Machine Learning Algorithm for Questionnaire-Based Student Learning Style Classification

Herman  Yuliansyah; Agung Tri  Lestari; Anton  Yudhana

doi:10.59395/ijadis.v7i1.1536

Authors

Herman Yuliansyah Department of Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Agung Tri Lestari Master Program of Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Anton Yudhana Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

DOI:

https://doi.org/10.59395/ijadis.v7i1.1536

Keywords:

Classification Data, Comparative Analysis, K-fold Cross Validation, Machine Learning, Student Learning Styles

Abstract

Identifying students’ learning styles is an important factor in supporting adaptive and data-driven learning. However, conventional methods based on manual questionnaires still have limitations in terms of efficiency and accuracy for data processing. This study presents a comparative analysis of machine learning algorithms to classify student learning styles based on questionnaire data. The dataset used consists of 1,170 student data with three learning style classes, namely visual, auditory, and kinesthetic. The four supervised learning algorithms used are Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbors. Model performance evaluation was conducted using 5-fold (80:20) and 10-fold (90:10) cross-validation with accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the Naïve Bayes algorithm has the most optimal and stable performance with the highest accuracy value of 90.60% in both validation scenarios. These findings indicate that machine learning-based classification approaches, particularly Naïve Bayes, are effective for identifying student learning styles and have the potential to support the development of adaptive and personalized learning systems.

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References

[1] D. Supit, M. Melianti, E. M. M. Lasut, and N. J. Tumbel, “Gaya Belajar Visual, Auditori, Kinestetik terhadap Hasil Belajar Siswa,” J. Educ., vol. 5, no. 3, pp. 6994–7003, Feb. 2023, doi: 10.31004/joe.v5i3.1487. DOI: https://doi.org/10.31004/joe.v5i3.1487

[2] S. Ramadandi and J. Jahring, “Student Learning Style Classification Using Naïve Bayes Classifier Method,” J. Teknol. dan Inf., vol. 10, no. 2, pp. 170–179, 2020, doi: 10.34010/jati.v10i2.3096. DOI: https://doi.org/10.34010/jati.v10i2.3096

[3] W. WAAM and P. HKS, “Identifying the Learning Style of Students Using Machine Learning Techniques: An Approach of Felder Silverman Learning Style Model (FSLSM),” Asian J. Res. Comput. Sci., vol. 17, no. 3, pp. 15–37, Jan. 2024, doi: 10.9734/ajrcos/2024/v17i3422. DOI: https://doi.org/10.9734/ajrcos/2024/v17i3422

[4] K. Daniel, M. M. Msafiri, F. Antony, and X. Wan, “Motivate Students for Better Academic Achievement: A Systematic Review of Blended Innovative Teaching and Its Impact on Learning,” Comput. Appl. Eng. Educ., vol. 32, no. 4, 2024, doi: 10.1002/cae.22733. DOI: https://doi.org/10.1002/cae.22733

[5] L. Rahmawati and S. Gumiandari, “Identifikasi Gaya Belajar (Visual, Auditorial Dan Kinestetik) Mahasiswa Tadris Bahasa Inggris Kelas 3F IAIN Syekh Nurjati Cirebon,” Pedagog. J. Pendidik., vol. 16, no. 1, pp. 54–61, 2021, doi: 10.33084/pedagogik.v16i1.1876. DOI: https://doi.org/10.33084/pedagogik.v16i1.1876

[6] M. A. Hassan, U. Habiba, F. Majeed, and M. Shoaib, “Adaptive gamification in e-learning based on students’ learning styles,” Interact. Learn. Environ., vol. 29, no. 4, pp. 545–565, May 2021, doi: 10.1080/10494820.2019.1588745. DOI: https://doi.org/10.1080/10494820.2019.1588745

[7] A. A. Nugraha and U. Budiyanto, “Adaptive E-Learning System Berbasis Vark Learning Style dengan Klasifikasi Materi Pembelajaran Menggunakan K-NN (K-Nearest Neighbor),” Technomedia J., vol. 7, no. 2, pp. 248–261, Sep. 2022, doi: 10.33050/tmj.v7i2.1900. DOI: https://doi.org/10.33050/tmj.v7i2.1900

[8] I. Martiana, “Peningkatan Hasil Belajar Siswa Dalam Mengikuti Pelajaran Sosiologi Melalui Strategi Pembelajaran Berdiferensiasi,” J. Locus Penelit. Dan Pengabdi., vol. 2, no. 6, pp. 500–506, 2023, doi: 10.58344/locus.v2i6.1194. DOI: https://doi.org/10.58344/locus.v2i6.1194

[9] E. J. Ngaga, E. Sulastri, F. Bukangdonu, I. Alip, K. W. Wae, and H. Hikmatiar, “Efektivitas Ekstrakurikuler Dan Dampak Pada Prestasi,” Bul. Edukasi Indones., vol. 2, no. 01, pp. 24–32, 2023, doi: 10.56741/bei.v2i01.254. DOI: https://doi.org/10.56741/bei.v2i01.254

[10] A. Komoder, L. Venaruzzo, and C. Young, “Up-Skilling Learners Through Story-Telling: Introducing the Learning Wave,” Ascilite Publ., 2023, doi: 10.14742/apubs.2023.468. DOI: https://doi.org/10.14742/apubs.2023.468

