2010 Zhang and Linn 2011), to make connections with prior knowledge (Akaygun and Jones 2013), to identify conflicts among their ideas (Chi 2009), and to provide feedback about students’ understanding (Stieff et al. Student-generated representations have been used to evaluate students’ understanding of scientific concepts (Hubber et al. Moreover, the results of previous studies confirm that learning gains are greater when students generate their own representations in general, as opposed to working with expert-generated representations (Kozma and Russell 2005 Wu and Puntambekar 2012). Visual representations have been reported to contribute to the development of students’ learning of science (Evagorou et al. Comparative quantitative studies are needed in order to judge the effectiveness of SMA in terms of both cognitive and non-cognitive learning outcomes. Also, the science concept that is to be presented as a SMA should be self-contained, dynamic in nature, and not too difficult to represent. The findings show that SMA can promote deep learning if appropriate scaffolding is provided, for example, in terms of presenting general strategies, asking questions, and using expert representations. Most studies were of a qualitative nature, and a significant portion (24 out of 42) pertained to student teachers. The publications were systematically categorized on learning outcomes, learning processes, learning environment, and student prerequisites.
For this review, 42 publications on student-generated SMA dating from 2005 to 2019 were studied. The purpose of this review is to give an overview of research in this field and to synthesize the findings. In recent years, student-generated stop-motion animations (SMAs) have been employed to support sharing, constructing, and representing knowledge in different science domains and across age groups from pre-school to university students.
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