“Choose Your Classmates, Your GPA Is at Stake!”
Bibliographic record
Abstract
This article presents results from an investigation of the association between student academic performance and social ties. Based on social capital and networked learning research, we hypothesized that (a) students’ social capital accumulated through their course progression is positively associated with their academic performance and (b) students with more social capital have significantly higher academic performance (operationalized as grade point average). Both hypotheses were supported by results of an empirical study that analyzed 10 years of student course enrolment records ( N = 505) in a master’s degree program offered through distance education at a Canadian university. These results are consistent with previous studies that looked at social networks built through student interaction in classrooms or computer-mediated communication environments. The significance of this research lies in the simplicity of the method used to establish student social networks from existing course registration records readily available through an institution’s student information system. Direct implications of this research are that (a) study plans for students should consider investment in building new social ties in each course during degree programs and (b) readily available data about cross-class networks can be used in software systems supporting study planning.
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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".