Abstract

Researchers have proposed that the ability to pay attention to teachers’ instruction is a prerequisite for learning. However, meaningful learning is often challenged by the presence of rule-breaking behaviors. In this study, we argue that although students exhibit different attention-related behaviors in all instructional settings, some behaviors are unique to a typical classroom. It is still unclear what factors uniquely determine students’ attention-related behaviors in animated programming environments because of the paucity of research evidence in this area. This study investigates students’ attention-related behaviors during animated programming instruction, including attentional growth trajectory, the nature of differences in attention-related behaviors, and predictors of these behaviors. Our analysis involved 8 classroom videos that collected the programming activities of 30 university students in our previous study. The video files were annotated on a one-dimensional, continuous scale, yielding 1,920 timestamped data points. The data on attentional trajectories and differences in attention-related behaviors were analyzed using latent and multi-level growth modeling, respectively, while data focusing on the predictors of attentional processes were analyzed using the Random Forest machine learning algorithm. We found that students’ attentional growth trajectory is linear and accelerates toward on-task events. However, these behaviors vary within and between students, leading to differences in attention-related behaviors. The results also revealed that individual and instructional characteristics predict the differences in attention-related behaviors. The findings highlight the importance of structured topics, safe classroom environments, quality instructional support, and interactive multimedia objects that activate students’ memory, eliminate task difficulty, and reduce the amount of mental resources required for meaningful learning.

Full Text
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