6 June 2003 ............... Length about 900 words (6,000 bytes).
This is a WWW document maintained by
Steve Draper, installed at http://www.psy.gla.ac.uk/~steve/localed/rblack.html.
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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:
Predictors of first year computing science student failure
(Department of Psychology, University of Glasgow).
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:
Depending on what is found, there could be a number of different kinds of
action taken in future:
- Change selection methods for admission to the course
- Identify weak students on the course and alert them, or concentrate
teaching resources on them.
- Identify weak and strong points in the course delivery, and modify that.
Any participant who would like feedback of any kind can obtain it from Rebecca
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.
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