When the Journey is as Important as the Destination: Time-Averaged Retention as an Alternate Measure of Student Engagement and Program Impact

Heath Jones

doi:10.5204/ssj.v11i1.1464

Abstract

University retention and completion rates underestimate true levels of student participation because of their reliance on measurements taken at commencement (or census date) and end of a program. As a result, these statistical snapshots miss what happens in between, failing to capture the true reach of the teaching and learning process, as well as the effort and resources involved. This is problematic when these numbers drive debate over higher education policy or institutional decisions over resource allocation. Here we propose a way of turning retention statistics into a more meaningful measurement of student participation, that we term engagement. In the context of this article, engagement is a calculated quantity based on the time-averaged student retention of a program or course. We argue that it addresses the shortcomings of snapshot metrics and provides some much-needed insight into student participation. We motivate its adoption and illustrate its use with worked examples, as a guide to practitioners, researchers and policymakers in the field.

Highlights

Widening participation has seen significant increases in Australian tertiary student numbers (Gale & Parker, 2013), mirroring similar trends worldwide (OECD, 2019)
We define instantaneous student retention, R(t), as the fraction of students (0 ≤ R(t) ≤ 1) participating in a program or course at time t
If we value partial completion enough and the retention data are largely in hand, we argue that this small extra effort is justified

Summary

Introduction

Widening participation has seen significant increases in Australian tertiary student numbers (Gale & Parker, 2013), mirroring similar trends worldwide (OECD, 2019). The problem becomes a mathematical one: how best to quantify participation of students who complete some fraction of a course (or program1) of study without making it to the end. Common benchmarks used to quantify rates of completion vary between policy makers, institutions and researchers (e.g. Department of Education and Training, 2017; Hodges et al, 2013; Norton et al, 2018; Wild & Ebbers, 2002). Two programs with identical completion rates might have completely different patterns of retention This is a problem when such statistics drive policy debate or resourcing. The analysis in this article does not differentiate between courses or programs (because the mathematical reasoning is identical) Nor is it concerned with how activity is measured. They should note Eqn (8) in Section 5 summarising the calculation in words

Mathematical Motivation

Example Calculations of Engagement

Findings

Summary