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

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.