探索动画编程环境中的注意力动态:轨迹、可变性和预测因素

Abdullahi Yusuf, Norah Md Noor, Lateef Adeyemi Yusuf
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摘要

研究人员提出,关注教师教学的能力是学习的先决条件。然而,有意义的学习往往会受到违反规则行为的挑战。在本研究中,我们认为,虽然学生在所有教学环境中都会表现出不同的注意力相关行为,但有些行为是典型课堂所独有的。目前还不清楚在动画编程环境中,哪些因素独特地决定了学生的注意力相关行为,因为这方面的研究证据很少。本研究调查了学生在动画编程教学中与注意力相关的行为,包括注意力的成长轨迹、与注意力相关行为差异的性质以及这些行为的预测因素。我们的分析涉及 8 个课堂视频,这些视频收集了我们之前研究中 30 名大学生的编程活动。视频文件采用一维连续刻度进行注释,共产生 1,920 个带有时间戳的数据点。我们使用潜增长模型和多层次增长模型分别分析了注意力轨迹数据和注意力相关行为差异数据,并使用随机森林机器学习算法分析了注意力过程预测因素数据。我们发现,学生的注意力增长轨迹是线性的,并朝着任务事件的方向加速。然而,这些行为在学生内部和学生之间存在差异,从而导致注意力相关行为的不同。研究结果还显示,个体和教学特点可预测注意力相关行为的差异。研究结果凸显了有条理的主题、安全的课堂环境、高质量的教学支持以及交互式多媒体对象的重要性,它们能激活学生的记忆、消除任务难度并减少有意义学习所需的心理资源量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Attentional Dynamics in Animated Programming Environments: Trajectories, Variability, and Predictors
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.
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