Visual representation fidelity and self‐explanation prompts in multi‐representational adaptive learning

• Published in: Journal of computer assisted learning Vol. 37; no. 4; pp. 1091 - 1106
• Main Authors: Joo, Hyun, Park, Jongchan, Kim, Dongsik
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.08.2021; Wiley; Wiley Subscription Services, Inc
• Subjects: Accuracy; Adaptive algorithms; Adaptive learning; Between-subjects design; Cognitive load; Cognitive Processes; College Students; Colleges & universities; Computer Assisted Instruction; Cues; Customization; Difficulty Level; Educational materials; Educational Strategies; Fading; Fidelity; Inference; Inferences; Instructional materials; Learning; mental model; Representations; Scaffolding; Scaffolding (Teaching Technique); Scaffolds; Schemata (Cognition); self‐explanation prompts; Teaching Methods; Textbooks; Visual Aids; Visual effects; visual representation fidelity
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.12548

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. Lay Description What is already known about this topic Various perspectives on designing visual representations and scaffolds have been taken into consideration to construct optimal adaptive instruction for multi‐representational learning: the varying degree of visual representation fidelity (i.e., schematic up to realistic) and the level of support for self‐explanation (i.e., structured or less‐structured prompts). What this paper adds Adaptively increasing the fidelity of the visual representations can enhance learners' pictorial constructions of mental models and promote knowledge inference. Fading scaffolding of self‐explanation prompts helps prevent the expertise reversal effect by averting any possible cognitive overload, and promote knowledge inference. Adaptive approach may influence the increase in the germane cognitive load to the most optimal degree to mediate the learning outcome. Implications for practice and/or policy Sequencing visual representations with increasing fidelity as learning progress in textbooks and other instructional materials, whether analogue or digital, and offering fading support for prompts tailored to learning progress are the two effective and complementary ways to ensure customized learning. The results regarding the effects of visual representation fidelity and self‐explanation prompts can be used to guide the application of adaptive instructional algorithms in intelligent tutoring systems.