[11] M. Simatauw, “Pentingnya Pendidikkan Agama Kristen Dalam Menerapkan Teori Bloom Di Sekolah,” Ichtus J. Teol. Dan Pendidik. Kristiani, vol. 6, no. 2, pp. 69–77, 2025, doi: 10.63830/91nrh307. DOI: https://doi.org/10.63830/91nrh307

[12] H. A. Setiawan and H. Yuliansyah, “Aspect-Based Sentiment Analysis of User Reviews on the Game ‘Honkai: Star Rail’ Using Naïve Bayes Classifier,” SISTEMASI, vol. 13, no. 5, p. 1956, Sep. 2024, doi: 10.32520/stmsi.v13i5.4343. DOI: https://doi.org/10.32520/stmsi.v13i5.4343

[13] A. Salsabila, M. Nasution, and I. Irmayanti, “Implementation of the Naïve Bayes Algorithm to Predict New Student Admissions,” Build. Informatics, Technol. Sci., vol. 6, no. 1, pp. 421–429, 2024, doi: 10.47065/bits.v6i1.5363. DOI: https://doi.org/10.47065/bits.v6i1.5363

[14] M. Garonga and R. Tanduk, “Comparison of Naive Bayes, Decision Tree, and Random Forest Algorithms in Classifying Learning Styles of Universitas Kristen Indonesia Toraja Students,” J. Tek. Inform., vol. 4, no. 6, pp. 1507–1514, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1020. DOI: https://doi.org/10.52436/1.jutif.2023.4.6.1020

[15] P. Ramírez-Correa, J. Alfaro-Pérez, and M. Gallardo, “Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques,” Appl. Sci., vol. 11, no. 22, p. 10505, Nov. 2021, doi: 10.3390/app112210505. DOI: https://doi.org/10.3390/app112210505

[16] T. T. Huynh-Cam, L. S. Chen, and H. Le, “Using decision trees and random forest algorithms to predict and determine factors contributing to first-year university students’ learning performance,” Algorithms, vol. 14, no. 11, 2021, doi: 10.3390/a14110318. DOI: https://doi.org/10.3390/a14110318

[17] T. Sutikno, A. Fadlil, Sunardi, I. Riadi, and A. Yudhana, “A review of Naive Bayes and decision tree methods for predicting particle size distribution in pharmaceutical manufacturing,” J. Intell. Syst. Eng. Appl. Data Sci., vol. 1, no. 1, pp. 8–14, 2024.

[18] I. Arfiani and H. Yuliansyah, “A Hybrid Model of Graph Attention Networks and Random Forests for Link Prediction in Co-Authorship Networks,” Int. J. Adv. Data Inf. Syst., vol. 6, no. 2, pp. 276–289, 2025, doi: 10.59395/ijadis.v6i2.1382. DOI: https://doi.org/10.59395/ijadis.v6i2.1382

[19] M. David and M. A. Firdaus, “Defect Rate Prediction in Manufacturing Process Using K-Nearest Neighbor Algorithm,” Int. J. Informatics Econ. Manag. Sci., vol. 3, no. 2, p. 161, 2024, doi: 10.52362/ijiems.v3i2.1599. DOI: https://doi.org/10.52362/ijiems.v3i2.1599

[20] S. Nayak, M. Bhat, N. V. S. Reddy, and B. A. Rao, “Study of Distance Metrics on K - Nearest Neighbor Algorithm for Star Categorization,” J. Phys. Conf. Ser., vol. 2161, no. 1, p. 12004, 2022, doi: 10.1088/1742-6596/2161/1/012004. DOI: https://doi.org/10.1088/1742-6596/2161/1/012004

[21] S. Uddin, I. Haque, H. Lu, M. A. Moni, and E. Gide, “Comparative Performance Analysis of K-Nearest Neighbour (KNN) Algorithm and Its Different Variants for Disease Prediction,” Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-10358-x. DOI: https://doi.org/10.1038/s41598-022-10358-x

[22] D. Wijaya and A. R. Widiarti, “Batik classification using KNN algorithm and GLCM features extraction,” E3S Web Conf., vol. 475, p. 02012, Jan. 2024, doi: 10.1051/e3sconf/202447502012. DOI: https://doi.org/10.1051/e3sconf/202447502012

[23] M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,” J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 20–24, Jan. 2019, doi: 10.30591/jpit.v4i1.1253. DOI: https://doi.org/10.30591/jpit.v4i1.1253

[24] M. Hossin and M. . Sulaiman, “A Review on Evaluation Metrics for Data Classification Evaluations,” Int. J. Data Min. Knowl. Manag. Process, vol. 5, no. 2, pp. 01–11, Mar. 2015, doi: 10.5121/ijdkp.2015.5201. DOI: https://doi.org/10.5121/ijdkp.2015.5201

[25] H. Yuliansyah, R. A. P. Imaniati, A. Wirasto, and M. Wibowo, “Predicting Students Graduate on Time Using C4.5 Algorithm,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 1, p. 67, Apr. 2021, doi: 10.20473/jisebi.7.1.67-73. DOI: https://doi.org/10.20473/jisebi.7.1.67-73