An approach for evaluating course acceptance based on Bayesian network Online publication date: Mon, 20-Feb-2023
by Ye Tao; Ying Jin; Jie Zhang
International Journal of Intelligent Internet of Things Computing (IJIITC), Vol. 1, No. 4, 2022
Abstract: In view of the difficulty in the evaluation of students' acceptance in the construction of general courses in colleges and universities, it analyses the key problems that need to be considered in the construction of courses and the difficulties encountered in the evaluation of course acceptance. It presents an approach for the course acceptance evaluation based on the Bayesian network. This approach is constructed on the set of attributes abstracted by profiling the student backgrounds. Considering the difficulty degree of the course, as well as the relationship between the attributes of the students and the difficulty degree of the course, it applies the Bayesian network to model and quantified the course acceptance. Taking the construction of big data analysis course in colleges and universities as an example, through the model verification method based on subjective belief, it is proven that the model is reasonable and sensible. Finally, it analyses the adaptability of the model in big data scenario.
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