Scientific Argumentation in Chemistry Education: Implications and Suggestions
Bibliographic record
Abstract
The Malaysia Education Blueprint 2012-2025 reveals the aspiration of government to prepare Malaysian children to meet the challenges of a 21st century economy. Nonetheless, Malaysia has a long way to go to achieve this target. PISA (2015) result indicates that Malaysian students have problems in reasoning skills. To achieve the target, Educational Blueprint advocates infusion of inquiry-based instruction in classrooms for students to acquire critical thinking skill. Critical thinking skill is a 21st century learning skill the students need to possess in today’s global economy. This skill includes the ability of individual to reason effectively. Scientific argumentation is a skill to promote critical thinking of students. Being the essential element of scientific inquiry and important activity in scientific reasoning, scientific argumentation helps students to develop and refine scientific knowledge. It is imperative to implement scientific argumentation in science classrooms. The purpose of this paper aims to raise some issues, including the results of previous studies about the impact of scientific argumentation on science achievement, the rationale for focusing on monological models in the three classification of argumentation models, and address the issues about the appropriateness of Toulmin argument model (an example of monologoical models), which is prevailed in science education to promote students’ scientific argumentation skills. Finally, this paper will outline some suggestions regarding implementation of Toulmin argument model to promote scientific argumentation in chemistry education.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".