Intelligent Assessment Model for Educational Management Decision Support Based on Linguistic Feature Extraction
Keywords:
Educational Management; Language Feature Extraction; Aspect-Based Sentiment Analysis; Multi-Attribute Decision Making (MCDM); Fully Consistent Method (FUCOM); Spherical Fuzzy Set (SFS)Abstract
Background: The expansion of digital campuses, massive open online courses (MOOCs), and blended learning environments has resulted in a substantial increase in unstructured student-generated text, including course evaluations, learning feedback, and online forum discussions. Such information offers an important basis for understanding teaching effectiveness, student satisfaction, and weaknesses in course design. Nevertheless, conventional educational management decision-support systems remain largely dependent on structured questionnaires and single-score assessments, limiting their capacity to represent and quantify the multiple dimensions of sentiment expressed through natural language. Decision-making in complex educational settings also frequently constitutes a multi-attribute decision-making (MCDM) problem because comparisons among teaching alternatives are often characterised by uncertainty, ambiguity, and hesitation. Method: To address these methodological and practical constraints, this study develops a hybrid intelligent evaluation framework integrating natural language processing (NLP) with fuzzy MCDM. The framework is designed to support systematic decision-making in educational management. Attribute-based sentiment analysis (ABSA) is initially employed to analyse linguistic information within student evaluations, identify key teaching attributes, including teaching design and teacher–student interaction, and classify the associated sentiments as positive, neutral, or negative. A mapping mechanism subsequently transforms the extracted sentiment information into spherical fuzzy set (SFS) parameters, enabling uncertainty and neutral sentiment to be represented effectively. Indicator importance is established through an integrated subjective–objective weighting procedure. Subjective weights are derived using the fully consistent method (FUCOM), which reduces the cognitive demands placed on experts during pairwise comparisons while preserving decision consistency. Objective weights are obtained through the information entropy method using the spherical fuzzy scoring function. The alternatives are then comprehensively assessed and ranked through the spherical fuzzy technique based on ideal solution similarity ranking (SF-TOPSIS). Results: Empirical validation was performed using large-scale, authentic student evaluation data covering five core courses at an institution of higher learning. The findings demonstrate that the proposed framework can effectively identify and quantify course performance across four principal attribute dimensions. Application of the FUCOM-SF-TOPSIS model revealed substantial variation in overall teaching quality among the evaluated courses, with the best-performing course achieving a closest coefficient (CC) of 0.8532. Sensitivity analysis also demonstrated that the principal decision outcomes remained highly robust despite considerable changes in the coefficients assigned to subjective and objective weights. In comparison with existing MCDM approaches, the proposed method showed notable advantages in representing fuzzy information and controlling consistency deviations. Conclusion: The proposed intelligent evaluation framework provides a systematic mechanism for transforming unstructured linguistic information into a structured mathematical decision-making system. Integrating artificial intelligence with operational research methods extends the interdisciplinary application of these approaches within educational management. The framework also offers methodological support for educational administrators seeking to enhance teaching quality, optimise resource allocation, and develop sustainable educational strategies.
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