The Impact of Philosophical Inquiry Method on Classroom Engagement and Reasoning Skills of Low Achievers
Bibliographic record
Abstract
This research project attempted to investigate the impact of applying philosophical inquiry method of teaching onclassroom engagement and reasoning skills of low achievers. Low achievers are those who have the potential tosucceed but lagged behind because of several factors that demotivate them to perform at their highest ability. In thisstudy, low achievers were students who failed or obtained the lowest grades in previous standardized schoolexamination. They were 22 students aged 12-13 years old from a school in Gombak district, Malaysia. The studentswere observed and video recorded while participating in discussing the questions they had formulated in response tothe given stimulus materials. Many assumed and projected that these students would not succeed in school and life;and would not have the intelligence to engage in discussion that employed higher order thinking. However, thefindings revealed that when low achievers were given opportunities to voice out their opinions in dialogic pedagogy,they demonstrated the ability to be focused and engaged in classroom discussion. Furthermore, this pedagogy hasproven effective in stimulating higher order thinking or reasoning skills among low achievers. Specifically, this studyfound indicators of behavioral, emotional and agentic engagement among low achievers; and demonstrated that lowachievers were capable of asking higher order thinking questions, clarifying meanings, giving examples, makingconclusion and inductive reasoning, distinguishing and classifying ideas.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".