Teaching Strategies and Students’ Performance in Mathematics in a Borderless Classroom
Bibliographic record
Abstract
As the educational sector shifted from traditional to borderless classrooms, having discussions through virtual platforms became the most popular option to keep the learning going amid the pandemic. This study investigated the teaching strategies of mathematics college instructors in college on the second-year mathematics students’ performance in borderless classrooms. It also determined how well the mathematics instructors adjusted to the situation, as evidenced by how and what teaching strategies they used in their classes. Fifteen mathematics instructors were recruited for the study using a random sampling technique. Findings revealed that some teachers were unfamiliar with various tools and platforms. Using the Pearson correlation coefficient, there is a significant positive relationship between the teaching strategies in the borderless classrooms and the student’s performance in mathematics r (13) = .49, p< .04. The Strategic Intervention Applied in Asynchronous Teaching (SIAAT) was proposed by which will ensure that students will continue to receive asynchronous learning in borderless classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".