Performance criteria and ratings in rubrics for evaluating learning in online asynchronous discussions
Bibliographic record
Abstract
The purpose of this study was to identify performance criteria and ratings in rubrics designed for the evaluation of learning in online asynchronous discussions (OADs) in post-secondary contexts and compare them to behaviors that researchers have focused on in context of transcript analyses of OADs. We analyzed rubrics collected from internet sources. Using purposive sampling, we reached saturation by the selection of 50 rubrics. Using keyword analysis and subsequent grouping of keywords into categories, we identified categories of performance criteria and ratings and compared these with the behaviors highlighted in the literature on transcript analysis of OADs. -- The analysis led to the identification of 153 performance criteria in 19 categories and 831 ratings in 40 categories. We subsequently identified four core categories: (i) Cognitive (44.0% of total performance criteria and ratings); (ii) Mechanical (19.0%); (iii) procedural/Managerial (18.29%); and (iv) Interactive (17.17%). Criteria and ratings assess: (a) thinking skills (cognitive core category); (b) learners' participation in the forum (procedural/managerial core category); (c) learners' interactions with others (Interactive core category); and (d) mechanical aspects of writing (mechanical core category). We found congruence between the literature and the rubrics' emphasis on thinking skills and no congruence with the rubrics' emphasis on mechanics. We found little evidence that the rubrics assess social presence.
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 | 0.038 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".