EARLY CHILDHOOD EDUCATION STUDENT-TEACHERS EXPERIENCING VIRTUAL MATH MAKERSPACES: ORGANIZING PLAYFUL LEARNING ENVIRONMENTS
Bibliographic record
Abstract
This study investigates the integration of makerspaces in early childhood education. It emphasizes the impact of play-based learning on children’s cognitive development and attitudes towards learning. Quality early childhood learning encourages positive attitudes toward learning and school, fostering strength, confidence, and resilience. The study focuses on implementing makerspaces, defined as environments for creative exploration and cognitive engagement through play. Participants in a Bachelor of Early Childhood Education (BECE) program engaged in a virtual, mathematics-focused makerspace. This study aimed to understand participants’ perceptions of makerspaces as effective learning environments for early learners. Participants were tasked with taking part in making activities like coding, beading, origami, and 3D construction, which connected mathematical concepts to real-world applications. Results indicated that makerspaces support the development of technological, pedagogical, and content knowledge (TPACK). Participants also found these environments promoted digital literacy, confidence, creativity, critical thinking, and fine motor skill development. The study further highlighted the role of makerspaces in building learning communities involving children, parents, and teachers, and the significance of incorporating technology in early childhood education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".