Audrey Watters, Teaching Machines: The History of Personalized Learning
Bibliographic record
Abstract
Audrey Watters' Teaching Machines is an account of the birth, rebirth and re-rebirth of an idea: personalized learning (read: self-paced, not self-directed) for school-aged children, organized via machines designed based on psychological research.Her book demonstrates how teaching by machine is repeatedly presented as new, when it in fact dates back at least to the 1920s and the work of Dr. Sidney L. Pressey of Ohio State University.Teaching machines promised three advantages to the relatively new, complex, and expensive American public education system: cost savings; freedom for students to self-pace; and the liberation of teachers from grading.Pressey's efforts notwithstanding, machines to automate teaching are more famously associated with the later work of Dr. B. F. Skinner at Harvard University, whose dogged determination, motivations, and personality assume centre stage in Watters' story.Through his archived correspondence with his colleagues, business partners, and even his attorney, she lays bare Skinner's hunger to have teaching machines find dominance in American education -and to take primary credit for this change.Of course, teaching machines have yet to dominate the education of school-aged children.Watters posits several explanations, including teacher resistance (which she claims has not been decisive); inadequate evidence of the machines' practicality and benefits in the existing school system; students' lack of enthusiasm for machine-mediated lessons; and lack of commitment on the part of commercial partners.This reviewer particularly appreciated Watters' exploration of Skinner's relationship with the Rheem Corporation and the company's continued re-organization, dithering, and doubt about the teaching machines agenda.Indeed, Watters demonstrates through multiple cases, spread across decades, the general reticence that capital has had to invest in the education market and in learning scientists' ideas.However, I do not believe this book was written as a cautionary tale for wouldbe education entrepreneurs.If not, for whom was this history of teaching machines from the 1920s through the 1970s written?Apparently, for all of us.In the closing chapters Watters displays concern with looming threats to personal freedom in the present century: especially surveillance capitalism 6 (and its cousin, learning analytics) meant to predict and control human behavior, and driven by ubiquitous online tracking.These threats are indeed terrifying; and Watters' text attempts to offer comfort by highlighting how developers and promoters of teaching machines have repeatedly botched the job in some way.Should this give us comfort?In her first chapter, Watters asserts that "To understand 6 Shoshana Zuboff provides a detailed account of surveillance capitalism and its dangers.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| 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".