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Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction
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Yeh, Kuan-Hue; She, Hsiao-Ching – Computers & Education, 2010
The purpose of this study is to examine the difference in effectiveness between two on-line scientific learning programs--one with an argumentation component and one without an argumentation component--on students' scientific argumentation ability and conceptual change. A quasi-experimental design was used in this study. Two classes of 8th grade…
Descriptors: Experimental Groups, Control Groups, Persuasive Discourse, Chemistry
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Wang, Pei-Yu; Vaughn, Brandon K.; Liu, Min – Computers & Education, 2011
This study examined the impact of animation interactivity on novices' learning of introductory statistics. The interactive animation program used in this study was created with Adobe Flash following Mayer's multimedia design principles as well as Kristof and Satran's interactivity theory. This study was guided by three main questions: 1) Is there…
Descriptors: Feedback (Response), Control Groups, Animation, Computer Assisted Instruction
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Wieling, M. B.; Hofman, W. H. A. – Computers & Education, 2010
To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional face-to-face course approach? In a between-subjects…
Descriptors: Feedback (Response), Grade Point Average, Predictor Variables, Lecture Method
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Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction