A Multimodal Framework for Explainable Evaluation of Soft Skills in Educational Environments

TL;DR

This paper introduces a fuzzy logic-based multimodal framework for explainable soft skills assessment in higher education, combining linguistic modeling and computational perception to improve reliability and interpretability.

Contribution

It presents a novel multimodal evaluation framework that integrates fuzzy logic and granular linguistic modeling for transparent soft skills assessment.

Findings

Framework effectively consolidates multimodal data for soft skills evaluation.

Integration of multiple modalities improves assessment accuracy.

Assessment process is transparent and understandable to stakeholders.

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.