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Gap-analysis method for identifying gateway courses via predicted versus observed DFW rates and persistence gaps

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

A learning-analytics method using institutional student records. A binary logistic regression predicts the probability a student posts a D, F, or W grade using Student Type, life-cycle stage, prior cumulative GPA, and prior DFW fraction; predicted and observed rates are recorded per course and "the difference (Gap) between them calculated." A second gap compares persistence of students in a course to students in all courses of the same type at the same degree-path point, by student type and stage.

Accounts

  • Gap-analysis method for identifying gateway courses via predicted versus observed DFW rates and persistence gaps: A learning-analytics method using institutional student records. A binary logistic regression predicts the probability a student posts a D, F, or W grade using Student Type, life-cycle stage, prior cumulative GPA, and prior DFW fraction; predicted and observed rates are recorded per course and "the difference (Gap) between them calculated." A second gap compares persistence of students in a course to students in all courses of the same type at the same degree-path point, by student type and stage. (Bloemer et al. (2017))
  • Judge course effectiveness relative to predicted DFW rates for the enrolled population rather than by raw DFW rankings alone: The article argues institutional attention should go to courses whose observed outcomes deviate from expectations. It predicts DFW probability per student using student type, life-cycle stage, prior GPA and prior DFW fraction, then computes the "difference (Gap) between them" to rank courses. This avoids misplaced or harmful fixes on courses simply serving a difficult population. (Bloemer et al. (2017))

Claims

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Key Sources

  • Bloemer, W., Day, S., & Swan, K. (2017). Gap analysis: An innovative look at gateway courses and student retention. Online Learning, 21(3), 5-14. https://doi.org/10.24059/olj.v21i3.1233
Page metadata
Field Value
type research-method
id gap-analysis-method-for-identifying-gateway-courses-via-predicted-versus-observed-dfw-rate
title Gap-analysis method for identifying gateway courses via predicted versus observed DFW rates and persistence gaps
description A learning-analytics method using institutional student records.
status review
generated by process:settle-candidates on 2026-10-16
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