POWER OF PEERS: EXPERIENCES USING AN ONLINE PEER ASSESSMENT TOOL TO GRADE STUDENT WORK
Notice bibliographique
Résumé
Abstract This paper explores the implementation, outcomes, and student perceptions of the use of an online tool for anonymous peer assessment of student work. Peer assessment, where one student assesses the work of another, provides an opportunity for important skill development, as well as a fully-scalable strategy for rich, timely, and frequent feedback. In first and third year engineering courses at the University of British Columbia, we have begun using an online peer assessment tool (peerScholar). The tool divides the peer assessment process into three phases: a creation phase where the work is written or uploaded, an assessment phase where students are randomly assigned to assess the work of a set number of their peers, and a review phase where students review the feedback they received, with options to revise their work or assess the quality of feedback received. We have successfully used this tool in two large (n = 750) classes and one moderate-sized (n = 130) class, with a wide range of different types of student work, including letters, technical memoranda, detailed design reports, and video presentations. Through surveys, student feedback with the tool and the process has been positive. Students at both year levels overwhelmingly recognize the importance of peer assessment—over 90% identified it as an essential skill for an engineer, and over 85% felt opportunities for peer assessment should be embedded in the curriculum. Both groups indicate that they felt the process of reviewing others’ work was beneficial for their own understanding of the material; however, first year students were more likely than third year students to put more effort into their work knowing it would be peer assessed, and that they found the content of the feedback received more helpful to their learning. Student acceptance has been good. In a third year mechanical design course, three different design assignments were independently assessed by students using peerScholar and by teaching assistants. The outcomes across all measures were encouraging: for each assignment, the students and teaching assistants had similar mean, standard deviation, minimum, and maximum values, as well as reasonable correlation (r = 0.5 overall). Overall, we consider the adoption of peerScholar a success. Students have been receptive, challenges have been minor, and feedback is more detailed and frequent.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».