Semantic-Enabled Visual Analytics for Explainable Research Evaluation in Higher Education

The increasing complexity of evaluation in the higher education setting, along with the growing availability of diverse data sources, has emphasized the need for more transparent and interpretable assessment processes and results. Current evaluation systems frequently depend on fragmented indicators and black-box procedures, hindering evidence-based decision-making. To address these challenges, this study proposes a framework that integrates semantic web technologies, visual analytics and multiple-criteria decision analysis. An ontology is developed to capture evaluation dimensions, criteria, indicators and relationships, enabling interoperability among heterogeneous data sources, including bibliometric databases, institutional repositories, and open science infrastructures. The framework further incorporates a multicriteria method to support transparent weighting and aggregation of evaluation criteria, as well as interactive visual analytics mechanisms designed according to cognition-informed principles. It was applied within a higher education evaluation scenario to support the integration, analysis, and interpretation of multidimensional assessment data. The framework was evaluated using a dataset comprising seven (7) evaluation entities, covering fifty-two (52) evaluation criteria across five (5) dimensions, a cross-cutting dimension and the overall ranking. The ontology-based ELECTRE III analysis involved twenty-one (21) pairwise comparisons and produced seven (7) final ranking positions, with no initial incomparable pairs requiring resolution. At the dimension level, 30 ranking positions were obtained from 105 pairwise comparisons, resulting in five (5) incomparable pairs, four (4) of which were resolved through the proposed SDR mechanism. These results demonstrate the framework’s ability to differentiate the evaluated entities at both the overall and dimension levels while providing an explicit mechanism for resolving ranking incomparabilities. The application aims to facilitate the integration of heterogeneous evaluation data sources, preserve an auditable decision trail, improve the transparency of evaluation procedures, and enhance the interpretation of complex evaluation outcomes. The proposed framework contributes to explainable academic evaluation by combining semantic modeling, decision support, and visual analytics, supporting more informed and transparent institutional decision-making.