Last changed 6 June 2003 ............... Length about 900 words (6,000 bytes).
This is a WWW document maintained by Steve Draper, installed at

Web site logical path: [] [~steve] [localed] [this page]

Project on predictors of student performance in first year programming

This page is a few notes about a followup to Matt Roddan's project, being done by Rebecca Black.

Below are provisional plans, and not all the aspects described here will necessarily be acted on this year. The completed project report is: Black,R. (2003) Predictors of first year computing science student failure (Department of Psychology, University of Glasgow).

Main idea

The overall approach of both Matt Roddan's project and this one is to look for things that might give an early indication of a student being likely to do badly in the course overall. If we could find any such early indicators, then it might subsequently be possible to help prevent this and improve the performance of such students. We are in principle interested in a very wide range of possible indicators. We may use data gathered in different ways: some paper questionnaires, some questions asked in lectures using the handsets, and so on. Students will be informed about each data collection method separately: this page offers some overall information about the project.

The first idea in this particular project is to test a tantalising, but fragile, finding of Matt Roddan's: that the only factor with good predictive power (correlation 0.7) for whether a student would do well in the class exam on level 1, semester 1 computer programming was their own self-estimate of how well they understood the material.

While we are at it, we will also explore more potential predictive factors, taking Matt Roddan's explorations of these still further.

All such potentially predictive factors will be tested for correlations with "outcome measures" such as exam performance, course performance; and perhaps some extra measures of our own of how well students get on e.g. motivation, whether they feel interested, ... If we could discover early predictors of which students were most likely to do badly, we could alert them and their tutors in time to make a difference.


Measure / investigate all of these:

Possible applications

Depending on what is found, there could be a number of different kinds of action taken in future:

Feedback, questions

Any participant who would like feedback of any kind can obtain it from Rebecca Black (

Both Rebecca Black and Steve Draper will be happy to answer any questions about this.


The identity of individual students is, and will be kept, confidential to the investigators, Rebecca Black and Steve Draper, and will be used only to link answers to other information gathered about a student at other times. The data will not be used in any way related to assessment, and will not be available to staff teaching the course except as overall totals. If anyone does not feel comfortable in answering any of the questions put to them, they may stop at any time.

Web site logical path: [] [~steve] [localed] [this page]
[Top of this page]