Developing a knowledge network for applied education research to mobilise evidence in and for educational practice
Notice bibliographique
Résumé
Background: The importance of ‘evidence-informed practice’ has risen dramatically in education and in other public policy areas. This article focuses on the importance of knowledge mobilisation strategies, processes and outputs. It is concerned with how these can support the adaptation and implementation of evidence from research and professional knowledge to inform changes in educational practices. It presents a case study of the Knowledge Network for Applied Education Research (KNAER), a tripartite initiative in Canada involving the Ontario Ministry of Education, University of Toronto and Western University and 44 KNAER-funded projects.\nPurpose: The purpose of the article is to analyse the developing approach towards supporting knowledge mobilisation by the KNAER provincial partners through the governing body of the Planning and Implementation Committee and strategic and operational work of the university teams, and also the knowledge mobilisation strategies, challenges and successes of 44 KNAER projects.\nDesign and methods: We utilised a qualitative case study approach to investigate the Knowledge Network for Applied Education Research’s (KNAER) approaches to developing knowledge mobilisation over four years (2010–2014).To explore the work of the KNAER provincial partners, we analysed 17 meeting notes from the Planning and Implementation Committee and 9 notes from the university KNAER partners’ meetings. To explore the knowledge mobilisation strategies, challenges and successes of KNAER-funded projects, we analysed the 44 knowledge mobilisation plans, 141 interim reports and 43 final reports submitted by projects. To further investigate the experiences of KNAER projects during their implementation, we analysed responses from 21 people from 19 KNAER projects who participated in a facilitated discussion about their experiences.\nResults: The Planning and Implementation Committee’s role involved three core responsibilities: (1) Approving knowledge mobilisation proposals submitted to the KNAER; (2) Ensuring that collaborative partnerships were developed at the local, provincial, national and international levels; and (3) Approving the KNAER operational and strategic plan. The university partners have taken on the roles of operational management, strategic leadership, and research and knowledge mobilisation expertise. KNAER projects varied in their knowledge mobilisation strategies, challenges and successes. ‘Exploiting Research’ projects focused on establishing connections and engaging communities of practice with people relevant to the project’s focus, creating an analysis of needs, designing or producing a relevant knowledge mobilisation product with the purpose of improving practice, monitoring the results or impact of the new product and sharing the dissemination process and results with others. ‘Building or Extending Networks’ projects engaged in creating or extending existing networks, developing a needs-based or gap assessment and producing appropriate products and dissemination processes based on the results gathered. ‘Strengthening Research Brokering’ projects organised steering committees to guide their work and gathered information via a literature review or by collecting information from stakeholders and then served as research brokers by collecting and mobilising relevant knowledge to inform practice. ‘Visiting World Experts’ projects developed knowledge mobilisation plans for host experts’ visits, involving establishing partnerships with networks, including universities and schools, and utilising social media and communication processes for knowledge mobilisation products.\nConclusions: KNAER included aspects of linear, relationships and systems models for connecting evidence and practice. Looking forward, KNAER is seeking to further advance a systemic approach. A systems model is in preference to linear models – which focus on evidence production only without attention to mobilisation or uptake of research, and/or relationships models – which may develop networks, but do not attend to capacity and resource barriers that need to be addressed for systemic and sustainable knowledge mobilisation.
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 | 0,007 | 0,007 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».