Results of Using the Take-away Technique on Students’ Achievements and Attitudes in High School Physics and Physical Science Courses
Bibliographic record
Abstract
The Take-away Technique was used in High School Physics and Physical Science courses for the unit on Newtonian mechanics in a teacher (6) by grade level (4) partially crossed design (N=272). All classes received the same IE instructional treatment. The experimental group (classrooms) did a short Takeaway after each class summarizing the key concepts and points covered in the class, whereas the control group (classrooms) wrote a short evaluation of what they liked and disliked about the class. The experimental group performed better than the control group on the standardized Force Concepts Inventory achievement measure (Hestenes et al., 1992) using Hake normalized gain scores by a quarter to two-thirds of a standard deviation. Neither group showed the typical decline in attitudes towards Physics that occurs in IE approaches. The experimental group students gave the same positive benefits for the Takeaway technique as given in a previous study done with college undergraduates in a psychology course with those students in the control group citing much lower rates of these benefits and primarily emotional expression benefits. Given its design, the increased achievement observed in this study could be attributed directly to the Take-away technique as opposed to other rival hypotheses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".