• Journal of Korea Game Society
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• v.16 no.5
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• pp.79-88
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• 2016

This study analyzes the status of female characters in several fighting action games. From July 15 to 17, 2016, the world-famous fighting action game tournament EVO 2016 was held in Las Vegas, Nevada. In this tournament, nine fighting action games were selected for competition, and this study examines the status of female characters on the basis of 1) Appearance 2) Ability in these fighting actions games. After doing comparison between female characters and male characters in the fighting action game, it reflects on the status and right of female characters in the virtual world.

• Journal of The Korean Association For Science Education
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• v.36 no.3
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• pp.389-397
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• 2016

This study aims to test the efficacy of English-based automated computer scoring models and machine translation to score Korean college students' written responses on natural selection concept items. To this end, I collected 128 pre-service biology teachers' written responses on four-item instrument (total 512 written responses). The machine translation software (i.e., Google Translate) translated both original responses and spell-corrected responses. The presence/absence of five scientific ideas and three $na{\ddot{i}}ve$ ideas in both translated responses were judged by the automated computer scoring models (i.e., EvoGrader). The computer-scored results (4096 predictions) were compared with expert-scored results. The results illustrated that no significant differences in both average scores and statistical results using average scores was found between the computer-scored result and experts-scored result. The Pearson correlation coefficients of composite scores for each student between computer scoring and experts scoring were 0.848 for scientific ideas and 0.776 for $na{\ddot{i}}ve$ ideas. The inter-rater reliability indices (Cohen kappa) between computer scoring and experts scoring for linguistically simple concepts (e.g., variation, competition, and limited resources) were over 0.8. These findings reveal that the English-based automated computer scoring models and machine translation can be a promising method in scoring Korean college students' written responses on natural selection concept items.