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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).

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.

- Weekly self-estimates by students of their understanding (see how soon they become predictive). Can students in fact privately tell how well they are going to do? Correlate these with marks in class exam (and lab exam); in January and in June.
- Get tutors to estimate how well their students will do.
Can tutors in fact privately tell how well their students are
going to do? If not every week, then say at week 6 and 12.
#### Other things that might predict performance

- Measure prior training and attainment in maths, general computing, programming. Measure this in some detail as the standard records are not necessarily fine-grained enough. In particular, learning about computers is NOT the same as learning to program; and learning programming may either be about learning the same language (and so the same vocabulary) OR might be analysed as having learned specific semantic (not notational) constructs: if-then, for, while, recursion, ....
- Measure aptitude. If we can find an aptitude test. One point of this is that it may be a tougher contrast by which to judge the (so far small) predictive power of other variables. But it is also of interest in its own right.
- Measure "Tinto" variables of "integration".
Tinto's is the single most referenced theory in the research literature on
student dropout and retention. Its central idea is that of "integration": it
claims that whether a student persists or drops out is quite strongly
predicted by their degree of academic integration, and social integration.
These evolve over time, as integration and commmitment interact, with dropouts
depending on commitment at the time of the decision. It remains to be seen
whether the theory applies to this course, and whether its concepts can be
usefully measured in practice. If they can, though, it may be possible to
design remedies for weakness on any given aspect.
(My notes on Tinto's concepts and possible extensions, are available.)

- Take measures of students' study habits: regularity, keeping up as the weeks go by, total time, devoting sufficient time to keep up, ...
- Measure each student's inner interest in the subject. I.e. attempt to measure instrinsic motivation.
- Perceptions by students of tutor quality: if tutors are a core part of the
course, then variations in tutor quality may have a big effect. On the other
hand, these perceptions may be caused by the students' success. Seeing
whether the perceptions correlate with exam results on the one hand, and
within groups on the other, would distinguish these possibilities.
#### Summary of kinds of measure

- Self-estimate of understanding of the material.
- Tutor's estimates of students' understanding
- Prior learning of programming / computing science
- Aptitude test
- Motivation for and interest in the subject
- Tinto variables of "integration"
- Study skills / practices
- Perceptions of tutor quality

- 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.

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

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