Constructing and validating an academic risk index for early identification of student nonpersistence
Description
This manuscript presents a transparent framework for identifying students who show early signs of academic difficulty. Rather than relying on a single indicator such as semester grade point average, it combines multiple semester-level measures from student information system data and converts them into clear risk indicators.
The framework groups students into actionable risk tiers and demonstrates the workflow with simulated data modeled on common higher education patterns. The paper is positioned as a reproducible methods template that institutions can adapt and test locally before using tier assignments for advising or intervention decisions.
What this paper contributes.
Builds a transparent academic risk index.
The method turns routinely collected student records into interpretable risk indicators and tiers.
Targets institutions with limited analytic capacity.
The workflow is intentionally reproducible and adaptable without requiring a complex predictive-modeling infrastructure.
Uses simulated data for method demonstration.
The paper shows how the index behaves under controlled conditions while avoiding claims of external predictive validity.
Supports local calibration before intervention.
Institutions are encouraged to test index behavior against their own retention outcomes before using it operationally.