Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data.

Area of Science:

• Educational Technology
• Data Science in Education
• Higher Education Pedagogy

Background:

• Limited research exists on integrating big data with ideological and political (IP) teaching quality evaluation.
• Current IP teaching quality assessment methods lack multi-value consideration and systematicity.
• Big data analysis offers potential for more rigorous and scientific teaching quality assessment.

Purpose of the Study:

• To develop a comprehensive big data-driven model for evaluating IP teaching quality in colleges.
• To address the limitations of traditional evaluation methods by incorporating multi-value indicators.
• To enhance university management systems and overall education quality through scientific assessment.

Main Methods:

• Collected big data on teaching quality using distributed algorithms.
• Integrated multi-value indicators into the quality evaluation system, emphasizing multi-value theory.
• Employed Hierarchical Analysis (AHP) and Fuzzy Comprehensive Evaluation (FCE) for data analysis and model validation.

Main Results:

• The evaluation model, applied to a university's educational data, indicated excellent IP teaching quality.
• The model successfully tested the education index system and validated indicator weight distribution.
• The comprehensive evaluation model demonstrated superiority over traditional methods.

Conclusions:

• The proposed big data model provides a more holistic analysis of IP teaching quality.
• Evaluation results can inform both overall teacher quality assessment and single-factor analysis.
• Findings support targeted strategies for improving instructional approaches and educational quality.