Pacing during a cross-country mountain bike mass-participation event according to race performance,experience, age and sex

This study describes pacing strategies adopted in an 86-km mass-participation cross-country marathon mountain bike race (the ‘Birkebeinerrittet’). Absolute (km·h^?1) and relative speed (% average race speed) and speed coefficient of variation (%CV) in five race sections (15.1, 31.4, 52.3, 74.4 and 100% of total distance) were calculated for 8182 participants. Data were grouped and analysed according to race performance, age, sex and race experience. The highest average speed was observed in males (21.8?±?3.7?km/h), 16–24?yr olds (23.0?±?4.8?km/h) and those that had previously completed >4 Birkebeinerrittet races (22.5?±?3.4?km/h). Independent of these factors, the fastest performers exhibited faster speeds across all race sections, whilst their relative speed was higher in early and late climbing sections (Cohen's d?=?0.45–1.15) and slower in the final descending race section (d?=?0.64–0.98). Similar trends were observed in the quicker age, sex and race experience groups, who tended to have a higher average speed in earlier race sections and a lower average speed during the final race section compared to slower groups. In all comparisons, faster groups also had a lower %CV for speed than slower groups (fastest %CV?=?24.02%, slowest %CV?=?32.03%), indicating a lower variation in speed across the race. Pacing in a cross-country mountain bike marathon is related to performance, age, sex and race experience. Better performance appears to be associated with higher relative speed during climbing sections, resulting in a more consistent overall race speed.     

Perspectives of applied collaborative sport science research within professional team sports

The purpose of the study was to examine the perspectives of both academics and practitioners in relation to forming applied collaborative sport science research within team sports. Ninety-three participants who had previously engaged in collaborative research partnerships within team sports completed an online survey which focused on motivations and barriers for forming collaborations using blinded sliding scale (0–100) and rank order list. Research collaborations were mainly formed to improve the team performance (Academic: 73.6?±?23.3; Practitioner: 84.3?±?16.0; effect size (ES?=?0.54), small). Academics ranked journal articles’ importance significantly higher than practitioners did (Academic: M_rank?=?53.9; Practitioner: 36.0; z?=??3.18, p?=?.001, p?q). However, practitioners rated one-to-one communication as more preferential (Academic: M_rank?=?41.3; Practitioner 56.1; z?=??2.62, p?=?.009, p?q). Some potential barriers were found in terms of staff buy in (Academic: 70.0?±?25.5; Practitioner: 56.8?±?27.3; ES?=?0.50, small) and funding (Academic: 68.0?±?24.9; Practitioner: 67.5?±?28.0; ES?=?0.02, trivial). Both groups revealed low motivation for invasive mechanistic research (Academic: 36.3?±?24.2; Practitioner: 36.4?±?27.5; ES?=?0.01, trivial), with practitioners have a preference towards ‘fast’ type research. There was a general agreement between academics and practitioners for forming research collaborations. Some potential barriers still exist (e.g. staff buy in and funding), with practitioners preferring ‘fast’ informal research dissemination compared to the ‘slow’ quality control approach of academics.     

南戏体制变化二例

南戏体制从宋元到明中叶,逐渐发生变化并趋于整饬。今人依据常见的元明南戏（传奇）剧本,以为从元代开始,南戏开场形式已经十分简练扼要,一概由末念诵一至二首词,这是一个误解。只是到了万历时期,文人创作讲求文采格律,才固定为简要的一词或二词形式。从南戏到传奇剧本,逐步走向分"出",开始时"折""出""节""套"皆用,宣德年间正式分"出",嘉靖年间开始列"出""目",万历以后趋于规整。     

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.