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Some local projects on retention/dropout

Here are some local projects on Tinto, retention, dropout.

Predictors of first year computing science dropout

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.

1. 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 the factors.

   • 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.

2. 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.

3. 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 courses.
   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)

4. 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.

5. Revision late on does not help (unlike for many other subjects where this learning strategy succeeds): understanding as you go seems to be crucial.

6. 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.

7. 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 organisers).

8. 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)

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 of failure. 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

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 persisting students. 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

This project was the first to do some substantial psychometrics on our Tinto inspired questionnaire. 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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