The Materiality of Learning: Technology and Knowledge in Educational Practice. Series: Learning in Doing: Social, Cognitive and Computational Perspectives by Estrid Sørensen
Bibliographic record
Abstract
The modern practice of schooling is and always has been inextricably intertwined with its materials: from Froebel gifts, Montessori object boxes, and Waldorf architecture, to Madeline Hunter's lesson plan format, the Blackboard learning management system, and Smart Technologies' Interactive Whiteboard.The particular technologies teachers use in the classroom appreciably shape and "influence the formation of learning and affect thinking and theorizing about education in general" (Srensen, 2009, p. 7).Yet the formative significance of materiality to the social project of education has received surprisingly little theoretical attention.Waltz (2006) points out that "this is especially curious given the serious work that has gone into the development and use of things as educational tools" (p.52).Even educational technology literature has remained relatively immune to the work of science and technology studies (STS) and Actor-Network Theory (ANT) scholars who, for example, observed early that technologies are often unfaithful to their creators and thus produce unanticipated effects beyond the (educational) aims intended.Estrid Sorensen's (2009) The Materiality of Learning: Technology and Knowledge in Educational Practice offers one corrective, methodological step toward addressing this theoretical deficiency, reframing "learning not as social but socio-material" (p.5).Srensen's book is a reworking of her 2005 dissertation, which examined the contribution of learning materials in constituting school practices in two Scandinavian grade 4 classrooms.Using an ANT-informed ethnographic approach, she observed teacher-student interactions "performed" with, around, and through a variety of established technologies-blackboard, chalk, notebooks, chairs, a bed-loft, and a bell-as well as several new media technologies-a blog, a conferencing system, and an online virtual environment called Femtedit.Srensen's intent is to provide a methodological approach to studying the materiality of learning in order to discover "how digital and traditional learning materials influence educational practice in general, and Catherine Adams is an assistant professor in the Department of Secondary Education.Drawing on phenomenology, philosophy of technology, and critical media scholarship, her research investigates changes in teachers' practices, students' learning approaches, and knowledge representation in the wake of digital technology integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".