Bibliographic record
Abstract
Research shows that carefully planned acceleration offers academic benefits with little social or emotional risk to high-ability learners. However, acceleration is underutilized and little is known about students’ motivations to accelerate. In this study, 21 high-ability high school students in Grades 11 and 12 took part in a structured conceptualization exercise that revealed why they chose to concurrently enroll in university courses. Participants brainstormed responses to a focus prompt, then structured the data by sorting and rating their responses. The structured data were analyzed using multidimensional scaling and hierarchical cluster analysis to produce a cluster map of participants’ motivations. In order of importance, key concepts included (a) university preparation, (b) demonstrating initiative, (c) getting ahead, (d) love of learning, (e) self-fulfillment, (f) seeking challenge, and (g) socializing. The key concepts were examined within a self-determination theory framework. Study findings provide a deeper understanding of high-achieving students’ views on concurrent enrollment. Educational and research implications are discussed.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".