Bibliographic record
Abstract
Student Services support, including learning skills assistance, can be integral in empowering learners. First-year students are expected to be self-directed in their learning, yet may have neither been challenged nor experienced negative consequences for a lack of perseverance. Academic skills professionals can be partners with teaching faculty in student success by helping to build transferable learning skills, especially for high-fail introductory courses. In this paper, I report on supplementary workshops developed to target fundamental skills with course-specific examples. This partnership included incentivizing academic support with both carrots and sticks; instructors in introductory biology strongly urged students receiving D grades or below on the first test to approach Student Services for support, while sociology faculty incorporated workshop attendance into the introductory course with participation grades. Following such incentivizing of learning skills, workshop attendance increased by 45%. In both courses, first test scores and high school averages for students attending workshops did not differ from students not attending workshops. However, students who attended learning skills workshops had significantly higher course grades, persistence, sessional grade point averages (GPAs), and cumulative GPAs than students not attending workshops. Controlling for high school average, each learning skills workshop attended was associated with a 0.11 to 0.27 increase in sessional GPA on a 4.3 point scale.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".