Abstract
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.
Abstract (translated)
在迅速变化的教育环境中，对软技能进行公正评估是一个重要的挑战，尤其是在高等教育领域。本文提出了一种采用现象颗粒语言模型结合多模态分析的模糊逻辑方法，用于评估本科生的软技能。通过利用计算感知，这种方法能够系统地分解复杂的软技能表达，捕捉细微的行为特征，并解决其内在不确定性，从而提高解释性和可靠性。 研究团队使用开发的一款工具对本科学生进行了实验，该工具可以评估决策能力、沟通能力和创造力等软技能。这款工具能识别并量化人类互动中的微妙方面，如面部表情和手势识别。研究结果表明，所提出的框架能够有效整合多种数据输入，生成有意义且一致的软技能评估结果。这证明将多模态融入评估过程显著提高了软技能评分的质量，使评估工作对教育相关方更加透明易懂。
URL
https://arxiv.org/abs/2505.01794
