A Data Visualization Model to Improve Academic Management in Higher Education Institutions: Design and Expert Validation

Higher education institutions (HEIs) generate substantial volumes of academic data, yet the visualization infrastructure required to transform this data into decision-relevant representations remains underdeveloped in most Latin American university contexts. This study addresses that gap through the design, development, and expert validation of the Institutional Data Visualization Model (IDVM) for academic management at Universidad Tecnológica Israel (UISRAEL). A mixed-methods design was employed, combining a quantitative survey instrument (N = 216) administered to faculty, students, and administrative staff with structured interviews with five institutional authorities analyzed thematically. Diagnostic findings revealed that perceived adequacy of data visualization practices ranged from moderate to adequate, with the lowest scores concentrated in accessibility and data provenance, and qualitative evidence confirming a pattern of data richness coexisting with representational poverty. The IDVM was developed through a four-phase iterative process aligned with design-based research methodology, operationalizing a three-level hierarchical visualization structure, six academic data domains aligned with national accreditation criteria, and an explicit data governance framework. Expert validation through a three-round Delphi process (n = 5 experts) produced a final Kendall concordance coefficient of W = 0.924 (p < 0.001). The IDVM provides a theoretically grounded, empirically derived, and locally adapted framework for improving academic data governance in Ecuadorian and Latin American higher education.