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Abstract 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. The purpose of this review is to give an overview of research in this field and to synthesize the findings. For this review, 42 publications on student-generated SMA dating from 2005 to 2019 were studied. The publications were systematically categorized on learning outcomes, learning processes, learning environment, and student prerequisites. Most studies were of a qualitative nature, and a significant portion (24 out of 42) pertained to student teachers. 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. 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. Comparative quantitative studies are needed in order to judge the effectiveness of SMA in terms of both cognitive and non-cognitive learning outcomes.
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Stored only when both papers are in the corpus — 45 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.