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Wang, Tzu-Ling; Tseng, Yi-Kuan – Journal of Computer Assisted Learning, 2020
The purpose of this study was to investigate not only the effectiveness of dynamic versus static visualizations on learning star motions but also the influence of students' spatial abilities with these two types of visualizations on their learning. We assigned 155 fifth-grade students to either a dynamic or a static condition. We used a science…
Descriptors: Teaching Methods, Computer Assisted Instruction, Spatial Ability, Science Achievement
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Erbas, Cagdas; Demirer, Veysel – Journal of Computer Assisted Learning, 2019
This study aimed to investigate the effects of augmented reality (AR) activities on students' academic achievement and motivation in a biology course. For this purpose, a mixed study was conducted, and a pretest and posttest control group model was used. In addition, the opinions of the experimental group students and the teacher about the AR…
Descriptors: Biology, Science Instruction, Teaching Methods, Science Achievement
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Thai, Ngoc Thuy Thi; De Wever, Bram; Valcke, Martin – Journal of Computer Assisted Learning, 2020
This study compares four learning environments: face-to-face learning (F2F), fully e-learning (EL), blended learning (BL), and flipped classroom (FC) with respect to students' learning performance. Moreover, this present research studies changes in perceived flexibility, intrinsic motivation, self-efficacy beliefs of students, and the interaction…
Descriptors: Blended Learning, Teaching Methods, Comparative Analysis, Self Efficacy
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Starbek, P.; Erjavec, M. Starcic; Peklaj, C. – Journal of Computer Assisted Learning, 2010
The main goal of this study was to explore whether the use of multimedia in genetics instruction contributes more to students' knowledge and comprehension than other instructional modes. We were also concerned with the influence of different instructional modes on the retention of knowledge and comprehension. In a quasi-experimental design, four…
Descriptors: Comprehension, Quasiexperimental Design, Prior Learning, Genetics