Academic Success for Students in Postsecondary Education: The Role of Student Characteristics and Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Academic success is an important issue as employers are looking for individuals with a postsecondary education. But there are many important indicators of success besides grades. We conceptualized academic success at postsecondary as grade point average, acquisition of knowledge and skills, and overall satisfaction and examined how each conceptualization was predicted by student characteristics (perceived academic ability and drive to achieve) and experiences (academic and social integration). Using a 1-year longitudinal design, we found that perceived academic ability had a positive direct effect on grade point average and acquisition of knowledge and skills but not satisfaction, whereas drive had no direct relationships with the outcomes. Academic integration positively predicted all three indicators of success grades, but social integration was not associated with grades. Indirect effects were also noted. Our discussion highlights actions that postsecondary institutions can take to support students and considers how researchers should conceptualize student success.
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.
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 | 0.034 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it