用多模态分析与模糊逻辑评估学生软技能，让评分更透明可信。
A Multimodal Framework for Explainable Evaluation of Soft Skills in Educational Environments
- 融合语音、表情、手势等多模态数据，用模糊逻辑建模软技能表现。
- 在本科生中测试显示，多模态融合使评分一致性提升显著。
- 适合教育评估者使用，特别关注过程透明与可解释性的场景。
在快速发展的教育环境中，软技能的无偏评估尤其在高等教育中面临挑战。本文提出一种基于模糊逻辑的方法，结合粒度语言模型与多模态分析，评估本科生的软技能。通过计算感知，该方法对复杂软技能表现进行结构化分解，捕捉细微行为特征，处理其内在不确定性，从而提升评估的可解释性与可靠性。实验使用自研工具，在本科生中评估决策力、沟通能力与创造力等软技能，识别并量化面部表情、手势等交互细节。结果表明，该框架能有效整合多源数据，生成一致且有意义的评估结果，证明多模态融合显著提升软技能评分质量，使评估过程对教育相关方透明可理解。
原文摘要 · Abstract (English)
In the rapidly evolving educational landscape, the unbiased assessment of soft skills is a significant challenge, particularly in higher education. This paper presents a fuzzy logic approach that employs a Granular Linguistic Model of Phenomena integrated with multimodal analysis to evaluate soft skills in undergraduate students. By leveraging computational perceptions, this approach enables a structured breakdown of complex soft skill expressions, capturing nuanced behaviours with high granularity and addressing their inherent uncertainties, thereby enhancing interpretability and reliability. Experiments were conducted with undergraduate students using a developed tool that assesses soft skills such as decision-making, communication, and creativity. This tool identifies and quantifies subtle aspects of human interaction, such as facial expressions and gesture recognition. The findings reveal that the framework effectively consolidates multiple data inputs to produce meaningful and consistent assessments of soft skills, showing that integrating multiple modalities into the evaluation process significantly improves the quality of soft skills scores, making the assessment work transparent and understandable to educational stakeholders.
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