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Lee, Hyeon Woo – Asia-Pacific Education Researcher, 2015
This study aims to examine the effects of a touchscreen device (iPad in this study) on students' learning compared to a desktop PC in terms of tactile vision. The participants were 56 undergraduate students: 24 studied using a desktop PC (mouse-based interaction) and the others studied using a touchscreen device (touch-based interaction) about the…
Descriptors: Manipulative Materials, Academic Achievement, Comparative Analysis, Handheld Devices
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Yelamarthi, Kumar – Journal of STEM Education: Innovations and Research, 2016
Many interesting research and design questions occur at the intersections of traditional disciplines, yet most coursework and research programs for undergraduate engineering students are focused on one discipline. This leads to underutilization of the potential in better preparing students through multidisciplinary projects. Identifying this…
Descriptors: STEM Education, Robotics, Engineering Education, Technology Education
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Swiggett, Wanda D.; Kotloff, Laurie; Ezzo, Chelsea; Adler, Rachel; Oliveri, Maria Elena – ETS Research Report Series, 2014
The computer-based "Graduate Record Examinations"® ("GRE"®) revised General Test includes interactive item types and testing environment tools (e.g., test navigation, on-screen calculator, and help). How well do test takers understand these innovations? If test takers do not understand the new item types, these innovations may…
Descriptors: College Entrance Examinations, Graduate Study, Usability, Test Items
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Sozcu, Omer Faruk – Turkish Online Journal of Distance Education, 2014
This study examines the relationships between cognitive styles of field dependent learners with their attitudes towards e-learning (distance education) and instructional behavior in e-learning instruction. The Group Embedded Figures Test (GEFT) and the attitude survey (for students' preferences) towards e-learning instruction as distance education…
Descriptors: Foreign Countries, Cognitive Style, Electronic Learning, Distance Education
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Hafidi, Mohamed; Bensebaa, Taher – International Journal of Distance Education Technologies, 2015
The majority of adaptive and intelligent tutoring systems (AITS) are dedicated to a specific domain, allowing them to offer accurate models of the domain and the learner. The analysis produced from traces left by the users is didactically very precise and specific to the domain in question. It allows one to guide the learner in case of difficulty…
Descriptors: Intelligent Tutoring Systems, Foreign Countries, Interdisciplinary Approach, Universities
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment