Online tutoring in pandemic: An investigation on students’ mathematics anxiety and learning motivation
Bibliographic record
Abstract
Learning activities should be shifted from offline to online mode as a consequence of the COVID-19 pandemic. Several previous studies have stated that this situation affects the psychology of students. This study aimed to examine mathematics anxiety and learning motivation of junior high school students based on the availability of online tutoring support during the COVID-19 pandemic. We employed a survey method by distributing a Google Form link containing a questionnaire to a public junior high school in East Jakarta, Indonesia. The questionnaire consisted of 22 items of mathematics anxiety and 23 items of learning motivation with a 5-point Likert scale. A total of 365 eighth-grade students were involved in this study. Data analysis used descriptive analysis, Mann-Whitney test, and correlation analysis. Our study revealed a significant difference between students who took and did not take online tutoring. Furthermore, the study also found a negative relationship between mathematics anxiety and learning motivation. The students who took online tutoring had a low level of mathematics anxiety and a very high level of learning motivation.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".