Why information systems and software engineering students enter and leave their study programme:a factor model and process theory
Bibliographic record
Abstract
The issues that influence students' decisions to enter and drop out of university IT programmes are a major concern for universities worldwide.The low enrolment of women in IT studies has received considerable attention from the academic world.This doctoral thesis aims to contribute to alleviating these problems.The primary contribution of this thesis is the laying out of implications for theory and practice in relation to the high student dropout rates in IT programmes.To elucidate this phenomenon, previous research on student dropout rates has advanced various factor models that explain or predict the dropout tendencies of university students.Although these studies enhance our understanding of the reasons students drop out of Computer Science (CS) courses, university studies, and online learning programmes, I found no research that describes the process that causes students to drop out of university.Such a process viewpoint is important given that students' decision to abandon a programme is not a static phenomenon, but a complex and dynamic occurrence.This phenomenon develops through a number of stages.As an initial step in filling the gap in research, I analysed qualitative interviews that centred on 40 Information Systems and Software Engineering (IS/SE) students who dropped out of the programme.I also conducted a second round of interviews with nine of these students to collect more accurate information on their motivation and emotions at the time they decided to drop out.On the basis of the interviews, I inductively developed a process theory approach, drawing from van de Ven (1992) and van de Ven and Poole (1995).The proposed process theory explains the trajectories that prompt university students to abandon the IS/SE programme.It also explains the course that the dropout process takes after decisions have been made.The findings reveal potential research directions in student dropout, and provide new insights into the reasons students abandon IS/SE studies.On the basis of the results, I formulate strategies for preventing student dropout.The second contribution of this thesis is that it sheds light on the factors that influence students' decision to enter IT programmes.Previous studies have been conducted in the US, Canada, and Australia, but only a few have been devoted to Europe.Not much research has been done on the Scandinavian context.To address this problem, I analysed the qualitative responses of 64 female sixth form students regarding their attitudes towards studying IT disciplines, including CS, IS, and SE.We also examined their perspectives on IT as a profession.This study extends the literature by offering new information on why females shun CS or IS careers and what attitudes they hold about these disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".