Conditions of implementation of an inductive learning sequence about the periodic table in high school chemistry
Bibliographic record
Abstract
In chemistry education, understanding the structure and role of the Periodic Table has been linked to subsequent learning of key concepts such as the properties of chemical elements. However, the way teachers introduce the Periodic Table to high school students is mostly traditional, students learning to use it to predict properties of elements according to given rules (Ben-Zvi & Grenut, 2007). Such a way to proceed does not allow students to engage in authentic science activities. Thus our research pursues two main objectives. The first one is to conceive a learning sequence to engage students in activities where they have to propose their hypotheses and verify them against data. The second one is to study the condition of implementation of this learning sequence in high school chemistry course. The experimentation took place with 14 adult learners who followed a high school chemistry course in two successive periods of two hours each in an adult learning center. To study the implementation of the learning sequence, the main researcher held a research diary where he recorded his observations on the sequence of events. In it, he also wrote his reflections about the observed events and established links between his observations and the theoretical framework of the present research (Altrichter & Holly, 2005). Our results point out that the sequence engages students in developing the classifications as well as find arguments to test them as they discuss their ideas in small groups and later expose them during classroom discussion. We also discuss conditions of implementation, such as the importance of providing a way to register all the contributions of each team to help sharing and examining the various hypotheses. One key aspect concerns the ability of the teacher to find the proper balance between supporting students’ process of categorization while not interfering with it. As a conclusion, we discuss the advantages and the limits of the research and made suggestions for future research.
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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 | 0.015 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".