Knowledge Building in Online Mode: Insights and Reflections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it