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Some local projects on retention/dropout
Department of Psychology,
University of Glasgow.
Here are some local projects on Tinto, retention, dropout.
Predictors of first year computing science dropout
Matt Roddan (2002) "The determinants of student failure and attrition in
first year computing science". (Undergraduate project report, Psychology
Department, University of Glasgow.)
Its main purpose was to look for factors which might predict which students
were most likely to fail an introductory computer programming course, with a
view in future to targeting staff intervention in time.
- It makes the point that most of the available literature in the area looks
at things at the level of organisations or whole countries. However much of
the action, both possible attempts at correction and the active causal
factors, are not at the level of countries or universities, but at the
department (and hence subject) level. Consequently research should, like
this study, be focussed at this level. The reasons for this include:
- There are large differences in dropout rates between departments
in the same institution. Averaging across them cannot explain this; and
conversely, studying such variations must afford new evidence distinguishing
- Most remedial actions will be at the department level: the locus of most
actions at universities.
- This is consistent with Tinto's theory, which explains retention and
dropouts in terms of social and academic integration. Academic integration
depends mainly on the disciplinary and departmental teaching practices.
Social integration may depend upon institutional practices (college, student
union, student residential structures) but also upon the presence or absence
of departmental structures such as field trips, reading parties, and so on
which affect how cohesive a class becomes.
- Of the many factors tested, including many measures of attendance, few
showed a statistically significant relationship with exam performance, and the
few that did showed too low a correlation to explain much of the variance.
These will not be of great practical use for identifying students at risk of
failing with a view to early intervention.
- The sole exception was the student's own self-estimate of how well they
understood the material (correlation 0.7, next biggest correlation was 0.39).
N.B. this is very similar to the findings that deep as opposed to surface
approaches to learning are predictive of success in introductory programming
Fincher, S., Baker, B., Box, I., Cutts, Q., de Raadt, M., Haden, P., Hamer,
J., Hamilton, M., Lister, R., Petre, M., Robins, A., Simon, Sutton, K.,
Tolhurst, D., Tutty, J. (2005)
Programmed to succeed?: a multi-national, multiinstitutional study of introductory programming courses
(Computing Laboratory Technical Report 1-05, University of Kent, Canterbury, UK)
Simon et al. (2006)
"Predictors of success in a first programming course"
Proceedings of the Eighth Australasian Computing Education Conference
(ACE 2006) pp.189-196Ê (Hobart, Australia)
- Learning computer programming really does seem to require understanding,
which is the defining mark of deep learning as opposed to shallow learning.
Those who did not understand the material, particularly the early material,
gradually "lost it" and did poorly. Effort and hours spent may or may not be
necessary, but were no substitute for actual, achieved understanding.
- Revision late on does not help (unlike for many other subjects where this
learning strategy succeeds): understanding as you go seems to be
- Most staff believe that previous teaching in computing (e.g. at school) is
of no benefit. This project showed some indications both for but also against
this view, suggesting another look at the issue may be worthwhile.
- The (new) lab exam in the course studied seems to fail to test what was
intended (contrary to the original expectation and intention of the course
- An attempt was made to get students to reflect on their time management
by filling in a personal timetable to show how their time went.
This largely failed as a data gathering instrument for the project due to very
low response rate. Yet interviews showed that at least for one student, it
was a powerful and beneficial prompt to reflection.
Predictors of first year computing science dropout (2)
Sarah Rebecca Black (2003)
"Predictors of first year computing science student failure"
(Undergraduate project report, Psychology Department, University of Glasgow.)
N.B. the appendices are missing from this version, but the questionnaire is
Following up a project by Roddan (2002), a number of variables were
investigated with the aim of building a predictive model of students at risk
Students' own self-estimate of how well they understood the material
again correlated well with eventual exam results, and became a better predictor
as the term progressed.
Instruments utilising the Tinto Student Integration Model (1975),
indicated that academic integration factors explained a significant amount of
the variance in first year student exam performance. Results are presented
and discussed, and recommendations for further research are made.
Interviewing post-dropout students
Lockhart,P. (2004) "An investigation into the causes of student dropout
behaviour" (Dept. of Psychology, University of Glasgow).
This study of student dropout at Glasgow University has the special feature of
being based on recruiting actual dropouts and comparing them to matched
persisters. It is hard to find published studies that use anything other than
This study tested four separate explanations for student dropout: Tinto's
concept of integration, personality, self-efficacy, and homesickness.
Overall the results suggest that academic integration is more important than
social integration (in this sample and university), especially if readiness to
get to know staff members is counted as academic rather than social; but that
ability to organise oneself to study may be another important factor
separating persisters from dropouts.
Tinto inspired questionnaire
Neil Duncan (2006)
"Predicting Perceived Likelihood of Course Change, Return to University
Following Withdrawal, and Degree Completion in Glasgow University Students"
(Dept. of Psychology, University of Glasgow).
This project was the first to do some substantial psychometrics on our Tinto
Its earlier derivation or rationale is
here; and a version of it is
on the web.
Participants studying psychology, law, English literature and biology from all
years of study completed an on-line questionnaire. This measured the
predictive variables of current and past residence, year of study, alcohol
use/attitude, confidence in course choice, student self-esteem, academic and
social integration in university, social integration outside university,
social support, academic self-confidence, goal and institutional commitment,
and the outcome variables of how much they have thought about changing course,
their perceived likelihood of degree completion, and the likelihood of
returning to university/college if leaving their present course. It was found
that thinking about changing subject was significantly predicted by low
academic integration, belief that course choice was not well informed,
distance from Glasgow before starting university, and low social integration
outside university. Perceived likelihood of degree completion was
significantly predicted by year of study, goal commitment, low extraversion,
belief that course choice was well informed, low conscientiousness, student
self-esteem and a lack of understanding of the work-grade link. Finally,
perceived likelihood of returning to university/college if leaving present
course was significantly predicted by year of study, distance from Glasgow
before starting university, openness, low understanding of the work-grade
link, goal commitment, low extraversion, and social integration within
university. It appears that academic and goal related concerns influence
students in making drop out decisions more than do social concerns. The
findings are discussed in relation to the life-span theory of control
(Heckhausen & Tomasik, 2002) and other recent theories on drop out, and
suggestions for future research are proposed.
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