<i>Beyond Monet: The Artful Science of Instructional Integration</i> by Barrie Bennett and Carol Rolheiser
Bibliographic record
Abstract
The Artful Science of Instructional Integration, Bennett and Rolheiser have taken a groundbreaking perspective on how "knowledge of instruction" can assist in responding to the never-ending press to create meaningful and powerful learning environments.In other words, they have gone beyond the core politics of education and focused on the importance of deep understanding for instructional organizers.These organizers include multiple intelligences, learning styles, ethnicity, gender, at-risk students, learning disabilities, critical thinking, and brain research.The authors assert that "The meaningless and superficial application of any instructional process does not do justice to that process nor does it value the learner" (p.4) Bennett and Rolheiser discuss myriad creative instruction devices for classroom teaching and learning.They focus on intelligent instruction for facilitating deeper understanding of subject knowledge.At the same time they do not underestimate the importance and appreciation of what might be effective for one teacher and a group of students may be ineffective for another.The book contains a compilation of ideas brought on by current research as to what makes a difference to student learning.In addition, the authors present ideas and views of instructional intelligence based on their own experiences as former schoolteachers and school administrators as well as their current practices as consultants and university professors.Although the book is aimed at effective teaching practices for school teachers, it might be of benefit to all stakeholders in all learning institutions to review these innovative practices and perhaps use them as a consumer guide to adopt and/or replicate some of them.This book delivers teaching practice highlights and some strategies introduced in schools to give educators, evaluators, and researchers comprehensive Anthony (Tony) Normore is an assistant professor of educational administration in the Department of Educational Leadership and Policy Studies in the College of Education.Before this he was in K-12 education for 20 years as a teacher, a school administrator, and a district office implementation specialist.He has presented at various local, state, provincial, national, and international conferences in Canada and the US.He has served as a Canadian representative to Nepal as an education facilitator for teacher and headmaster training sponsored by the Canadian Teachers' Federation.He has conducted inservice training and workshops for teachers and administrators on school improvement, the change process, organizational planning, and effective schools.He is currently writing in the areas of leadership development; leadership succession planning; professional and organizational socialization of school administrators; and recruitment, selection, and accountability of school administrators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".