Who Is the Successful University Student? An Analysis of Personal Resources
Bibliographic record
Abstract
A number of factors have been identified in the research literature as being important for student success in university. However, the rather large body of literature contains few studies that have given students the opportunity to directly report what they believe contributes to their success as an undergraduate student. The primary purpose of this study is to explore students’ descriptions of the personal resources that they use to succeed while attempting to reach their goals as well as those personal characteristics or obstacles that keep them from reaching their goals. Prominent themes supportive of student success included having a future orientation, persistence, and executive functioning skills such as time management and organization. Results also demonstrate that stress, inadequate academic skills, and distractions are detrimental to student success in university. This study is unique in that it gathers the content data directly from the population of interest; it is one of the few qualitative studies of undergraduate students’ self-generated perceptions. Implications for university administrators and academic counsellors and directions for future research are discussed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".