Exploring Existing Knowledge using Query Type of Questions to Launch and Sequentially Conduct a Math Lesson
Bibliographic record
Abstract
The underlying logic behind most of the things is a form of mathematics. Therefore, a grasp and ownership of mathematical concepts is key to understanding how things work, particularly at tertiary level of education. Given the importance of math, from registration records, it was found that approximately 1500, i.e., 16.3% of the current student population at a private university in Bangladesh, delayed taking the foundation course on math till the final year of study. Among these are students who are retaking the subject. Among reasons found are that students do not like mathematics, or are not interested and/or have a fear of math. Based on these apprehensions in mind, the lesson delivery methodology of an existing foundation mathematics course was redesigned. The Query Based Access to Neurons (QuBAN) methodology is an interactive brain-engaging teaching methodology that first taps existing knowledge of the learners, then utilizes this to build new knowledge. This study aims to evaluate the efficacy of the QuBAN approach. Before and after the course were evaluated for measuring changes in their perceptions, interests, views, and thoughts about mathematics and the new teaching methodology through the pre and post-questionnaires respectively. Half of the students found the usefulness of the methodology is between 6.84 to 7.5 out of 10. That is, the methodology has shown its effectiveness to reduce fear of mathematics in the students.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".