使用多种动态连接的表征,培养地震周期的表征能力和概念理解能力

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Education Pub Date : 2024-08-28 DOI:10.1016/j.compedu.2024.105149
Christopher Lore, Hee-Sun Lee, Amy Pallant, Jie Chao
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引用次数: 0

摘要

使用计算方法来制作和解释多种科学表象现已成为许多科学学科的普遍做法。研究表明,学生在跨越、连接和感知多种表征方面存在困难。因此,有必要培养学生针对具体任务的表征能力,使他们能够利用多种表征进行推理和科学探究。在本研究中,我们重点关注三种表征能力:1) 表征之间的联系;2) 从多重表征中进行学科感知;3) 从多重表征中对与领域相关的内容进行概念化。我们开发了一个基于区块代码的计算建模环境,其中包含三种不同的表征,并将其嵌入到在线活动中,让学生围绕地震周期开展调查。这三种表征包括块编码的程序表征、陆地变形累积的几何表征以及变形随时间累积的图形表征。我们研究了学生的表征能力程度,以及哪些能力与学生未来在计算支持的地球科学调查中的表现最相关。结果表明,431 名学生中的大多数至少表现出了某种形式的表象能力。然而,相对较少的学生在表象的联系、感知和概念化方面表现出了较高的水平。在七种表象能力中,有五种能力与学生在终结性地球科学调查中的成绩有显著相关,其中最突出的是代码感知能力(η2 = 0.053, p < 0.001)。
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Using multiple, dynamically linked representations to develop representational competency and conceptual understanding of the earthquake cycle

Using computational methods to produce and interpret multiple scientific representations is now a common practice in many science disciplines. Research has shown students have difficulty in moving across, connecting, and sensemaking from multiple representations. There is a need to develop task-specific representational competencies for students to reason and conduct scientific investigations using multiple representations. In this study, we focus on three representational competencies: 1) linking between representations, 2) disciplinary sensemaking from multiple representations, and 3) conceptualizing domain-relevant content derived from multiple representations. We developed a block code-based computational modeling environment with three different representations and embedded it within an online activity for students to carry out investigations around the earthquake cycle. The three representations include a procedural representation of block codes, a geometric representation of land deformation build-up, and a graphical representation of deformation build-up over time. We examined the extent of students' representational competencies and which competencies are most correlated with students’ future performance in a computationally supported geoscience investigation. Results indicate that a majority of the 431 students showed at least some form of representational competence. However, a relatively small number of students showed sophisticated levels of linking, sensemaking, and conceptualizing from the representations. Five of seven representational competencies, the most prominent being code sensemaking (η2 = 0.053, p < 0.001), were significantly correlated to student performance on a summative geoscience investigation.

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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
期刊介绍: Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.
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