Students’ Self-Efficacy and Confidence in Technological Abilities Resulting from Participation in “The Curriculum and Community Environmental Restoration Science (STEM + Computer Science)”
Bibliographic record
Abstract
The rationale for this research is the ever-increasing reliance on technology in all aspects of life, but especially in the realm of education. Technology tools, use, and approaches that support inclusive student learning are supported by the empirical evidence found in this report. The research emphasized self-efficacy levels achieved in the student learning of technology-supported integrated science, technology, engineering, and mathematics (STEM). The Curriculum and Community Environmental Restoration Science STEM + Computer Science (CCERS) makes use of web-based authentic STEM content, providing interactive technology on a dynamic environmental science platform and providing real-world environmental conundrums. Results of this study indicate that CCERS respondents have higher confidence in their technological abilities than those of the non-CCERS respondents. In addition, under-represented groups (URG) CCERS respondents, on average, have higher confidence in their technological abilities than URG non-CCERS respondents. This suggests that CCERS has a positive impact on participants' confidence in their technological abilities, a key indicator in pursuing STEM careers. This study provides practical implications for current and future research in technology-supported learning in integrated STEM learning environments and student outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".