Online Learning: How Does It Impact on Students’ Mathematical Literacy in Elementary School?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to find out how to improve elementary school students’ mathematical literacy in online learning during the COVID-19 Pandemic. This study uses a pre-experimental method with a one-group pretest-posttest design. The population in this study were grade 5 students in one of the sub-districts in Bandung. The sample used random sampling criteria with a total of 50 students. The instrument used a mathematical literacy test and an online learning perception questionnaire. The data analysis measures descriptive and inferential statistics using Microsoft Excel and SPSS version 25. The results show that online learning during the COVID-19 Pandemic is going well, although in its implementation, there are still various obstacles and problems. Based on the results of the t-test that the sig. is 0.000. So, there are differences in mathematical literacy skills before and after online learning during the COVID-19 Pandemic. This is also supported by the N-Gain score of 0.35 in the medium category. This research is expected to contribute to education to create effective online learning and improve mathematical literacy skills, especially in elementary schools.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it