Audrey Watters, Teaching Machines: The History of Personalized Learning
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,018 |
| Communication savante | 0,008 | 0,014 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».