Knowledge Building in an Online Environment: A Design-Based Research Study
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
This article explores knowledge-building in an online distance-learning environment. The research examines how knowledge-building principles can be translated into online classroom practice for graduate students. Specifically, how do the course components and the online learning environments created in two online graduate courses contribute to student knowledge-building as evaluated by the 12 determinants proposed by Scardamalia (2003)? The results of the study indicated that the emphasis on social interaction and collaboration has enhanced student learning and fostered the socio-cognitive developments for knowledge-building. The course components and the learning environment created in the courses have encouraged knowledge-generation, representation, and linked annotations, which helped learners to organize their ideas from multiple perspectives and “integrate them with personal knowledge” (Hannafin, Land, & Oliver, 1999). Several significant findings are discussed including the students' strong feelings about community, and new ways of working and interacting in online settings. The students' learning process and products presented in this article indicate a rich knowledge-building experience.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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