How to Prepare Learners for a Knowledge Society——Interview with Dr.Marlene Scardamalia
Bibliographic record
Abstract
Dr.Marlene Scardamalia is the Presidents' Chair in Education and Knowledge Technologies at OISE/U of T in Canada.As co-founder and Director of the Institute for Knowledge Innovation and Technology(IKIT)her research helps to provide intellectual leadership for an international network of educational innovators.Her areas of research include education for knowledge creation,cognitive development,psychology of writing,intentional learning,the nature of expertise,educational use of computers,and research-based innovation in learning and knowledge work.In 2005,along with Carl Bereiter,she was honoured with the first-ever CSCL(Computer Supported Collaborative Learning)Career Achievement Award for foundational work in knowledge building environments and the Knowledge Society Network.In 2006 Marlene received the World Award of Education from the World Cultural Council,for a worldwide effort to advance educational opportunities.In 2007,the Institute for Knowledge Innovation and Technology received the ORION(Ontario Research and Innovation Optical Network)Learning Award for development of the world's first collaborative learning environment and for leadership in research-based innovations in theory,pedagogy,and technology,all aimed at making citizens part of a 21st-century knowledge-creating culture.Her publications include The Psychology of Written Composition and Surpassing Ourselves:An Inquiry into the Nature and Implications of Expertise(both with Carl Bereiter)and Writing for Results(with Bereiter and Bryant Fillion).In recent years,Dr.Marlene Scardamalia is using the SSHRC(the Social Sciences and Humanities Research Council of Canada)grant to work on a study called Beyond Best Practice:Research Based Innovation Learning and Knowledge Work.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".