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

Jared D. T. Guerrero-Sosa, Francisco P. Romero, Víctor Hugo Menéndez-Domínguez, Jesus Serrano-Guerrero, Andres Montoro-Montarroso, Jose A. Olivas

TL;DR

The paper tackles the difficulty of objectively and explainably assessing soft skills in higher education. It introduces a multimodal framework based on the Granular Linguistic Model of Phenomena (GLMP) that integrates video, audio, and text data with fuzzy logic to produce interpretable, linguistically grounded assessments. An evaluation tool combines deep learning perception with GLMP-driven reasoning and large-language-model–generated reports to deliver transparent feedback while preserving privacy. Results from two undergraduate cohorts demonstrate alignment with instructor ratings and highlight modality effects, supporting the framework's potential for scalable, explainable soft-skill Evaluation in educational settings.

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.

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

TL;DR

The paper tackles the difficulty of objectively and explainably assessing soft skills in higher education. It introduces a multimodal framework based on the Granular Linguistic Model of Phenomena (GLMP) that integrates video, audio, and text data with fuzzy logic to produce interpretable, linguistically grounded assessments. An evaluation tool combines deep learning perception with GLMP-driven reasoning and large-language-model–generated reports to deliver transparent feedback while preserving privacy. Results from two undergraduate cohorts demonstrate alignment with instructor ratings and highlight modality effects, supporting the framework's potential for scalable, explainable soft-skill Evaluation in educational settings.

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.
Paper Structure (25 sections, 6 figures, 13 tables)

This paper contains 25 sections, 6 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Theoretical Framework and Related Work
  3. Challenges in Soft Skills Evaluation
  4. Multimodal Analysis in Educational Environments
  5. Granular Linguistic Model of Phenomena
  6. Methods
  7. Research Target
  8. Research Design
  9. Group 1: Machine Learning Course
  10. Group 2: Human-Computer Interaction Course
  11. Evaluation Tool
  12. Input Data
  13. Perception Process
  14. Reasoning Process
  15. Reporting
  16. ...and 10 more sections

Figures (6)

  • Figure 1: Dimensions, attributes and measures related to soft skills
  • Figure 2: Video Evaluation Tool
  • Figure 3: Recommended distances for close-up video
  • Figure 4: Example of a Soft Skill Report
  • Figure 5: Tool Interface - Video of the learner's task and soft skills results
  • ...and 1 more figures