Explainable AI to understand study interest of engineering students

被引:4
|
作者
Ghosh, Sourajit [1 ]
Kamal, Md. Sarwar [2 ]
Chowdhury, Linkon [3 ]
Neogi, Biswarup [1 ]
Dey, Nilanjan [4 ]
Sherratt, Robert Simon [5 ]
机构
[1] JIS Univ, Comp Sci & Engn, Kolkata, India
[2] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, Australia
[3] East Delta Univ Technol, Comp Sci & Engn, Chattogram, Bangladesh
[4] Techno Int New Town, Comp Sci & Engn, Kolkata, India
[5] Univ Reading, Dept Biomed Engn, Reading, England
关键词
Explainable AI; Belief rule base; SP-LIME; PCA;
D O I
10.1007/s10639-023-11943-x
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Students are the future of a nation. Personalizing student interests in higher education courses is one of the biggest challenges in higher education. Various AI and ML approaches have been used to study student behaviour. Existing AI and ML algorithms are used to identify features for various fields, such as behavioural analysis, economic analysis, image processing, and personalized medicine. However, there are major concerns about the interpretability and understandability of the decision made by a model. This is because most AI algorithms are black-box models. In this study, explain- able AI (XAI) aims to break the black box nature of an algorithm. In this study, XAI is used to identify engineering students' interests, and BRB and SP-LIME are used to explain which attributes are critical to their studies. We also used (PCA) for feature selection to identify the student cohort. Clustering the cohort helps to analyse the between influential features in terms of engineering discipline selection. The results show that there are some valuable factors that influence their study and, ultimately, the future of a nation.
引用
收藏
页码:4657 / 4672
页数:16
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