A Special Issue of the Canadian Journal of Learning and Technology on Knowledge Building
Bibliographic record
Abstract
In a pervasive media and technology landscape that is increasingly global, participatory and connected, one in which learners and teachers can increasingly become creators of knowledge rather than mere consumers of prepared messages and ideas, it is vital for the field of educational technology to take stock of the latest research on knowledge building. Marlene Scardamalia and Carl Bereiter, innovative pioneers in the area of Knowledge Building in education, define the construct of Knowledge Building as having several characteristics that distinguish it from constructivist learning in general. Two key characteristics of Knowledge Building are intentionality and community knowledge. Intentionality captures that people engaged in knowledge building know they are doing it and that advances in knowledge are purposeful. Community knowledge captures that while learning is a personal matter, knowledge building is done for the benefit of the community. Scardamalia and Bereiter emphasize that in contrast to being spontaneous, a knowledge building culture requires a supportive learning environment and teacher effort and artistry to create and maintain a community devoted to ideas and to idea improvement. Distinct from improving individual students’ ideas and understanding, the collective work of Knowledge Building is explicitly focused on the creation and improvement of knowledge of value to one’s community – advancement of the knowledge itself.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.019 |
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".