Students’ Need Satisfaction Profiles: Similarity and Change over the Course of a University Semester
Bibliographic record
Abstract
Self‐determination theory positions the satisfaction of students’ needs for autonomy, competence, and relatedness as important determinants of various educational outcomes. In this study, we identify subpopulations of students characterized by distinct configurations of need satisfaction in the educational context, and assess the extent to which the nature of these configurations, and students’ individual profiles, remain stable over the course of a university semester. We also examine the role of perfectionism in the prediction of profile membership, and how these profiles relate to a variety of educational outcomes. A total of 521 first‐year undergraduate university students completed our measures at the beginning and end of a university semester. We identified five need satisfaction profiles, which remained unchanged over the course of the study. Students characterized by higher levels of self‐oriented perfectionism were more likely to be a member of a profile characterized by high levels of relatedness and global needs satisfaction associated with average levels of competence need satisfaction (“Globally Satisfied and Highly Connected” profile) relative to the other ones, and into the “Globally Satisfied” profile relative to the “Globally Dissatisfied, Highly Connected, and Competence Deficient” profile. Finally, the “Globally Dissatisfied, Highly Connected, and Competence Deficient” profile was associated with the least desirable outcomes (the lowest levels of students’ interest toward their studies, satisfaction, and attendance, and the highest levels of dropout intentions).
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".