Empowering learning through integration: Enhancing understanding of variables and functions in the context of STEM education
Bibliographic record
Abstract
This paper explores the integration of STEM activities in teaching and learning, emphasizing the importance of innovative pedagogical approaches in effectively introducing theoretical concepts, such as variables and functions, and merging them with practical applications. Drawing on existing literature, this study investigates the integration of STEM activities with real-world applications to enhance mathematics learning, highlighting intrinsic motivation, self-efficacy beliefs, and goal orientation as key factors in fostering student engagement. This case study explores the integration of a STEM activity to introduce students to variables and functions through a pendulum experiment. The aim is to demonstrate the impact of this approach on students' understanding of abstract mathematical concepts, as well as their problem-solving skills. By combining cognitive and social constructivism with technological modes (virtual labs), the study showcases the transformative potential of innovative techniques in STEM education. The outcomes of the study highlight, to some extent, the positive effects of STEM activities on students' engagement, motivation, understanding of theoretical concepts, and problem-solving skills. The focus on hands-on activities supports practical learning experiences and fosters critical thinking. Additionally, virtual labs enrich students' exploration of complex mathematical phenomena, enhancing their ability to apply prior knowledge to new contexts and transcend the boundaries of traditional lab settings. Overall, the findings underscore the transformative potential of innovative pedagogical approaches and technological modes in creating engaging learning environments within STEM disciplines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".