Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.
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
We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technologyrich learning environment.Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for learning (control condition).Students' conceptualizations (task definitions) of the problem, self-regulatory processes, and mathematics achievement were then compared across the 2 conditions.To measure task definitions of the mathematics problem, students developed concept maps of the problem using a tablet application.To capture self-regulatory processes, students were asked to think out loud as they solved the problem.Results revealed that students in the learning by preparing to teach intervention developed a more detailed and better-organized concept map of the problem compared with students in the control condition.Students in the learning by preparing to teach intervention also engaged in more metacognitive processing strategies and had higher levels of mathematics problem solving achievement compared with students in the control condition.No differences were found, however, in planning and goal setting or in use of cognitive strategies across the 2 conditions.Implications of this research suggest students' initial task definitions may be a key factor in differences found when learning by teaching compared with solely learning for learning.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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