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Visual representation fidelity and self‐explanation prompts in multi‐representational adaptive learning
Author(s) -
Joo Hyun,
Park Jongchan,
Kim Dongsik
Publication year - 2021
Publication title -
journal of computer assisted learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.583
H-Index - 93
eISSN - 1365-2729
pISSN - 0266-4909
DOI - 10.1111/jcal.12548
Subject(s) - fidelity , schematic , mental representation , representation (politics) , computer science , cognition , inference , adaptive learning , visual learning , instructional design , human–computer interaction , cognitive psychology , cognitive load , psychology , cognitive science , multimedia , artificial intelligence , telecommunications , electronic engineering , neuroscience , politics , law , political science , engineering
In their prior research on adaptive instruction for multi‐representational learning, the researchers explored various perspectives on designing visual representations and scaffolds. However, controversies and discrepancies regarding the fidelity of visual representations and self‐explanation prompts have yet to be resolved. This research thus examines types of visual representations and self‐explanation prompts and thereby suggests instructional strategies for multi‐representational adaptive learning. Sixty‐nine college students participated in a 2 × 2 between‐subjects study design (schematic only and adaptively increasing the fidelity of visual representation as well as fixed and fading self‐explanation prompts). Adaptively increasing visual fidelity was shown to be effective for mental model construction. Knowledge inference was most enhanced in the group utilising both adaptive approaches. The increased germane cognitive load appears to have mediated, in particular, the effects of visually adaptive instruction. This research suggests that visually adaptive instruction should include customized self‐explanation supports to ensure successful multi‐representational adaptive learning. This research reveals that sequencing visual representations with increasing fidelity as learning progress in instructional materials and offering fading support for prompts tailored to learning progress are the two effective and complementary ways to ensure customized learning.