• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.529-538
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• 2014

This study was conducted as an need analysis through close and open semi-structured surveys, in order to identify the adaptive elements of the adaptive e-learning system. The study was conducted on students majoring computer education in teacher's college. In terms of the process of the need analysis, Rossett Model was applied. For the research method, responses on the open questionnaire were analyzed. In terms of the analysis method, coding was used to extract the theme of the content, and through the constant comparison method, categorizing took place. As the element that offers adaption in the adaptive learning system, it escapes from the existing learning style, and recognized the importance of providing adaptability for different elements such as the learner's level, learning objectives, and learning contents. Especially, An instructional model was identified as an important element that helps reach rationality as well as efficiently conduct the learning objectives.

• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.219-230
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• 2017

In this study, we developed a data science education program using spreadsheet, applied it after educational method to improve elementary school student 's Computational Thinking, and then verified its effect. Based on the results of preliminary requirement analysis conducted by Rossett's request analysis the educational program was developed based on the procedure of the ADDIE model which is the representative model of the teaching design based on the result of prior requirement analysis of 205 elementary school students and computer teaching major 20 incumbent elementary school teachers, applying Rossett's requirement analysis model. In order to verify the effect of the developed educational program, we are promoting 42 hours of lecture for a total of 6 days for 20 students of applicants who volunteered for volunteer votes of educational donation programs implemented at ${\bigcirc}{\bigcirc}$University, We analyzed the educational effect using the results of pre-post test. As a result of the analysis, we learned that the educational program developed in this study is effective for improving elementary school student 's Computational Thinking.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.551-562
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• 2020

The purpose of this study is to investigate the effects of Data Science Education classes using PBL-based App Inventor on elementary student Computational Thinking and Creativity. Based on the results of the pre-requisite analysis by Rossett's demand analysis model, PBL-based Data Science Education class was designed according to the procedure of ADDIE model which is 42 hours of classroom instruction for elementary student. As a result of the Paired t-test, it was proved that the Computational Thinking was statistically significantly improved in the post-test. In addition, as a result of the Paired t-test and Wilcoxon's signed rank test, it was found that the sub-factors of Creativity were 'Originality', 'Fluency', 'Closure', 'Average', and 'Index'. Therefore, it was confirmed that the PBL-based Data Science Education class using App Inventor is effective in improving Computational Thinking and Creativity of elementary student.