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Methods: Twelve competitive male cyclists completed an incremental test to exhaustion, a FTP test and 4–5 self-paced TT bouts on a stationary bike within the lab, and a 16.1 km road TT, using mobile power meters.
Results: CP and W? derived from the power-duration relationship closely predicted TT performance. The 16.1-km road TT completion time (26.7 ± 2.2 min) was not significantly different from and was significantly correlated with the predicted time-to-completion (27.5 ± 3.3 min, r = 0.89, P < 0.01). CP and FTP were not significantly different (275 ± 40 W vs. 278 ± 42 W, P > 0.05); however, the limits of agreement between CP and FTP were 30 to -36 W.
Discussion: The findings of this study indicate that CP and W? determined using mobile power meters during maximal, self-paced TT prediction trials can be used to accurately predict 16.1-km cycling performance, supporting the application of the CP and W? for performance prediction. However, the limits of agreement were too large to consider FTP and CP interchangeable. 相似文献
Practitioner notes
What is already known about this topic- Educational game play has been demonstrated to positively affect learning performance and learning persistence.
- Trace-based assessment from digital learning environments can focus on learning outcomes and processes drawn from user behaviour and contextual data.
- Existing approaches used in learning analytics do not (fully) meet criteria commonly used in psychometrics or for different forms of validity in assessment, even though some consider learning analytics a form of assessment in the broadest sense.
- Frameworks of knowledge representation in trace-based research often include concepts from cognitive psychology, education and cognitive science.
- To assess skills-in-action, stronger connections of learning analytics with educational measurement can include parametric and nonparametric statistics integrated with theory-driven modelling and semantic network analysis approaches widening the basis for inferences, validity, meaning and understanding from digital traces.
- An expanded methodological foundation is offered for analysis in which nonparametric multidimensional scaling, multimodal analysis, epistemic network analysis and evidence-centred design are combined.
- The new foundations are suggested as a principled, theory-driven, embedded data collection and analysis framework that provides structure for reverse engineering of semantics as well as pre-planning frameworks that support creative freedom in the processes of creation of digital learning environments.