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Enregistrement W2548672494

The Effect of School Improvement Planning on Student Achievement

2015· article· en· W2548672494 sur OpenAlexaboutno aff
David Huber, James M. Conway

Notice bibliographique

RevuePlanning and changing · 2015
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueEducational Assessment and Improvement
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAccountabilityAcademic achievementStudent achievementIntervention (counseling)Plan (archaeology)White paperPsychologyEffective schoolsStandardized testProcess (computing)Quality managementQuality (philosophy)Mathematics educationPolitical scienceEngineeringOperations managementComputer scienceGeography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Under the No Child Left Behind (NCLB) Act of 2001, schools identified as not making adequate progress are to submit a school improvement plan (SIP). SIPs were designed to close achievement gaps and raise levels of student achievement (White, 2009). Although not required for all schools, Fernandez (2009) found that by 2000, most schools were writing formal plans for improvement. States have recognized the importance of school accountability and are using student achievement and the process of school improvement planning as a method of distinguishing effective and ineffective schools (Phelps & Addonizio, 2006). Given the use of SIPs for decision making, it is critical to examine whether SIP quality is related to student achievement.A review of the literature on characteristics of effective SIPs indicates the importance of targeted areas for improvement, integration of specific intervention strategies, frequent monitoring of student data, and identification of persons responsible for implementation of each strategy (Fernandez, 2009; Reeves, 2004; White, 2009). Other areas necessary for systemic improvement, yet often missing from SIPs, include leadership strategies, data analysis techniques, decision making practices, and an evaluation of a school's readiness to change along with the process for improvement (Beach & Lindahl, 2004; Hall & Hord, 2011; Reeves, 2004; White, 2009). Without the integration of these steps and a frequent formal evaluation of the improvement process, sustained improvement is unlikely (Webb, 2007; White & Smith, 2010).School improvement efforts have been documented since the 1970s, and it is surprising that a clear agreement on exactly how to carry out the improvement efforts has yet to emerge (White & Smith, 2010). Despite recommendations on the content for SIPs, evidence suggests that plans often fall short. To date, there still is no required format for an SIP. Mclnerney and Leach ( 1992) and Webb (2007) have found, within the process of planning there is the chance that schools will set goals that are inappropriate or fail to meet specific subgroup needs. Additionally, if administrators only create a SIP because it is required (rather than because it is a valued process in a school), they are unlikely to build in effective strategies for achieving goals, or mechanisms for frequent monitoring of goal progress.Evidence of Effectiveness of SIPsGiven that SIPs are required in some cases, and that they have been used in decision making about schools, it is important to ask whether differences in quality correlate with student achievement. Only three of the studies have provided evidence on the effectiveness of school improvement planning. One study that examined the role of SIPs and student achievement was by Curry (2007). The study involved a content analysis of SIPs for 67 middle and high schools. The results showed significant negative correlations for student achievement with the number of math strategies found in plans and the number of writing operational action steps. These findings are consistent with Reeves' (2004) and White's (2009) recommendation to limit the number of goals and strategies.Two additional studies were more closely related to the current study. Reeves' (2011) planning, implementation, and monitoring (PIM) study and Fernandez's (2009) study on effectiveness of school improvement plans provide a framework for the current study. Both studies used similar rubrics to examine specific characteristics of SIPs in an effort to quantify the plans' effectiveness. The PIM study (Reeves, 2011) included 2,000 schools in the United States and Canada using achievement data for more than 1.5 million students. The participants in this study represented a very diverse group including both urban and rural districts spanning levels from elementary to high school. The study included double-blind reviews of SIPs in an attempt to see what components of a plan focusing on leadership practices, could be associated with increases in student achievement. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,055
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,100

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,055
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,001
Communication savante0,0030,002
Science ouverte0,0020,004
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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.

Tête enseignante Opus0,112
Tête enseignante GPT0,439
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations19
Publié2015
Routes d'admission1
Résumé présentoui

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