Determining friction and effective loading for sled sprinting

Understanding the impact of friction in sled sprinting allows the quantification of kinetic outputs and the effective loading experienced by the athlete. This study assessed changes in the coefficient of friction (µ_k) of a sled sprint-training device with changing mass and speed to provide a means of quantifying effective loading for athletes. A common sled equipped with a load cell was towed across an athletics track using a motorised winch under variable sled mass (33.1–99.6 kg) with constant speeds (0.1 and 0.3 m · s^?1), and with constant sled mass (55.6 kg) and varying speeds (0.1–6.0 m · s^?1). Mean force data were analysed, with five trials performed for each condition to assess the reliability of measures. Variables were determined as reliable (ICC > 0.99, CV < 4.3%), with normal-force/friction-force and speed/coefficient of friction relationships well fitted with linear (R² = 0.994–0.995) and quadratic regressions (R² = 0.999), respectively (P < 0.001). The linearity of composite friction values determined at two speeds, and the range in values from the quadratic fit (µ_k = 0.35–0.47) suggested µ_k and effective loading were dependent on instantaneous speed on athletics track surfaces. This research provides a proof-of-concept for the assessment of friction characteristics during sled towing, with a practical example of its application in determining effective loading and sled-sprinting kinetics. The results clarify effects of friction during sled sprinting and improve the accuracy of loading applications in practice and transparency of reporting in research.     

Evidence‐Based Practices: Applications of Concrete Representational Abstract Framework across Math Concepts for Students with Mathematics Disabilities

Students with mathematics disabilities (MD) experience difficulties with both conceptual and procedural knowledge of different math concepts across grade levels. Research shows that concrete representational abstract framework of instruction helps to bridge this gap for students with MD. In this article, we provide an overview of this strategy embedded within the explicit instruction framework. We highlight effective practices for each component of the framework across different mathematical strands. Implications for practice are also discussed and a detailed case study is provided.     

Running mechanical alterations during repeated treadmill sprints in hot versus hypoxic environments. A pilot study

We determined if performance and mechanical running alterations during repeated treadmill sprinting differ between severely hot and hypoxic environments. Six male recreational sportsmen (team- and racket-sport background) performed five 5-s sprints with 25-s recovery on an instrumented treadmill, allowing the continuous (step-by-step) measurement of running kinetics/kinematics and spring-mass characteristics. These were randomly conducted in control (CON; 25°C/45% RH, inspired fraction of oxygen = 20.9%), hot (HOT; 38°C/21% RH, inspired fraction of oxygen = 20.9%; end-exercise core temperature: ~38.6°C) and normobaric hypoxic (HYP, 25°C/45% RH, inspired fraction of oxygen = 13.3%/simulated altitude of ~3600 m; end-exercise pulse oxygen saturation: ~84%) environments. Running distance was lower (P < 0.05) in HOT compared to CON and HYP for the first sprint but larger (P < 0.05) sprint decrement score occurred in HYP versus HOT and CON. Compared to CON, the cumulated distance covered over the five sprints was lower (P < 0.01) in HYP but not in HOT. Irrespective of the environmental condition, significant changes occurred from the first to the fifth sprint repetitions (all three conditions compounded) in selected running kinetics (mean horizontal forces, P < 0.01) or kinematics (contact and swing times, both P < 0.001; step frequency, P < 0.001) and spring-mass characteristics (vertical stiffness, P < 0.001; leg stiffness, P < 0.01). No significant interaction between sprint number and condition was found for any mechanical data. Preliminary evidence indicates that repeated-sprint ability is more impaired in hypoxia than in a hot environment, when compared to a control condition. However, as sprints are repeated, mechanical alterations appear not to be exacerbated in severe (heat, hypoxia) environmental conditions.     

An explicit multicomponent alphabet writing instruction program in grade 1 to improve writing skills

European Journal of Psychology of Education - Learning to write involves the acquisition of several skills, not the least of which is handwriting. Indeed, studies in cognitive psychology have...     

Children’s epistemological views according to their learning centre preferences

The purpose of the study was to investigate children’s epistemological beliefs in terms of their free play preferences in relation to learning centres. The study was conducted with 115 children coming from families with high socio-economic status (54 girls and 61 boys, 57 five years old and 58 six years old) during the 2015/2016 spring semester. The Epistemological Beliefs Scale for Children was administered to each child individually. In addition, all the children were shown pictures of science, drama, music, literacy, painting and mathematic centres prepared by two researchers and asked to choose the one they preferred for play during free time. Each child was interviewed by one researcher and each interview lasted approximately 15–20 minutes. According to the overall results of the study, children’s epistemological views tended to be dogmatic; however, some children thought more sceptically. Furthermore, children had a sophisticated thinking style in certain dimensions of knowledge while having immature reasoning in others. It was also found that the source of knowledge and hypothesising sub-dimensions of epistemological views indicated dogmatic thoughts. On the other hand, children had sceptical thoughts concerning the sub-dimension of change in knowledge.     

Machine Learning Literacy for Measurement Professionals: A Practical Tutorial

The COVID-19 pandemic has accelerated the digitalization of assessment, creating new challenges for measurement professionals, including big data management, test security, and analyzing new validity evidence. In response to these challenges, Machine Learning (ML) emerges as an increasingly important skill in the toolbox of measurement professionals in this new era. However, most ML tutorials are technical and conceptual-focused. Therefore, this tutorial aims to provide a practical introduction to ML in the context of educational measurement. We also supplement our tutorial with several examples of supervised and unsupervised ML techniques applied to marking a short-answer question. Python codes are available on GitHub. In the end, common misconceptions about ML are discussed.