🤖 AI Summary
To address the substantial bias and poor interpretability inherent in current soft skills assessment practices in higher education, this study proposes an interpretable evaluation framework integrating multimodal perception with fuzzy logic. Methodologically, it pioneers the fusion of a Granular Linguistic Model of Phenomena (GLMP) with computer vision–driven facial expression and gesture recognition to enable fine-grained modeling and explicit quantification of uncertainty for competencies such as decision-making, communication, and creativity. The primary contributions are: (1) the first semantic-interpretability–enabled and uncertainty-transparent soft skills assessment paradigm; (2) empirical validation showing significant improvements in scoring consistency (+32.7%) and expert comprehensibility among undergraduate students; and (3) demonstrably superior assessment quality from multimodal integration versus unimodal approaches, yielding traceable, educationally actionable outputs.
📝 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.