Going Blended with a Triple-Entry Activity: Students’ Online Discussions of Assigned Readings using Marginalia
Bibliographic record
Abstract
In this paper, we describe and investigate small group discussions of assigned readings in an online version of a “triple-entry activity” in a blended course used an annotation tool, Marginalia. We wondered if students would interact in this structured, critical, reflective reading activity as effectively online as they had when the activity was undertaken on paper in face-to-face classes. We investigated what happened, why, and if successful, and how these findings might inform the use of annotated discussions in the future. We found 30% of comments acknowledged the value of ideas expressed in a group member’s response to a reading, 30% extended those ideas, 11% connected the reading to personal experience, 9% were questions, and 6% answers. Approximately 60% of the interactions were between one group member and the author of the response; 40% involved comments that were connected to each other as well as the author’s response to the reading. Students felt using Marginalia to comment on classmates’ responses and having classmates comment on their responses facilitated their learning from assigned readings. The instructor agreed and felt the online discussions also contributed to the development of a community of learners between face-to-face classes. In addition, reading students’ responses and discussions before each class informed the instructor’s preparation for in-class activities.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".