A Multimodal Framework for Explainable Evaluation of Soft Skills in Educational Environments
This addresses the problem of evaluating soft skills like decision-making and communication for educational stakeholders, but it is incremental as it builds on existing multimodal and fuzzy logic methods.
The paper tackled the challenge of unbiased soft skills assessment in higher education by developing a fuzzy logic and multimodal analysis framework, which effectively consolidated multiple data inputs to produce consistent assessments, showing that integrating modalities significantly improved the quality of soft skills scores.
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.