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.

中文翻译：

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使用多个动态链接的表示来发展地震周期的表示能力和概念理解


使用计算方法来产生和解释多种科学表示现在是许多科学学科的常见做法。研究表明，学生在跨越、联系和理解多种表征方面存在困难。需要培养学生针对特定任务的表征能力，以便使用多种表征进行推理和进行科学研究。在本研究中，我们关注三种表征能力：1）表征之间的联系，2）从多种表征中构建学科意义，3）概念化从多种表征中衍生的领域相关内容。我们开发了一个基于块代码的计算建模环境，具有三种不同的表示形式，并将其嵌入在线活动中，供学生围绕地震周期进行调查。这三种表示包括块代码的程序表示、土地变形累积的几何表示以及随时间推移的变形累积的图形表示。我们研究了学生的代表性能力的程度，以及哪些能力与学生未来在计算支持的地球科学研究中的表现最相关。结果表明，431 名学生中的大多数至少表现出某种形式的表达能力。然而，相对少数的学生表现出复杂的联系、意义建构和概念化的水平。七项代表性能力中的五项，最突出的是代码意义构建（η2 = 0.053，p < 0.001），与学生在总结性地球科学研究中的表现显着相关。