Scholarship in Teaching and Learning: An Interview with John Mitterer
Bibliographic record
Abstract
John Mitterer earned his PhD in cognitive psychology from McMaster University. Like many teaching professors, his career took an unexpected turn early on when he was hired to teach introductory psychology at Brock University, near Niagara Falls, in Ontario, Canada. It was love at first lecture. He never left the course and now, as a full professor some 25 years later, has taught well over 20,000 students. Along the way he has participated in the development of a wide variety of learning materials, including textbooks, videodiscs of support materials, student-learning CD-ROMs, online learning objects, Web sites, test banks, PowerPoint slides, study guides, and instructor's manuals. Mitterer now thinks of himself as a scholar of teaching and learning with a special interest in the educational potential of digital technology. He is a frequent presenter at conferences devoted to teaching and learning and currently holds a Brock University Chancellor's Chair for Teaching Excellence. He is the recipient of the 2003 Brock University Distinguished Teaching Award, a 2003 Ontario Confederation of University Faculty Associations (OCUFA) Teaching Award, a 2004 3M Teaching Fellowship, and the 2005 Canadian Psychological Association Award for Distinguished Contributions to Education and Training in Psychology. David B. Daniel is involved with forging reciprocal links between cognitive-developmental psychology and teaching practices and pedagogy. He is the coordinator of the Society for Research in Child Development's (SRCD) Teaching of Developmental Science Institute as well as chair of the SRCD Teaching Committee, and the managing editor of Mind, Brain, and Education. He chaired the Society for the Teaching of Psychology's task force on pedagogical innovations. His interest in the development of effective teaching has informed his current efforts to develop effective pedagogical techniques that positively impact both student learning and teacher performance.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.034 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".