“When I saw my peers annotating”
Bibliographic record
Abstract
Purpose Social annotation (SA) is a genre of learning technology that enables the annotation of digital resources for information sharing, social interaction and knowledge production. This study aims to examine the perceived value of SA as contributing to learning in multiple undergraduate courses. Design/methodology/approach In total, 59 students in 3 upper-level undergraduate courses at a Canadian university participated in SA-enabled learning activities during the winter 2019 semester. A survey was administered to measure how SA contributed to students’ perceptions of learning and sense of community. Findings A majority of students reported that SA supported their learning despite differences in course subject, how SA was incorporated and encouraged and how widely SA was used during course activities. While findings of the perceived value of SA as contributing to the course community were mixed, students reported that peer annotations aided comprehension of course content, confirmation of ideas and engagement with diverse perspectives. Research limitations/implications Studies about the relationships among SA, learning and student perception should continue to engage learners from multiple courses and from multiple disciplines, with indicators of perception measured using reliable instrumentation. Practical implications Researchers and faculty should carefully consider how the technical, instructional and social aspects of SA may be used to enable course-specific, personal and peer-supported learning. Originality/value This study found a greater variance in how undergraduate students perceived SA as contributing to the course community. Most students also perceived their own and peer annotations as productively contributing to learning. This study offers a more complete view of social factors that affect how SA is perceived by undergraduate students.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".