Data Quality Frameworks in Educational Assessment: Ensuring Scoring Integrity at Scale

Abstract

Educational assessment systems generate complex, high-volume data that must meet rigorous standards of accuracy and fairness before informing high-stakes decisions. This article examines structured data quality frameworks as a foundational requirement for scoring integrity in large-scale assessment environments, where manual validation methods are insufficient to address the scale and diversity of errors that emerge across distributed data pipelines. Drawing on literature spanning data governance, psychometric measurement, streaming validation architectures, and process data analysis, the article characterizes the principal categories of assessment data failure including range violations, categorical inconsistencies, timestamp anomalies, and duplicate identifiers and traces the mechanisms through which these errors propagate into subgroup reporting and equity metrics. A layered validation methodology is presented, encompassing ingestion-level data validation, cross-system reconciliation, statistical anomaly detection, and psychometric integration, with particular attention to the diagnostic transparency and field-level auditability that high-stakes reporting environments demand. The article further addresses the transition from error detection to systematic remediation, arguing that automated correction pipelines embedded within governance architectures, followed by iterative revalidation, are essential for producing defensible, accurate, and complete assessment records at the scale modern programs require.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8213