Online Learning: How Does It Impact on Students’ Mathematical Literacy in Elementary School?
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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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".