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    Snapshot of Computational Thinking in Turkey: A Critique of 2019 Bebras Challenge
    (2022) Kalelioglu, Filiz; Dogan, Dilek; Gulbahar, Yasemin
    This study aims to provide a deeper understanding about the Bebras tasks, which is one of the computational thinking (CT) unplugged activities, in terms of age level, task category, and CT skills. Explanatory sequential mixed method was adopted in the study in order to collect data according to the research questions. The participants of the study were 113,653 school students from different age levels. Anonymous data was collected electronically from the Turkey 2019 Bebras challenge. Factor analysis was employed to reveal the construct validity to determine how accurately the tool measured the abstract psychological characteristics of the participants. In ad-dition, the item discrimination index was calculated to measure how discriminating the items in the challenge were. Qualitative data gathered through the national Bebras workshop was analysed according to content analysis. The findings highlighted some interesting points about the implica-tions of the Bebras Challenge for Turkey, which are discussed in detail. Furthermore, common problems of Bebras tasks are identified and possible suggestions for improvement are listed.
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    Image processing based rapid upper limb assessment method
    (2017) Can, Gulin Feryal; Figlali, Nilgun
    Occupational Musculoskeletal System Disorders (OMSDs) are disorders that inflict a great deal of economical burden on enterprises and nations, decrease quality, productivity and cause inability to sustain livings of employees. One of the most important factor that cause OMSDs is working posture. In literature, there are various methods for determining risk levels of working postures. In this study because of its common usage, Rapid Upper Limb Assessment Method (RULA) that identfies hazard level created by working postures on employees' upper limb musculoskeletal health is selected for improving with image processing systems. It is necassary to improve RULA's performance due to complications stemming from its implementation method based on observation, lack of information on the best duration of observation, subjectivity on results and extensive analysis time etc. For compansate these requirements a modified method is proposed in this study named as Advanced RULA (ARULA). Reliability and validity analysis are implemented statistically for ARULA. As a result, ARULA is recommended as a practical tool for analyzing risk levels of working postures for tasks that contain intensive usage of upper extremity.