An Examination of High School Students' Understanding of Learning in a Computer Applications Class
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
Students' voices have not been extensively heard in research in computer education. In this study, a secondary school teacher of computer applications (the first author) explores with her students their ideas and perceptions about learning in her class, asking them about how they learn best and what approaches and teaching strategies they find most conducive to learning. Analysis of qualitative data collected through use of questionnaires, interviews, and a focus group suggests that, given the opportunity, students can articulate a variety of ideas about their learning, engage in metacognitive activity, identify their strengths and difficulties in learning, and make suggestions about how to improve learning. Issues of cognitive load and the purposes and goals of instruction in computer applications are discussed. The findings inform not only the teaching and learning of computer applications courses, but teaching and learning more generally.
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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 | 0.013 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it