Knowledge Building in Online Mode: Insights and Reflections
Bibliographic record
Abstract
It seems certain that blended learning will be on the rise in higher education, with in-person meetings increasingly precious time, and online synchronous and asynchronous sessions used to complement them. This paper examines Knowledge Building in two graduate courses conducted during the COVID-19 pandemic. There were no in-person sessions; rather, synchronous Zoom sessions were combined with asynchronous work in a knowledge building environment–Knowledge Forum. Knowledge Forum is designed to make transparent and accessible means by which deep understanding and sustained creative work proceed. Accordingly, for example, rise-above notes and view rearrangement support synthesis and explanatory coherence, epistemic markers support knowledge-advancing discourse, and analytics support self-and group-monitoring of progress as work proceeds. In this report, we focus on these aspects of Knowledge Building, using a subset of analytics to enhance understanding of key concepts and design of principles-based practices to advance education for knowledge creation. Overall, we aimed to have students take collective responsibility for advancing community knowledge, rather than focus exclusively on individual achievement. As we reflect on our experiences and challenges, we attempt to answer the following questions: Do courses that introduce Knowledge Building in higher education need an in-person or synchronous component? In what ways can we leverage in-class time and Knowledge Forum work to engage students in more advanced knowledge creation? We conclude that synchronous and asynchronous Knowledge Building can be combined in powerful new ways to provide students with more design time and deeper engagement with content and peers.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".