How longitudinal datasets can shed light on higher education. A South African application

The primary focus of this study is the tracking of students through the Higher Education system of South Africa, using student unit-records of the Higher Education Management Information System (HEMIS). It is very difficult, if not impossible, to track students' progress as they flow through the system with the data that is readily available. Although data of students are collected, such student-level data, generally are not freely available or available in the right format. Longitudinal data coverage is a key requirement in order to facilitate the process to track the movement of students through the Higher Education System - longitudinal data coverage is a dataset that includes data gathered of the same student for different years. There is no consistent and integrated national data source that accurately shows data for each individual student of each Higher Education Institution for different years in the same dataset. Individual institutions also cannot count on their own data system alone to produce meaningful estimates of student progress and attainment, because students change institutions and drop out of the system, but may drop back into the system at a later stage (but not necessarily at the same institution).

This study deals with two specific objectives:

(a)  To indicate the processes involved in creating a longitudinal student unit-record dataset.

(b)  Do a cohort analysis of Higher Education first year students for a particular cohort and following them over a period of 10 years determining how successful they were.

With this longitudinal data coverage and cohort analysis method one can thus determine exactly how many students of a specific cohort dropped out without any qualification, how many graduated and how many are still in the system, but have not received any qualifications at all. The main findings of the cohort analysis will also be discussed.