Diagnosing grade inflation: a curriculum analytics approach to quality assurance in higher education

We examined how curriculum design and its implementation shape student outcomes through case studies from Chilean and Spanish universities. Using a Curriculum Analytics approach, we analyzed program-level grade distributions and introduced quantitative indicators to detect patterns in instructors' assessment practices. Courses relying on non-standard evaluations–driven by perceptions, limited rubrics, or few assessments–tend to inflate grades, granting higher marks than standard evaluations, which employ multiple independent assessments with rigorous rubrics. Grade inflation reduces differentiation between high- and low-performing students and artificially increases completion rates. Conversely, programs dominated by standard evaluations provide more reliable evidence of learning but often underestimate credit requirements for foundational courses, lowering completion rates and extending study duration. Our analysis revealed systematic correlations between gaps in credit allocation, assessment types, and performance indicators, with potential consequences for equity and funding in publicly financed systems. This study is, to our knowledge, the first to integrate program-level grade-distribution analytics with credit points estimation to quantitatively diagnose grade inflation and expose its link to instructors' assessment practices, offering actionable evidence for curriculum redesign and quality-assurance policy.