Reflections on the past two decades of Mind, Brain, and Education
Bibliographic record
Abstract
In the early 2000s, Kurt Fischer and colleagues founded the Mind, Brain, and Education (MBE) field (Blake & Gardner, 2007; Fischer et al., 2007), including a flagship journal, society (International Mind, Brain, Education Society [IMBES]), and a master's degree program at the Harvard Graduate School of Education (Harvard). The MBE program was first-of-its-kind, focused on the intersection of neurobiology, psychology, and educational research and practice (Blake & Gardner, 2007; Fischer, 2009). Between its first cohort in 2004 and its final cohort in 2022, the program graduated 668 students from around the world (see Figure 1). Contemporaneously, scholars developed MBE or related Educational Neuroscience initiatives in several U.S. states, Canada, the UK, Austria, The Netherlands, China, Israel, across Latin America, and other locations around the world.In 2022, the MBE masters program was integrated into a broader human development and education offering at Harvard. Program faculty and alumni gathered in a virtual event to mark the sunsetting of the program and to reflect on advancements in the MBE field. This commentary grew out of that meeting and reflects the perspectives of individuals affiliated with MBE on the evolution and impact of the field, with an eye toward future directions. Specifically, to guide their reflections, all authors addressed the following questions: 1.How has the field of MBE changed, and what developments particularly excite you in your area of the field? 2.What do you regard as the most significant impact of MBE on educational practice?3.As you look ahead, what potential advancements or emerging trends do you envision for MBE research and its practical applications?
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.021 | 0.056 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".