Students’ interest, engagement, and achievement in online high school science courses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Though documented extensively using self-report measures—especially in post-secondary course contexts—the relations among students’ interest, engagement, and academic achievement in high school science courses has been studied far less—especially when considering online versions of these courses. Self-reported interest collected at the beginning of the semester, process measures of emotional, behavioural, and cognitive engagement measured with textual analysis using the Linguistic Inquiry and Word Count (LIWC) measures of online discussion threads and time spent on the learning management system were collected throughout the semester, and final course grades were collected at the end of the semester. A sample of 622 high school students were recruited from five STEM courses to participate in this study. Results indicate that interest predicts behavioural engagement, behavioural and cognitive engagement predict final course grade, and interest predicts final course grade. Findings extend research by describing the role of engagement in online high school STEM courses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it