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Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model
The present study classified and predicted high and low potential archers from a set of bio-physiological variables
trained via a machine learning technique namely k-Nearest Neighbour (k-NN). 50 youth archers drawn from various
archery programmes completed a one end archery shooting score test. Bio-physiological measurements of systolic
blood pressure, diastolic blood pressure, resting respiratory rate, resting heart rate and dietary intake were taken.
Multiherachical agglomerative cluster analysis was used to cluster the archers based on the variables tested into low,
medium and high potential archers. Three different k-NN models namely fine, medium and coarse were trained
based on the measured variables. The five-fold cross-validation technique was utilised in the present investigation. It
was shown from the present study, that the utilisation of k-NN is non-trivial in the classification of the performance of
the archers. © 2018, Springer Nature Singapore Pte Ltd
Empowering professional and ethical competence on reflective teaching practice in digital era
In the last decade, the reflective teaching practice has been considered to look at program and planning which should
be taken into action. However, the digital era has shifted teaching practice from direct interaction to virtual one. It has
challenges such as professional skills on online learning. This chapter aims at filling this gap by proposing the
framework model on reflective teaching practice to pay special attention to professionalism and ethical essence in a
way that can be taken seriously among the school holders. This chapter could enhance learning and professional
development of teachers, which should be seen as a continuing process combining formal, non-formal, and informal
learning so that students will have the chance to reflect in a direct way even though the educational practice is
constituted in a special way in various settings. © 2018, IGI Global. All rights reserved