Bibliographic record
Abstract
Mathematics is a key to all scientific subjects. Learning mathematics requires cognitive and meta-cognitive effort.Many students suffer from mathematics anxiety that very often leads to physiological symptoms. Self-efficacy isdefined as people's beliefs about their capabilities to produce designated levels of performance that affect their lives.Regardless of previous ability achievement, high-efficacious students work harder, persist longer, persevere in theface of adversity, have lower anxiety, and achieve more than low-efficacious students. Therefore, very important inmathematical education is the nurturing of young people who will exercise control over learning mathematics. In thisqualitative action research, we diagnosed and attempted to enhance eight 6th graders' efficacy beliefs to learnmathematics. Research tools were 22 interviews, six observations, and 10 field notes. We asked what constitutedstudents' self-efficacy profile to learn mathematics. The constant comparative and grounded theory techniques wereused for data analysis. Intervention included goal-setting, skill and strategy acquisition, and reflection. Thetheoretical contribution of this study is a very detailed diagnosis of self-efficacy resulting in a profile rather than in asingle score. Practically, this profile enabled efficient intervention that resulted in students' high self-efficacy andachievement and improvement in teacher instruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".