Student Behaviour Analysis To Detect Learning Styles Using Decision Tree, Naïve Bayes, And K-Nearest Neighbor Method In Moodle Learning Management System

Santi Tiodora Sianturi, Umi Laili Yuhana

Abstract


A learning management system (LMS) manages online learning and facilitates inter- action in the teaching and learning processes. Teachers can use LMS to determine student activities or interactions with their courses. Everyone learns uniquely. It is necessary to understand their learning style to apply it in students’ learning activi- ties. One factor contributing to learning success is the use of an appropriate learning style, which allows the information received to be appropriately conveyed and clearly understood. As a result, we require a mechanism to identify learning styles. This study develops a learning style detection system based on learning behavior at the LMS of Christian Vocational School Petra Surabaya for the subject of Network System Administration using the Decision Tree, Naïve Bayes, and K-Nearest Neigh- bor. The results of the study showed that the Decision Tree method could better detect and predict learning styles, namely using the 80:20 train split test, which obtained an accuracy of 0.96 process time of 0.000998 seconds, while the K-Fold 10 Cross-Validation test obtained an accuracy of 0.98 and a processing time of 0.04033 seconds.

Keywords


Decision Tree; K-Nearest Neighbor; Learning Management System; Naïve Bayes; Student Learning Style

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References


Graf S, Kinshuk, Liu TC. Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour. In: 2008 Eighth IEEE International Conference on Advanced Learning Technologies IEEE; 2008. p. 482–286. https://doi.org/10.1109/icalt.2008.84.

Popescu E. Diagnosing Students' Learning Style in an Educational Hypermedia System. In: Cognitive and Emotional Processes in Web-Based Education IGI Global; 2009.p. 187–208. https://doi.org/10.4018/978-1-60566-392-0.ch011.

Feldman J, Monteserin A, Amandi A. Automatic detection of learning styles: state of the art. Artificial Intelligence Review 2014 may;44(2):157–186. https://doi.org/10.1007/s10462-014-9422-6.

Viola S, Graf S, Kinshuk, Leo T. Analysis of Felder-Silverman Index of Learning Styles by a Data-Driven Statistical Approach. In: Eighth IEEE International Symposium on Multimedia (ISM'06) IEEE; 2006. p. 959—-964. https://doi.org/10.1109/ism.2006.30.

Ikawati Y, Rasyid MUHA, Winarno I. Student Behavior Analysis to Detect Learning Styles in Moodle Learning Management System. In: 2020 International Electronics Symposium (IES) IEEE; 2020. p. 501—-506. https://doi.org/10.1109/ies50839.2020.9231567.

Lwande C, Muchemi L, Oboko R. Identifying learning styles and cognitive traits in a learning management system. Heliyon 2021 aug;7(8):e07701. https://doi.org/10.1016/j.heliyon.2021.e07701.

Kolekar SV, Pai RM, Pai M. Adaptive User Interface for Moodle based E-learning System using Learning Styles. Procedia Computer Science 2018;135:606–615. https://doi.org/10.1016/j.procs.2018.08.226.

Aissaoui OE, Madani YEAE, Oughdir L, Allioui YE. Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles. Procedia Computer Science 2019;148:87–96. https://doi.org/10.1016/j.procs.2019.01.012.

Rasheed F, Wahid A. Learning style detection in E-learning systems using machine learning techniques. Expert Systems with Applications 2021 jul;174:114774. https://doi.org/10.1016/j.eswa.2021.114774.

Pasina I, Bayram G, Labib W, Abdelhadi A, Nurunnabi M. Clustering students into groups according to their learning style. MethodsX 2019;6:2189–2197. https://doi.org/10.1016/j.mex.2019.09.026.

Bernard J, Chang TW, Popescu E, Graf S. Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Systems with Applications 2017 jun;75:94–108. https://doi.org/10.1016/j.eswa.2017.01.021.

Crockett K, Latham A, Whitton N. On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. International Journal of Human-Computer Studies 2017 jan;97:98–115. https://doi.org/10.1016/j.ijhcs.2016.08.005.

Heidrich L, Barbosa JLV, Cambruzzi W, Rigo SJ, Martins MG, dos Santos RBS. Diagnosis of learner dropout based on learning styles for online distance learning. Telematics and Informatics 2018 sep;35(6):1593–1606. https://doi.org/10.1016/j.tele.2018.04.007.

Bajaj R, Sharma V. Smart Education with artificial intelligence based determination of learning styles. Procedia Computer Science 2018;132:834–842. https://doi.org/10.1016/j.procs.2018.05.095.

Costa RD, Souza GF, Valentim RAM, Castro TB. The theory of learning styles applied to distance learning. Cognitive Systems Research 2020 dec;64:134–145. https://doi.org/10.1016/j.cogsys.2020.08.004.

Shi K, Qin H, Sima C, Li S, Shen L, Ma Q. Dynamic Barycenter Averaging Kernel in RBF Networks for Time Series Classification. IEEE Access 2019;7:47564–47576. https://doi.org/10.1109/access.2019.2910017.

Cao G, Downes A, Khan S, Wong W, Xu G. Taxpayer Behavior Prediction in SMS Campaigns. In: 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC) IEEE; 2018. p. 19–23. https://doi.org/10.1109/besc.2018.8697317.

Mahardhika AA, Saptono R, Anggrainingsih R. Sistem Klasifikasi Feedback Pelanggan Dan Rekomendasi Solusi Atas Keluhan Di UPT Puskom UNS Dengan Algoritma Naive Bayes Classifier Dan Cosine Similiarity. Jurnal Teknologi & Informasi ITSmart 2016 sep;4(1):36. https://doi.org/10.20961%2Fits.v4i1.1806.

Jauhari D, Hanafi A, Yuniarsa MFA, Satria AR, H LH, Cholissodin I. Prediksi Nilai Tukar Rupiah Terhadap US Dollar Menggunakan Metode Genetic Programming. Jurnal Teknologi Informasi dan Ilmu Komputer 2016 dec;3(4):285. https://doi.org/10.25126%2Fjtiik.201634235.




DOI: http://dx.doi.org/10.12962/j20882033.v33i2.13665

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