Bibliographic record
Abstract
In this article I will share a qualitative self-study about a 15-week blended 100% online graduate level course facilitated through synchronous meetings on Blackboard Collaborate and asynchronous discussions on Blackboard. I taught the course at the University of Tennessee (UT) during the spring 2012 semester and the course topic was online learning environments. The primary research question of this study was: How can the designer/instructor optimize learning experiences for students who are studying about online learning environments in a blended online course relying on both synchronous and asynchronous technologies? I relied on student reflections of course activities during the beginning, middle, and the end of the semester as the primary data source to obtain their insights regarding course experiences. Through the experiences involved in designing and teaching the course and engaging in this study I found that there is room in the instructional technology research community to address strategies for facilitating online synchronous learning that complement asynchronous learning. Synchronous online whole class meetings and well-structured small group meetings can help students feel a stronger sense of connection to their peers and instructor and stay engaged with course activities. In order to provide meaningful learning spaces in synchronous learning environments, the instructor/designer needs to balance the tension between embracing the flexibility that the online space affords to users and designing deliberate structures that will help them take advantage of the flexible space.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".