Facets of Conscientiousness in Relation to Academic Performance Across Programs of Study
Bibliographic record
Abstract
Conscientiousness and its six facets (competence, order, dutifulness, achievement striving, self-discipline and deliberation) were examined in relation to cumulative grade point average (GPA) for undergraduate university students. The aim was to determine the degree to which conscientiousness predicted a higher GPA for students in varying programs, and to compare these results to previous findings. The programs examined were psychology, business, science/engineering, and general arts (i.e. B.A. programs other than psychology). Students were screened using Johnson’s (2014) “IPIP-NEO-120” (a 120-item version of the International Personality Item Pool-NEO: which measures constructs similar to those in the NEO Personality Inventory). Multiple regression analyses of the data revealed variations in concurrent validity of the facets of conscientiousness across majors. For psychology students, only Competence predicted GPA. For science and engineering students, Competence, Dutifulness and Achievement Striving predicted GPA. For general arts, only Deliberation predicted GPA. None of the facets were significant predictors for business students. Conscientiousness was thus a significant predictor of GPA across all majors, but the key facets were dependent on the area of study. The only gender difference detected was at low conscientiousness, with females having a significantly higher GPA than males. Additional research is necessary to further explore the predictive validity of the other Big Five personality traits and their facets, for a wider range of academic majors on academic success. Implications involve the role personality traits could play to the decision to enrol in a specific program, and how professors may bettor teach/mentor students in different programs.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".