• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.247-257
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• 2017

The aims of this study are to analyse how smartphone affects (1)self-control and (2)self-directed learning ability of middle-school students, and also affect (3)self-directed learning ability by mediation of self-control. For these aims, this study used Seoul Educational Longitudinal Study panel data which was conducted in 2015, and conducted multi-group comparisons using structural equation modeling analyses. In analysis of smartphone effects, the use of smartphone of students was divided into 'for entertainment' and 'for learning', and also their first time of smartphone use was considered(elementary vs. middle school). Results indicated that while 'smarphone use for entertainment' was related negatively with self-control and self-directed learning ability of students, 'smarphone use for learning' was related positively, regardless of when smartphone was first used. Also, while 'smarphone use for entertainment' was related negatively with self-directed learning ability by mediation of self-control, 'smarphone use for learning' was related positively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.811-819
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• 2014

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone.