Improving Student Data Accuracy: A Mixed-methods Action Research Study in Higher Education

Abstract

Aims: This study aimed to examine the institutional and human factors influencing student data accuracy at Kenya Assemblies of God EAST University (EAST), Kenya. It specifically assessed data entry processes, system integration, verification procedures, staff training, and data governance practices, with the goal of identifying practical strategies for improving data accuracy. Study Design: The study adopted a mixed-methods descriptive survey design guided by the Information Quality Theory. Place and Duration of Study: The research was conducted at Kenya Assemblies of God EAST University (EAST), Kenya, among staff involved in student data management during the active academic data processing period. Methodology: A total of 54 staff members responsible for student data entry, processing, verification, and reporting participated in the study. Stratified random sampling was used to ensure representation across departments. Data were collected using a structured questionnaire comprising Likert-scale and open-ended questions, administered electronically. Quantitative data were analyzed using descriptive statistics, while qualitative responses were analyzed through thematic analysis. Results: The findings revealed significant challenges affecting student data accuracy. Frequent data entry errors were reported by 70.3% of respondents, while 31.5% experienced difficulty transferring data across non-integrated systems, leading to duplication and inconsistencies. Verification processes were ineffective, with 76% indicating that errors persisted even after verification. Inadequate training was identified by 61.2% of respondents, and 66.6% reported unclear or inconsistently enforced data governance policies. These issues collectively undermined the reliability and consistency of student records. These findings are significant because they provide empirical evidence to guide higher education institutions in designing integrated, process-based, and governance-driven interventions for improving student data accuracy. Practically, the results show institutions can operationalize improvements by embedding automated data controls, integrating systems, and institutionalizing training and governance. Conclusion: Improving student data accuracy requires strengthening data entry controls, integrating information systems, enhancing verification mechanisms, providing continuous staff training, and enforcing clear data governance structures.