Going Against the Grain in an Urban Arizona High School: Secondary Preservice Teachers Emerging as Culturally Responsive Educators     

Pablo Ramirez  Margarita Jimenez-Silva  April Boozer  Ben Clark 《Multicultural Perspectives》2016,18(1):20-28

This one year study examines the journey of two preservice urban high-school teachers in Arizona as they enact Culturally Responsive Teaching in a year-long student teaching residency. Factors that influenced their Culturally Responsive Teaching practices are discussed along themes that emerged from interviews and classroom observations. Recommendations for ways of integrating Culturally Responsive Teaching in teacher education programs are provided.  相似文献   

Snap-N-Send: A valid and reliable method for assessing the energy intake of elite adolescent athletes     

Nessan Costello  Kevin Deighton  Joshua Dyson  Jim Mckenna  Ben Jones 《European Journal of Sport Science》2017,17(8):1044-1055

To ensure that elite adolescent athletes meet their unique training, growth and maturation demands, it is imperative to have access to valid measures of energy intake. Contemporary methods demand close attention-to-detail, meaning that athletes often do not fully adhere to real-time protocols. This study represents the first investigation of a real-time dietary assessment designed using a comprehensive behaviour change framework (COM-B). In a crossover design, 12 elite adolescent male rugby players recorded their energy intake via an estimated food diary (est-FD) and photography-based mobile assessment (‘Snap-n-Send’), combined with a 24-h dietary recall interview. Two 4-day assessment periods were divided into three separate recording environments: 96?h free-living and researcher-observed; 72?h free-living and 10?h researcher-observed. Assessment periods were one month apart. All foods and beverages were provided and weighed by the research team to quantify actual intakes. ‘Snap-n-Send’ reported a small mean bias for under-reporting across 96?h (?0.75?MJ?day^?1; 95% confidence interval [CI] for bias?=??5.7% to ?2.2%, p?<?.001), 72?h (?0.76?MJ?day^?1; 95% CI for bias?=??5.6% to ?2.1%, p?=?.004) and 10?h (?0.72?MJ?day^?1; 95% CI for bias?=??8.1% to ?0.1%; p?=?.067) environments. The est-FD reported a moderate mean bias for under-reporting across 96?h (?2.89?MJ?day^?1; 95% CI for bias?=??17.9% to ?10.2%; p?<?.001), 72?h (?2.88?MJ?day^?1; 95% CI for bias?=??17.9% to ?10.1%; p?<?.001) and 10?h (?2.52?MJ?day^?1;?26.1% to ?5.3%; p?=?.023) environments. Results evidence the ability of ‘Snap-n-Send’ to accurately assess the diet of elite adolescent athletes, signalling the exciting promise of this comprehensive and theoretical behavioural approach within valid dietary assessment.  相似文献   

Using causal models to bridge the divide between big data and educational theory     

Kirsty Kitto  Ben Hicks  Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

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Implementation Factors and Their Influence on Student Mathematics Outcomes     

Tasia Brafford  Beth Harn  Ben Clarke  Christian T. Doabler  Derek Kosty  Kathleen Scalise 《Learning disabilities research & practice》2023,38(1):5-14

Assessing implementation allows for a better understanding of an intervention's effects and the mechanisms that influence its impact. Two main areas of implementation are (a) the quality with which an intervention is delivered and (b) instructors’ adherence to the programmed intervention. The current study used data from a kindergarten mathematics intervention program to (a) examine if and how treatment adherence was associated with implementation quality and (b) explore implementation measures’ relation to student mathematics outcomes. Results indicated high implementation scores across time for both adherence and quality. Neither treatment adherence nor implementation quality was found to relate to a general outcome measure of student mathematics achievement; however, both were similarly related to the curricular-aligned measure.  相似文献   

电子对抗专业应用型人才实践教学研究 ——以"电子对抗技术与指挥"人才培养方案为例     

任明秋  王冰切  冷毅  商犇 《高等教育研究学报》2022,45(1):116-120

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1982-1983 NSPI awards official call for nominations     

Ben Troughton 《Performance Improvement》1982,21(7):42-46

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