An Investigation of Human-Computer Interaction Approaches Beneficial to Weak Learners in Complex Animation Learning

Oct 15, 2026

Animation supports instruction in complex domains, yet the evidence on its effectiveness varies and weak learners may not benefit when they receive information passively. This study examines whether specific human-computer interaction approaches can support weak learners with limited domain knowledge and difficulty comprehending complex subjects during animation-based data-structures learning. Three interaction approaches are evaluated using near- and far-transfer measures and learning-process surveys. Results show reason-predict-combination yields the strongest transfer performance and highest perceived usefulness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Effective Interactions in Educational Animations

An Investigation of Human-Computer Interaction Approaches Beneficial to Weak Learners in Complex Animation Learning Yu-Fang Yeh Aletheia University, Taiwan Abstract Animation is one of the useful contemporary educational technologies in teaching complex subjects. There is a growing interest in proper use of learner-technology interaction to promote learning quality for different groups of learner needs. The purpose of this study is to investigate if an interaction approach supports weak learners, who have poor domain knowledge and comprehension difficulty of the learning subject, in complex animation learning. Three interaction approaches were designed and evaluated in an educational animation program teaching a complex subject of data structures. Participants were 70 undergraduate students performed poorly in the experimental course of introductory data structures. They were randomly assigned into one of the three interaction approaches: purereason-dialogue, predict-oriented, and reason-predict-combination interactions. Learning effects of these interaction approaches were measured by near-transfer and far-transfer tests as well as learning process surveys including perceived content difficulty, mental effort expenditure, and usefulness of the interaction approach. Findings indicate that the reasonpredict-combination interactions approach led to the greatest transfer performance and was rated by students as the most useful interaction approach for understanding the animation content. The findings generally recommend that for weak learners, interactions of reasoning dialogue is effective to develop near-transfer ability at the initial learning phase, whereas when learners’ knowledge grows to be capable of near-transfer task, the predictoriented interactions become more helpful to gain far-transfer knowledge. Implications for design principles for interactive instructional animations and recommendations for future research are discussed. Keywords: Human-computer interaction; Instructional animation; Weak learner; Data structures Introduction Nowadays, animation is considered as a useful instructional medium to depict complex subjects that involve extensive mental imagination for understanding such as cardiovascular system, meteorological phenomena, and computer data structures and algorithms. The educational power of animation is that it can help learners directly visualize temporal changes in state, shape and space of dynamic contents, and hence free up learners’mental resources from memorizing trivial object/event changes to performing important cognitive processes. Although animations have great educational potential, research evidence about their educational effectiveness is mixed. While some empirical studies show positive effects of animations on learning (Levy, Ben-Ari, & Uronen, 2003; Yeh, Chen, Hung, & Hwang, 2010) . Other studies have found neutral effects (ChanLin, 1998; Morrison & Tversky, 2001; Price, 2002) or even negative effects (Scheiter, Gerjets, & Catrambone, 2006; Schnotz, Böckheler, & Grzondzeil, 1999) . One possible reason explaining no/negative effects might be that learners often are passive information receivers in animation learning, and tend not to actively reflect animation contents (Hundhausen, Douglas, & Stasko, 2002) . To help learners taking advantages of animations, various interactions are used (Mayer & Mareno, 2002) . The animation-interactivity operation and reasoning dialogue-cannot only increase learning involvement and learning responsibility, but also trigger off necessary cognitive processes for understanding (Betrancout, 2005) . To be effective, interactions must be well designed in accordance with learners’ zone of proximal development (ZPD) (Vygotsky, 1978) . The ZPD of a learner refers the knowledge and cognitive skills he/she can reach with interactions’assistance. Interactions that are not tailored to learner’s ZPD, i.e. too simple to challenge new knowledge or

FAQs

What problem does the study address about animation learning?

The study addresses mixed evidence on animation effectiveness and the concern that weak learners may remain passive, making it hard to reflect animation content and develop understanding.

How were the interaction approaches designed and compared?

Three interaction approaches—pure reason-dialogue, predict-oriented, and reason-predict-combination—were implemented in an educational animation program and evaluated with undergraduate participants assigned randomly.

Which interaction approach performed best for weak learners and why does it matter?

Reason-predict-combination produced the greatest transfer performance and was rated most useful. The findings suggest different interaction types can help at different learning phases as learners’ knowledge grows.

Oct 15, 2026

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