Impact of Educational Neuroscience Teacher Professional Development: Perceptions of School Personnel
Bibliographic record
Abstract
There has been an increased focus on the importance of educational cognitive neuroscience for teachers, yet the research on the outcomes of teacher training in this area are minimal. We created and implemented an Educational Neuroscience professional development (PD) delivered throughout the 2020–2021 school year. This study was co-designed between researchers and school district partners. Participants were school personnel from a high school in Western Canada consisting of approximately 1,400 students and 75 teachers. All participants in the PD, including teachers and school staff, were invited to participate in interviews about their experiences during the PD. Seven in-depth structured interviews were performed to understand participants’ experiences, their perceptions of the value of educational neuroscience, and how the PD impacted their teaching practice. Through inductive coding and thematic analysis, we found that the PD had a positive impact on participants and their students. The sessions primarily increased participants’ knowledge of neuroscience concepts and provided them with practical and useful applications that they were able to employ in their classrooms in areas related to lesson planning, assessment, and student engagement. Participants described the remarkable impact that increased neuroscience knowledge had on their relationships with students and on students’ own understandings of neuroscience concepts. Overall, these findings provide further evidence on the significance of infusing educational neuroscience in teacher PD and highlight the importance of collaborative programs between researchers and educators to bridge the research to practice gap.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".