Student-Directed Assessment of Knowledge Building Using Electronic Portfolios
Bibliographic record
Abstract
Despite emphasis and progress in developing collaborative inquiry in computer-supported collaborative learning research, little attention has been given to examining how collective learning can be assessed in computer-supported collaborative learning classrooms, and how students can have agency in assessing their own collaborative process. We propose that assessments should capture both individual and collective aspects of learning and be designed in ways that foster collaboration. We describe the design of student-directed electronic portfolio assessments to characterize and “scaffold” collaborative inquiry using Knowledge Forum™. Our design involved asking students to identify exemplary notes in the computer discourse depicting knowledge building episodes using four knowledge building principles as criteria. We report three studies that examined the designs and roles of knowledge building portfolios with graduate and Grade 12 students in Hong Kong and Canada. The findings suggest that knowledge building portfolios help to characterize collective knowledge advances and foster domain understanding. We discuss lessons learned regarding how knowledge building may be fostered and provide principles for designing assessments that can be used to evaluate and foster deep inquiry in asynchronous online discussion environments.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".