The Curriculum and Community Environmental Restoration Science (STEM + Computer Science) Project – Attaining a STEM Mindset Through Improved Technological Ability
Bibliographic record
Abstract
Increasing students’ confidence in their technological ability has been found to have a broader impact on their content knowledge in several subject areas, but most strikingly, in STEM (science, technology, engineering, and mathematics). A sample of 513 students in grades 6 through 12 in the New York City public school system were questioned on their perceived technological ability after participating in The Curriculum and Community Environmental Restoration Science (STEM + Computer Science) Project, hereafter referred to as the CCERS STEM + C Project. Also explored was the students’ access to technology to determine if this would be a factor in student self-efficacy in technology ability. Analysis revealed that science self-efficacy and technology ability were both strengthened through participation in the project. Additionally, the study found that working alongside STEM professionals and exposure to STEM careers were also contributing factors. The study aims to determine if increased access to technology would, in turn, increase students’ self-efficacy in their technology knowledge and skills and have a positive effect on their self-confidence in STEM content. The results of the study contribute to the body of research that suggests greater access to technology may be an important factor in students’ self-agency and academic achievement.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".