Exploring influential factors in peer upvoting within social annotation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».