Emerging Model of Questioning through the Process of Teaching and Learning Electrochemistry
Bibliographic record
Abstract
Verbal questioning is a technique used by teachers in the teaching and learning process. Research in Malaysia related to teachers’ questioning in the chemistry teaching and learning process is more focused on the level of the questions asked rather than the content to ensure that students understand. Thus, the research discussed in this paper is intended to explore in-depth the types of questions posed by teachers when teaching electrochemistry. This topic was chosen as it is categorized as a difficult topic by both students and teachers. This research employed qualitative techniques in exploring teachers’ verbal questioning during the teaching process. Participants included five teachers teaching Chemistry Form 4 (Grade 10). The data were collected through non-participant observations and verbatim recordings during the teaching and learning process. The findings indicate that the types of teachers’ verbal questions when teaching electrochemistry can be categorized in two main areas: content and management questions. Content questions could be sub-divided into five sub-categories: linking questions, questions based on process, comparison questions, questions based on students’ observations, and questions based on terminology. As for the management questions, they consisted of four sub-categories: questions for probing, monitoring, and motivation as well as bilingual questions. The data analysis also showed that the content and management questions are complementary as both types are needed in the verbal questioning process during the teaching and learning of electrochemistry.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".