Science Mapping the Knowledge Base on Student Retention in Higher Education: A Bibliometric Review of Research Papers from 1914-2022
Bibliographic record
Abstract
In this study, we systematically review existing research on college student retention. It utilizes a total of 5277 publications indexed either in Scopus or Web of Science from 1914 to 2022. The results show that most of the research on student retention has been conducted in English-speaking countries such as the United States, Australia, the United Kingdom, and Canada. The United States produces almost two-thirds of scholarly publications worldwide. The term ‘retention’ is commonly used alongside ‘persistence,’ ‘attrition,’ ‘engagement,’ and ‘success.’ Moreover, the term higher education is associated with dropout, completion, and academic performance, as well as new methodological terms like data mining, machine learning, learning analytics, and logistic regression. Retention is also studied in fields such as nursing, engineering, and STEM. Special attention is given to community colleges due to higher dropout rates. Unlike the United States, Australia, the United Kingdom, and Canada, where higher education research on retention is extensive, countries like China and India, which have recently expanded their higher education systems, show a comparatively limited volume of research output concerning student retention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.041 | 0.191 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".