Self-assessment of online participation: Using a reflective approach to enhancing student experience
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
The use of online technology in education continues to grow (Canadian Virtual University, 2012; Kanuka, 2008; White, Warren, Faughnan, & Manton, 2010) and student engagement is critical to their successful achievement of educational goals It is therefore valuable for educators to understand how to support student engagement in online environments As self-assessment has the potential to increase student engagement (Kearney, 2013), a component of the final grade in an online master’s course was determined through the use of a student self-assessment log After the conclusion of the course, students completed a survey in which they reflected on how the log affected their feelings of engagement with peers and with their instructor This presentation will discuss the study findings, including the sense of responsibility to others, social participation, and motivation, and will conclude with recommendations on how to include self-assessment of participation in graduate-level courses.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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