Learning to solve ill-defined statistics problems: does self-explanation quality mediate the worked example effect?     

Bichler  Sarah  Stadler  Matthias  Bühner  Markus  Greiff  Samuel  Fischer  Frank 《Instructional Science》2022,50(3):335-359

Instructional Science - Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for...  相似文献   

Fatigue detection in strength training using three-dimensional accelerometry and principal component analysis     

Niklas Brown  Sebastian Bichler  Meike Fiedler  Wilfried Alt 《Sports biomechanics / International Society of Biomechanics in Sports》2016,15(2):139-150

Detection of neuro-muscular fatigue in strength training is difficult, due to missing criterion measures and the complexity of fatigue. Thus, a variety of methods are used to determine fatigue. The aim of this study was to use a principal component analysis (PCA) on a multifactorial data-set based on kinematic measurements to determine fatigue. Twenty participants (strength training experienced, 60% male) executed 3 sets of 3 exercises with 50 (12 repetitions), 75 (12 repetitions) and 100%-12 RM (RM). Data were collected with a 3D accelerometer and analysed by a newly developed algorithm to evaluate parameters for each repetition. A PCA with six variables was carried out on the results. A fatigue factor was computed based on the loadings on the first component. One-way ANOVA with Bonferroni post hoc analysis was calculated to test for differences between the intensity levels. All six input variables had high loadings on the first component. The ANOVA showed a significant difference between intensities (p < 0.001). Post-hoc analysis revealed a difference between 100% and the lower intensities (p < 0.05) and no difference between 50 and 75%-12RM. Based on these results, it is possible to distinguish between fatigued and non-fatigued sets of strength training.  相似文献