Construction of an Ideological and Political Teaching Effectiveness Evaluation Index System for the Introduction to Big Data Course Based on the CIPP Evaluation Model

Abstract

Advances in big data and AI are steering educational assessment toward data - driven and intelligent methods. Evaluating the effectiveness of ideological and political teaching in the Introduction to Big Data course is vital for better integrating ideological and ethical elements into curricula. This study uses the CIPP evaluation model with big data analysis to create an evaluation index system for such teaching effectiveness. The system has four primary indicators, nine secondary, and twenty - seven tertiary indicators. Through iterative Delphi expert consultations, the indicators were refined for scientific validity. AHP was applied to assign weights, exploring the impact of each teaching phase and emphasizing the CIPP model's procedural and situational features. The system focuses on both immediate outcomes and the relationships among context, input, and process, providing a comprehensive view for assessing and improving IPC teaching effectiveness in big data and other academic areas.