Labatorials and Reflective Writing for a Better Understanding of Dynamics in High School
Bibliographic record
Abstract
Decades of research show that introductory physics students struggle to learn Newtonian concepts of force and motion. Conventional lecture method of instruction has been unable to improve students’ ideas and attitudes. This study examined the impact of combining Labatorials and Reflective Writing on high school students’ knowledge of Newtonian dynamics. \nParticipants are 210 secondary 5 (grade 11) students, from three private schools in Montreal, who took a physics course during 2017-2018 and 2018-2019. Their ideas and opinions about forces and learning physics were investigated, prior to and following the study, with: (a) the Discipline-focused Epistemological Beliefs Questionnaire; (b) the Force Concept Inventory (FCI); (c) a concept map focused on the relations between force and motion. Pre- and post- semi-structured interviews were conducted with 12 participants. The post interview required students to analyse a hands-on experiment about the two-way motion of a fan cart. Data was also collected from participants’ teachers throughout the duration of the study. \nResults from the FCI indicate a medium gain as calculated by Hake (1998) which is similar to those obtained when Interactive Engagement practices are used in teaching physics (Hake, 1998). The interviews with students as well as feedback from teachers showed that students preferred the combination of Labatorials with Reflective Writing to traditional labs. Preliminary analysis of concept maps completed in the post-test to those in the pretest indicate that students better connected concepts related to forces and motion. The gathered data and interviews indicate that the process of combining Labatorials with Reflective Writing improves students’ knowledge of the subject as well as their attitudes towards learning it.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".