Bayesian Intelligent Tutoring System for Vocational High Schools
PDF (Inggris)

Kata Kunci

Bayesian Network
Intelligent Tutoring System
Vocational High Schools

Abstrak

The absence of individualized tutorials during regular school hours has resulted in a suboptimal learning method at Vocational High Schools(SMK), limiting students' ability to reach their optimum competency. Several Computerized self-study systems have been created as potential solutions to these challenges. Regrettably, a notable drawback of the system lies in its failure to address students' diverse range of abilities adequately. This study presents a proposed model for an Intelligent Tutoring System (ITS) utilizing the Bayesian Network (BN) at Vocational High Schools. The model aims to assess students' proficiency levels and deliver skill-based instructional materials tailored to individual students' abilities. This type of research is called research and development (R&D), to develop and know the validity of a product. The system under development will undergo trials within the Computer and Network Engineering (TKJ) program at SMK Negeri 4 Gowa. These trials will employ a quasi-experimental approach, explicitly utilizing a one-group pretest-posttest design.The findings indicated that there were notable disparities in the learning outcomes of students following the implementation of the proposed ITS. To put it otherwise, the proposed ITS has improved students' proficiency in Vocational High Schools. The evaluation outcomes suggest that the BN model had a significant level of accuracy, reaching 84%.
PDF (Inggris)

Referensi

Asri, V., Vitalocca, D., & Miru, A. S. (2017). Tinjauan Komparatif Lulusan SMK Kompetensi Keahlian Teknik Komputer Dan Jaringan Di Kota Makassar. Electronics, Informatics, and Vocational Education, 2.

Baharuddin, B., & Dalle, J. (2017). Interactive courseware for supporting learners' competency in practical skills. Turkish Online J. of Edu.Tech, 16(3), 88–99.

Carvalho, S. D., de Melo, F. R., Flôres, E. L., Pires, S. R., & Loja, L. F. B. (2020). Intelligent tutoring system using expert knowledge and Kohonen maps with automated training. Neural Computing and Applications, 32(17), 13577–13589. https://doi.org/10.1007/s00521-020-04767-0

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2015). Bayesian Data Analysis 3rd Edition. Chapman and Hall/CRC. https://doi.org/10.1201/b16018

Han, J., Zhao, W., Jiang, Q., Oubibi, M., & Hu, X. (2019). Intelligent Tutoring System Trends 2006-2018: A Literature Review. 2019 Eighth International Conference on Educational Innovation through Technology (EITT), 153–159. https://doi.org/10.1109/EITT.2019.00037

He, Y., Hui, S. C., & Quan, T. T. (2009). Automatic summary assessment for intelligent tutoring systems. Computers & Education, 53(3), 890–899. https://doi.org/10.1016/j.compedu.2009.05.008

Huang, L., Cai, G., Yuan, H., & Chen, J. (2021). A hybrid approach for identifying the structure of a Bayesian network model. Expert Systems with Applications, 131, 308–320. https://doi.org/10.1016/j.eswa.2019.04.060

Javidian, M. A., Wang, Z., Lu, L., & Valtorta, M. (2020). On a hypergraph probabilistic graphical model. Annals of Mathematics and Artificial Intelligence, 88(9), 1003–1033. https://doi.org/10.1007/s10472-020-09701-7

Karaci, A., Ibrahim, H., Bilgici, G., & Arici, N. (2018). Effects of Web-based Intelligent Tutoring Systems on Academic Achievement and Retention. International Journal of Computer Applications, 181(16), 35–41. https://doi.org/10.5120/ijca2018917806

Keleş, A., Ocak, R., Keleş, A., & Gülcü, A. (2009). ZOSMAT: Web-based intelligent tutoring system for teaching–learning process. Expert Systems with Applications, 36(2), 1229–1239. https://doi.org/10.1016/j.eswa.2007.11.064

Khurniawan, A. (2023). Grand Design Pengembangan Teaching Factory dan Technopark di SMK.

Kularbphettong, K., Kedsiribut, P., & Roonrakwit, P. (2015). Developing an Adaptive Web-based Intelligent Tutoring System Using Mastery Learning Technique. Procedia - Social and Behavioral Sciences, 191, 686–691. https://doi.org/10.1016/j.sbspro.2015.04.619

Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420

Majid, N. W. A., Ridwan, T., Fauzi, A., & Hikmawan, R. (2019). Integrating of E-learning to Improve Students Competence in Vocational School. Proceedings of the 5th UPI International Conference on Technical and Vocational Education and Training (ICTVET 2018). https://doi.org/10.2991/ictvet-18.2019.17

Marcot, B. G., & Hanea, A. M. (2021). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics, 36(3), 2009–2031. https://doi.org/10.1007/s00180-020-00999-9

Marcot, B. G., & Penman, T. D. (2019). Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling & Software, 111, 386–393. https://doi.org/10.1016/j.envsoft.2018.09.016

Rahmi Ramadhani, S. P. I. M. P., & Nuraini Sri Bina, S. P. M. P. (2021). Statistika Penelitian Pendidikan: Analisis Perhitungan Matematis dan Aplikasi SPSS. Prenada Media. https://books.google.co.id/books?id=0WFHEAAAQBAJ

Ramírez-Noriega, A., Juárez-Ramírez, R., & Martínez-Ramírez, Y. (2017). Evaluation module based on Bayesian networks to Intelligent Tutoring Systems. International Journal of Information Management, 37(1), 1488–1498. https://doi.org/10.1016/j.ijinfomgt.2016.05.007

Saepulloh, A. R., Sumarna, N., & Permana, T. (2018). STUDI TENTANG KETERCAPAIAN STANDAR UJI KOMPETENSI SISWA DALAM MATA PELAJARAN PEMELIHARAAN KELISTRIKAN DI SMK. Journal of Mechanical Engineering Education, 3(2), 154. https://doi.org/10.17509/jmee.v3i2.4544

Scanagatta, M., Salmerón, A., & Stella, F. (2019). A survey on Bayesian network structure learning from data. Progress in Artificial Intelligence, 8(4), 425–439. https://doi.org/10.1007/s13748-019-00194-y

VanLEHN, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369

Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng, Z., Yang, D., Li, M., Zhu, X., & Fei, C. (2023). Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective. Education and Information Technologies, 28(7), 9113–9148. https://doi.org/10.1007/s10639-022-11555-x

Xu, J., Zhang, Y., & Miao, D. (2022). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064

Authors who publish in PENA TEKNIK: Jurnal Ilmiah Ilmu-ilmu Teknik agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.