Using Survey Data to Improve Student Learning: A Team Approach.
Notice bibliographique
Résumé
student assessments, and student support data. Two questions helped us to use the data productively: What patterns are showing up in the data? What little things can we do to address the concerns of students? These questions assured staff that our focus was where it needed to be – not on evaluating individual teachers, but on what we might do as a team to set some new conditions for learning. In September 2008, I made adjustments to my own practice and introduced the What did you do in school today? project early to new staff. I listed everything I had done the previous year to develop a team approach to decision-making and then presented data from a teacher’s survey, indicating that teachers clearly did not yet feel that they were part of a team or involved in shared decision making. I then asked the staff to describe what teamwork and shared decision-making meant to them. Their responses provided a focus for our ongoing development as a professional learning community. Figure 1 shows the agenda we followed at one transformational school-based professional development day. For the past six years, the Halifax Regional School Board has been developing a solid framework for improving student achievement. As a school principal, I welcomed the board’s direction because it resonated with my own beliefs about our purpose as educators and our need to use data effectively. The Canadian Education Association’s (CEA) initiative, What did you do in school today? is about reflecting on thoughtful questions and using data to make improvements.1 It has played a significant role in creating positive change in our school over the past two years, but it didn’t happen on its own, and it didn’t happen overnight. Before our school could make those improvements, we had to get comfortable with data. When I started as principal at Sir Robert Borden Junior High School in 2007, I set ambitious goals for the school, inspired by what I believe about evidence-based decision making, professional learning, teamwork, and the importance of measuring everything we do as a staff by its impact on student learning. However, I could see at the end of our first day together that the staff was completely overwhelmed; they weren’t yet comfortable using data and clearly felt it might be used to judge them. Just as we traditionally have blamed students when they struggle to succeed in school, I attributed the fear and resistance to the culture of the school, not realizing that I had forged ahead as the hare, when I should have taken the slow and steady path of the tortoise. I had to pause and try to see my own ideas from the perspective of the staff. What context had I provided? What connections had I made to help us all see the relationships among the many different initiatives that seem to continually come at us at high speed? What coaching had I provided to foster the skills of data analysis? I took a big step back and a few small steps forward. Working as a team, we developed shared answers to these questions, and we started to explore the value of teamwork for school and classroom improvement. We began asking the right questions and effectively using the answers to guide our classroom practice. And so, when our school was given the opportunity to participate in the What did you do in school today? survey, we were ready. Matthew Moriarty, a teacher at SRB, took on the role of survey coordinator.
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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 | 0,002 | 0,002 |
| 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 | 0,001 | 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 ».