Bayesian Network Student Modelling on Intelligent Tutoring System

DOI

10.2991/978-94-6463-084-8_26How to use a DOI?

Keywords

Intelligent Tutoring System; Bayesian Network; Object Oriented Programming

Abstract

Intelligent Tutoring System (ITS) is an electronic tutorial that has intelligence for adapting learning materials. It adjusts the contents of learning materials depending on the users’ requirements. This research developed an ITS for Object Oriented Programming course that is based on Bayesian Network as its inference engine. It has run well which is shown by the variation of the study tracks of each student based on the Bayesian Network Student Model. This tutorial is also divided into two part, there are basic parts that contains basic programming technique and the advanced part that provides the concepts of Object Oriented Programming. Based on those two parts, this study wants to examine their differences of them. By the Levenshtein distance, the highest distance between students’ ways of learning is 11 and its average is 5.385 for the basic part of Programming and 4.8 for the advanced part of the advanced part. The advanced part also has a positive value of skewness of the frequency distribution, it is 0,81 (left skew), which means that this distance is majority short. Whereas the basic of programming has a negative value that is −0.68 (right skew), in the other words, it shows that the elementary part has a longer distance of learning way. Data of the distances of advanced material parts concentrated on the second quartile by median is 4, whereas the basic part, by median is 6, data is concentrated in the third quartile. Mean Opinion Score shows that students are more interested to use the ITS than the classical Tutoring System, it is because the ITS has an average value is 8 (very agree) and the classical tutorial is 7 (agree). Besides that, the other criteria such as user-friendly, speed, report response, and appearance have 8 scores or very agree, and only a criterion has 7 (agree) in the stability.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Heri Wijayanto
AU  - I G. P. Suta Wijaya
AU  - Siti Nurmutmainnah
AU  - Ida Bagus Ketut Widiartha
AU  - Ramaditia Dwiyansaputra
PY  - 2022
DA  - 2022/12/26
TI  - Bayesian Network Student Modelling on Intelligent Tutoring System
BT  - Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
PB  - Atlantis Press
SP  - 298
EP  - 310
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-084-8_26
DO  - 10.2991/978-94-6463-084-8_26
ID  - Wijayanto2022
ER  -