神经网络认知测量在工程教学课程评价中的应用

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Abstract Due to unobtrusive confounding factors in the evaluation of engineering teaching curriculum, teachers cannot get reliable data when calculating the evaluation of the achievement of curriculum objectives, thus affecting the teachers to carry out the continuous improvement of teaching in the future. To solve this problem, this paper proposes a de-confounding method combining neural network cognitive measurement and causal inference science. Firstly, the neural network cognitive diagnosis model is constructed according to the students' daily practice data, and the students' mastery of knowledge points is taken as the measurement index of students' ability. Then, the students' ability assessment results are used as the mediating variable data in the causal inference model of this case. Finally, through the method of front door adjustment, the causality effect of actual course teaching on the evaluation of the achievement of the course goal is got. Taking the professional course Power Supply Technology for undergraduates majoring in electronic information in Xiangtan University in an academic year as a case, this paper revises the evaluation of the achievement of course objectives in that academic year, and obtains the actual average achievement evaluation of course objectives in that academic year as 88.92%. The results show that this method can effectively shield confounder data and help teachers get more reliable and fair evaluation of the achievement of curriculum objectives in actual teaching.

Key words： engineering teaching objectives of the course cognitive measurement neural network causal inference

Received: 20 October 2022

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http://dqjs.cesmedia.cn/EN/Y2023/V24/I2/52

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