Book Review: How Education Works: Teaching, Technology, and Technique
Bibliographic record
Abstract
Teaching, Technology, and Technique reveals the essence of how technologies-broadly defined as the orchestration of anything(s) to do anything a purpose-encourage, assist, and shape good teaching and learning as well as core competences for the mastery of learning, such as innovation, imagination, motivation, and dedication.This book targets a broad audience that includes (but is not limited to) teachers, curriculum developers, software developers, education researchers, and parents who support and educate their children's learning.Dron uses a preamble-Elephants in the Classroom-to demonstrate typical predicaments in education: misalignments between teaching methods and learning results, the imbalance of teaching styles and learning outcomes, personal tutoring outperforming traditional, long-established methods of education, and difficulties in the replication of successful interventions in education.The body of the book is divided into three sections in which Dron 1) adapts the definition of technology from Brian Arthur (2009) and, exploring other scholars' definitions, explains how people participate in technologies and illustrates how the way people enact technologies, whether hard or soft, allows technologies to be orchestrated for learning; 2) introduces the co-participation model and argues
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".