Notice bibliographique
Résumé
In the 2010s, growth of information and communication technologies and the emergence of big data led to the possibility of meaningful analysis of data at scale. University student interactions were channeled through learning management systems (LMS) or virtual learning environments (VLEs), so researchers were able to collect clickstream data and observe patterns of use which were previously invisible or non-existent. Leading thinkers saw the potential for learning analytics and led the development of this distinct field, separating away from other data-informed approaches, such as electronic data mining and academic analytics. The field was strengthened through the development of the Society of Learning Analytics Research (SoLAR). SoLAR publish the Journal of Learning Analytics and established leading conferences to build a worldwide network of disciplinary expertise. Thought leaders from Australia, Canada, Europe, the United Kingdom, and the United States led the global movement to implement learning analytics. Initially, learning analytics focused on the potential for using data-driven decision-making to inform actionable insights or interventions, which could improve student learning outcomes. As data reveals information not previously accessible, ensuring ethical approaches and respecting student privacy have been consistent themes. Data collection has grown to be multimodal in nature and analytic approaches have continued to develop, expanding to include social and networked analyses, cluster analyses, and others. Attention was directed to the way students and instructors visualize and communicate findings from the wealth of data. There is a continued focus on sense-making via visual displays to ensure information is effectively interpreted and understood. Tools and applications for implementing interventions were often initially tested in siloed or individual courses. The field is now expanding to bring insights and positive findings from initial learner support to inform a broader understanding of how and in what way these tools specifically support learners through this complex, situational, and social process across institutions worldwide. Researchers argue for greater pedagogical and theoretical links to ensure scalability and support for learners and educators alike. The most effective use of these technologies combines established learning theories and learning design with analytics to generate useful and actionable insights. The ideal is to support student success through personalized learning. However, the significant potential for improving student learning outcomes can only be achieved through broad stakeholder engagement. To support widespread adoption by educators and implementation at an institutional-level, policy frameworks such as the SHEILA framework and DELICATE checklist have been developed.
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 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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 ».