Exploring influential factors in peer upvoting within social annotation
Bibliographic record
Abstract
Abstract Upvotes serve important purposes in online social annotation environments. However, limited studies have explored the influential factors affecting peer upvoting in online collaborative learning. In this study, we analysed the factors influencing students' upvotes received from their peers as 91 participants utilized Perusall, an online social annotation system, for collaborative reading. The participants were asked to collaboratively annotate 29 reading materials in a semester. We collected student reading behaviours and analysed their annotations with a text‐mining tool of Linguistic Inquiry and Word Count (LIWC). Moreover, conditional inference tree was used to determine the relative importance of explanatory factors to the upvotes students received. The results showed that the high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations than those who did not receive upvotes. The two groups of students had no significant differences in the upvotes given to others, as well as cognitive activities and positive emotions involved in annotations. Moreover, the number of annotations was the determining factor in predicting the upvotes that one could receive in social annotation activities. This study has significant practical implications regarding providing interventions in social annotation‐based collaborative reading. Practitioner notes What is already known about this topic Social annotations enhance students' reading experience, facilitate knowledge sharing and collaboration, promote high‐quality learning interactions and ultimately lead to improved performance. In social annotation environments, receiving upvotes from peers is not only a type of feedback but also a form of motivation, social interaction and social validation. No study has explored the influential factors in peer upvoting within social annotation‐based learning. What this paper adds This study was the first to examine social annotations through the lens of the community of inquiry framework. We investigated the relationships between students' cognitive and social presence in their annotations and the upvotes they received in an online social annotation environment. Our study revealed the strategies for obtaining upvotes from peers in social annotation‐based learning environments. Implications for practice and/or policy The high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations compared to the low‐upvote group. The two groups of students did not show significant differences in the upvotes they gave to others or in the cognitive activities and positive emotions involved in annotations. The number of annotations was the primary factor predicting the number of upvotes received in the collaborative reading. This study could inform the design of future online social annotation systems to better support collaborative learning and peer interaction.
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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 | 0.009 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".