Educational Evaluation Information System Model of Political Ideological Big Data in Colleges and Universities

Abstract

This study presents the fundamental guidelines for developing an evaluation index system for political thought education in light of the shortcomings of the current system, including its limited evaluation objectives, singular evaluation methods, lack of pertinence of evaluation indexes, and subjectivity of evaluation standards. In parallel, big data-related technologies are being used to determine the indicators and index weight coefficients of the evaluation system of political thought teaching in institutions of higher learning. This is done on the basis of earlier research on big data and teaching evaluation. The decision tree method is used in this study to create a prediction model for teaching evaluation, which is then pruned based on “relative support” to categorize the sample data. The decision tree method is aimed at a large number of teachers’ information databases. The model is validated at the end. According to the experimental findings, this educational evaluation system is stable to a degree of 94.6 percent, and its evaluation accuracy can reach a level of 95.33 percent. The validity and reliability of the educational evaluation system developed in this study are confirmed by this result. Big data can therefore be used to evaluate political thought instruction in higher education institutions, which offers some fresh perspectives for related research.