Teacher and Student Questions: A Case Study in Malaysian Secondary School Problem-Based Learning
Bibliographic record
Abstract
Problem-based learning (PBL) promotes high order questioning and stimulates student thinking, thus playing an important role in preparing students to face real-world challenges. Yet, PBL is an uncommon instructional strategy in Malaysian secondary school science classrooms. Occurrence of questioning in the traditional spoon-feeding classroom is low. Thus, the PBL model adapted from Barrows has been introduced. This article investigates whether PBL is able to promote high order questioning and thinking in the Malaysian science classroom. A PBL class with 1 teacher and 17 students divided into 4 groups was observed, video-and audio-recorded, and the verbatim were analysed. Questions are categorized into high order, low order, eliciting ideas, and evaluating ideas questions. Findings show that the percentage of student questions is 67.9% while for teacher questions is 32.1%. The amount of student questions per hour is relatively high at 8.2 questions per student. Nearly half of the classroom questions are low order questions (47.9%), such as clarification, verification, concept completion, disjunctive, definition, example, quantification, and feature specification questions. High order questions consist of 16.3%, which include causal antecedent, causal consequence, goal orientation, comparison, enablement, and reflective questions. Eliciting ideas questions raised by the teacher cover 8.8% while evaluating ideas questions by students cover 27.1%. This study shows that the PBL environment promotes active learning, student thinking, and questioning in the Malaysian science classroom. However, student and teacher questions should be enhanced to be at higher order level. Several suggestions to extend low order questions into high order questions are discussed in this paper.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".