Research on the Instrumental Variable Mechanism of Computational Psychometrics in Engineering Teaching Assessment

Abstract

Since teachers typically focus on the quantitative analysis of the data when writing the analysis report on the achievement of course objectives, there is no guarantee that the final calculation result is valid. They should give equal consideration as to whether the source of the data is also reliable. This paper combines the emerging computational psychometrics and causal inference science, and mainly solves two engineering teaching problems. First, this paper proposes to use computational psychometrics as an instrumental variable to explore its deconfounding effect in teaching assessment, which can eliminate the influence of confounding factors in engineering experimental teaching assessment. Secondly, the scientific method of causal inference is used to calculate the causal effect factor of the experimental results on the test scores from the observation data. Then, characterize the influence of the experimental scores on the test scores, thus solving the cross-modal problem of the process data participation calculation. The method proposed in this paper cannot only ensure the reliability of the data source but can also unify the calculation mode so that the degree of achievement of the course objectives can be more accurately calculated, which is helpful for teachers to continuously improve the teaching level.