A systematic review and meta-analysis. Journal of Educational Psychology, 116(1), 48–75. https://doi.org/10.1037/edu0000833
