Browsing by Author "Göğüş,A."
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Article Citation Count: 1E-content design for TabletPC implementation project(Sakarya University, 2015) Göğüş,A.; Yanıkoğlu,B.This paper presents the findings of a study conducted with a 1st grade students in an elementary school to test econtent modules developed for TabletPC implementation during a grant project called “Development and Implementation of Handwriting Recognition Technology Used in Smart Classrooms” and supported by the Scientific & Technological Research Council of Turkey (TUBITAK). The effectiveness of the use of the developed technologies for handwriting recognition and the use of the learning modules by both students and teachers are investigated. Reflections from students and teachers are discussed. © The Turkish Online Journal of Educational Technology.Article Citation Count: 2A scoring approach for the assessment of study skills and learning styles(International Journal of Information and Education Technology, 2020) Göğüş,A.; Göğüş, Aytaç; Eğitim Bilimleri / Educational SciencesThis paper presents the application of a scoring method and algorithm, adapted from the domain of financial risk management, for the computer-based assessment of study skills and learning styles of university students. The goal is to provide a single score that summarizes the overall intensity of a student’s study skills and, in effect, develop a deeper understanding of the relation between learning styles and study skills. The dimensionality reduction obtained through the scoring algorithm also enables comparing the single-dimensional study skill scores of students for various learning styles. The algorithm computes a weight for each study skill to measure its linear contribution to the overall study skill score, also providing a natural ranking of various study skills with respect to impact on total score. Statistical tests have been conducted to measure the differences in scores for various styles in Kolb’s four-region and nine-region models. The results suggest that students with different learning styles can have statistically significant differences in their overall study skill scores. The primary contribution of the study is illustrating how a scoring approach, based on unsupervised machine learning, can enable a deep understanding of learning styles and development of educational strategies. © 2020 by the authors.