Voice,post-structural representation and the subjectivity of ‘included’ students

Aligned with the broader movement from structuralism to the post-structuralisms [Lather, P. 2013. “Methodology-21: What Do We Do in the Afterward?” International Journal of Qualitative Studies in Education 26 (6): 634–645; St. Pierre, E. A. 2009. “Afterword: Decentering Voice in Qualitative Inquiry.” In Voice in Qualitative Inquiry: Challenging Conventional, Interpretive, and Critical Conceptions in Qualitative Research, edited by A. Y. Jackson and L. A. Mazzei, 221–236. London: Routledge; St. Pierre, E. A. 2013. “The Posts Continue: Becoming.” International Journal of Qualitative Studies in Education 26 (6): 646–657], research in disability studies for the past two decades has found ‘the potholes’ [Miller, L., J. B. Whalley, and I. Stronach. 2012. “From Structuralism to Poststructuralism.” In Research Methods in the Social Sciences, edited by B. Somekh and C. Lewin. London: SAGE] of disability rights scholarship. In this paper, I offer a critical research framework in the field of disability studies in education that is theoretical, political and personal. Concentrating on the positioning of disability, I draw on the methodological tools of post-structural representation, subjectivity and constructivist grounded theory to study how discursive practices within (and around) secondary schools shape ‘included’ disabled subjects. In the paper I develop this framework and then demonstrate its application in ongoing research that critically counters the conventions that marginalize particular students in schools.     

Mechanical determinants of acceleration and maximal sprinting speed in highly trained young soccer players

Abstract

The aim of the present study was to examine, in highly trained young soccer players, the mechanical horizontal determinants of acceleration (Acc) and maximal sprinting speed (MSS). Eighty-six players (14.1 ± 2.4 year) performed a 40-m sprint to assess Acc and MSS. Speed was measured with a 100-Hz radar, and theoretical maximal velocity (V₀), horizontal force (F₀) and horizontal power (P_max) were calculated. Within each age group, players were classified as high Acc/fast MSS (>2% faster than group mean), medium (between ?2% and +2%), and low/slow (>2% slower). Acc and MSS were very largely correlated (?0.79; 90% confidence limit [?0.85; ?0.71]). The determinants (multiple regression r² = 0.84 [0.78; 0.89]) of Acc were V₀ (partial r: 0.80 [0.72; 0.86]) and F₀ (0.57 [0.44; 0.68]); those of MSS (r² = 0.96 [0.94; 0.97]) were V₀ (0.96 [0.94; 0.97]) and P_max (0.73 [0.63; ?0.80]). High/Med have likely greater F₀ (Cohen’s d: +0.8 [0.0; 1.5]), V₀ (+0.6 [?0.1; 1.3]) and P_max (+0.9 [0.2; 1.7]) than Low/Med. High/Fast have an almost certainly faster V₀ (+2.1 [1.5; 2.7]) and a likely greater P_max (+0.6 [?0.1; 1.3]) than High/Med, with no clear differences in F₀ (?0.0 [?0.7; 0.6]). Speed may be a generic quality, but the mechanical horizontal determinants of Acc and MSS differ. While maximal speed training may improve both Acc and MSS, improving horizontal force production capability may be efficient to enhance sprinting performance over short distances.     

澳大利亚游泳协会兴奋剂危机事件管理经验与启示

目的以澳大利亚游泳协会面临的兴奋剂事件为例,概述其危机公关应对现状,以利益相关者为研究视角,深入分析澳大利亚游泳协会在处理兴奋剂事件时面临的挑战和存在的问题。方法运用文献资料法和逻辑分析法。结果与结论深入分析澳大利亚泳协应对危机案例的成功经验,从危机公关的重要性及具体应对措施两部分进行阐述。对于澳大利亚来说,国家和泳协对反兴奋剂的支持力度是非常大的。在面对兴奋剂事件时,澳大利亚泳协迅速应对危机、及时建立危机公关渠道并迅速掌握舆论主导权,公关信息具有透明度且公关周期计划完整,这对中国体育非营利组织来说具有一定的借鉴意义。     

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

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.

     

Implementation Factors and Their Influence on Student Mathematics Outcomes

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.