Taking risks, getting messy, and having fun with professional learning: Makerspaces as professional development for 21st century second language teachers
Bibliographic record
Abstract
The use of makerspaces in education has exploded around the world over the past decade (Halverson & Sheridan, 2014); however, their employment in professional development for teachers has only recently emerged within the literature. Previous studies have found that makerspaces have the potential to radically transform how professional development is delivered to teachers by fostering nurturing opportunities to collaboratively engage in professional learning (see Girvan et al., 2016; Kjällander et al., 2017; Panganelli et al., 2017). Despite its emergence in the literature, the study of makerspaces in teacher professional development is limited to those studies inspired by STEAM education (science, technology, engineering, arts, and math). Consequently, little knowledge exists about their use in professional development for second language teachers. While presenting data gathered from reflective feedback questionnaires of teacher participants taking part in makerspace workshops, this paper contributes to the conversation in the literature by exploring the utility and application of makerspaces as professional development for second language teaching. The goal of the study was to explore in what ways this type of experiential professional development might enhance professional learning and reflective practice and contribute to professional growth and development among early career second language teachers. Findings strongly indicate that makerspace professional development sessions offer second language teachers a positive and supportive space in which to reflect and expand on their professional knowledge of best practices in second language teaching by directly engaging with learning activities meant to support students in their acquisition of the target language.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".