Engineering Student Retention and Attrition Literature Review
Bibliographic record
Abstract
The University of Saskatchewan, similar tomany engineering colleges, would like to improve studentretention. With that in mind, a literature review wasundertaken to summarize current peer reviewed literaturerelated to engineering student retention and attrition inan attempt to better understand the potential structuralcauses, processes, and student characteristics that maycontribute to student success or attrition. Through asystematic search of several major databases using thekeywords “engineering and attrition or retention,” andafter narrowing the scope to peer reviewed articleswritten between 2005 and the present, each article’sabstract was read and evaluated. Forty-five papers weredeemed to be highly relevant, and were thus included inthe literature review. Preliminary trends that haveemerged in this review are: the potential causes of highattrition rates in engineering schools, various methodsthat have been used to determine the causes of attrition,interventions that have been implemented and stories oftheir success/failure, and attributes that have been foundto correlate with student attrition or success. This paperis an attempt to organize this body of research into asingular source that can be referenced by engineeringeducators or researchers who wish to increase studentretention and improve the educational experience of theirstudents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".