基于积木的编程活动中的交互模式可预测计算思维:性别、认知负荷、空间能力和编程能力差异分析

Abdullahi Yusuf, Norah Md Noor, Marcos Román-González
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引用次数: 1

摘要

计算思维(CT)研究的最新进展表明,计算思维对学龄儿童的学习大有裨益。长期以来,人们一直认为计算机编程困难重重,这对从编程教育中获得计算思维技能提出了挑战。为了降低编程难度,提高学生参与计算相关活动的积极性,人们开发了几种基于块的编程环境(BBPE)。尽管已有大量研究考察了学生在基于块的编程模式(BPM)活动中的互动水平,但文献中的一个主要空白是,很少有研究证据报告这些互动与 CT 之间的关联。因此,本研究调查了 BPM 活动期间的互动模式与 CT 技能之间的关联。本研究采用纵向方法,对同一参与者进行了为期八周的观察。本研究从尼日利亚一所研究型大学招募了 35 名计算机科学和计算机教育专业的二年级学生(平均年龄:19.8 岁;男生 23 人,女生 12 人)。对他们在研究期间的计算活动进行了录像。使用计算思维测试和计算思维量表收集了参与者的计算技能。研究结果显示了四种互动模式:学习者-学习者、学习者-内容、学习者-教师和学习者-指导者。学习者-学习者和学习者-内容是最普遍的互动模式。虽然在性别、认知负荷、空间能力和编程熟练程度方面存在明显差异,但这些互动模式对 CT 技能的预测作用很大。这项研究为教育工作者提供了将 BBPE 与学习编程和 CT 概念相结合的机会。虽然这种整合有可能在强有力的教育政策帮助下实现,但我们鼓励教师在编程活动中培养学生的协作精神。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interaction Patterns During Block-based Programming Activities Predict Computational Thinking: Analysis of the Differences in Gender, Cognitive Load, Spatial Ability, and Programming Proficiency
The recent advancement in computational thinking (CT) research has reported numerous learning benefits to school-age children. The long-standing perceived difficulty of computer programming has challenged the acquisition of CT skills from programming education. Several block-based programming environments (BBPEs) have been developed to reduce this difficulty and enhance active engagement in computational-related activities. Although numerous studies have examined students’ level of interactions during block-based programming modality (BPM) activities, a major gap in the literature is the paucity of research evidence reporting the association between these interactions and CT. This study, therefore, investigates the association between interaction patterns during BPM activities and CT skills. The present study employed a longitudinal approach where the same participants were observed over eight weeks. Thirty-five, second-year-level computer science and computer education students (mean age: 19.8; male = 23, female = 12) from a research university in Nigeria were recruited. Their computational activities over the study periods were video-recorded. The participants’ CT skills were collected using the computational thinking test and the computational thinking scale. Findings indicate four interaction patterns: learner–learner, learner–content, learner–teacher, and learner–distractor. Learner–learner and learner–content were prevalent. The interaction patterns significantly predict CT skills although significant differences exist across gender, cognitive load, spatial ability, and programming proficiency. The research has provided opportunities for educators to integrate BBPEs in learning programming and CT concepts. Although such integration is likely to occur with the help of strong educational policies, teachers are encouraged to cultivate the spirit of collaboration in students during programming activities.
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