Enterprise AI Analysis
A Multi-dimensional Evaluation Research on the Learning Outcomes of Economics in the Context of Digitalization and Intelligence
This paper introduces a novel multi-dimensional evaluation model for economics learning outcomes in the digital intelligence era, addressing the limitations of traditional methods. Utilizing the Fuzzy Network Analysis Method (FANP), it constructs a hierarchical structure to evaluate aspects like digital intelligence literacy, theoretical knowledge integration, problem-solving, and economic ethics. Empirical tests with economics students validate its effectiveness in assessing comprehensive capabilities and identifying teaching strengths/weaknesses, providing a scientific tool for educational reform and precise intervention.
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Improved Accuracy in Assessing Student Capabilities
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Higher Precision in Teaching Intervention
Deep Analysis & Enterprise Applications
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Methodology
Evaluation Model
Empirical Findings
This category focuses on the Fuzzy Network Analysis Method (FANP), its principles, and its application in constructing the multi-dimensional evaluation model. It explains how triangular fuzzy numbers are used to handle subjective fuzziness and how network analysis addresses interdependencies among indicators to calculate comprehensive weights.
FANP Model Construction Process
Analyze Characteristics & Construct Index System
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Establish Expert Team & Criteria
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Construct Fuzzy Pairwise Comparison Matrices
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Calculate Local Weights
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Calculate Supermatrix & Weighted Supermatrix
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Calculate Comprehensive Weights
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Core Dimensions of Evaluation Model
This section details the four core dimensions of the evaluation model: 'Digital Intelligence literacy and Application Ability', 'Integration and internalization of Theoretical Knowledge', 'Modeling and Solution of Complex Economic Issues', and 'Digital Intelligence Economic Ethics and Critical Thinking'. It also covers the specific network layer indicators for each dimension, outlining what is measured and why.
| Control Layer Indicator | Network Layer Indicator | Interpretation |
|---|---|---|
| Digital intelligence literacy and tool application ability A |
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| Depth of integration and internalization of economic theories B |
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Network Layer Indicators Defined
This section presents the results of the empirical tests conducted with economics major students. It analyzes the comprehensive index weights derived from the FANP model, highlighting which capabilities (e.g., digital intelligence literacy, problem-solving) receive greater emphasis. The findings demonstrate the model's accuracy in depicting student capabilities and identifying teaching strengths and weaknesses.
Case Study: Economics Student Evaluation
Problem: Traditional evaluation models centered on final grades struggled to comprehensively measure students' growth in aspects like literacy and critical thinking in the digital intelligence era.
Solution: The multi-dimensional FANP evaluation model was applied to economics major students in domestic universities. The model constructed a four-dimensional feedback system: 'digital intelligence literacy - theoretical internalization - problem-solving - ethical judgment'.
Outcome: The model placed greater emphasis on digital intelligence literacy (A1) and problem-solving abilities (C1, C2, C3), accurately reflecting students' comprehensive capabilities. It identified areas for targeted teaching reform, moving beyond a theoretical emphasis to practical application and ethical considerations in the digital economy.
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Highest Weighted Indicator: Data Acquisition & Processing Capability (A1)
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Your AI Implementation Roadmap
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Phase 1: Model Customization & Data Collection
Tailor the multi-dimensional FANP model to your specific educational context and collect relevant student performance data. This includes defining specific indicators and establishing data acquisition protocols.
Phase 2: Expert Evaluation & Weight Calculation
Convene an expert panel to provide fuzzy judgments on indicator interdependencies. Utilize the FANP method to calculate local and comprehensive weights for all evaluation dimensions.
Phase 3: Diagnostic Analysis & Reform Strategy
Analyze the evaluation results to identify student strengths and weaknesses. Develop targeted teaching reforms and intervention strategies based on the insights gained from the model.
Phase 4: Continuous Improvement & Feedback
Implement the reforms and establish a continuous feedback loop. Regularly re-evaluate the model's effectiveness and adjust teaching practices to foster ongoing improvement in learning outcomes.
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