Using multiple, dynamically linked representations to develop representational competency and conceptual understanding of the earthquake cycle

被引:0
|
作者
Lore, Christopher [1 ]
Lee, Hee-Sun [1 ]
Pallant, Amy [1 ]
Chao, Jie [1 ]
机构
[1] Concord Consortium, Concord, MA USA
基金
美国国家科学基金会;
关键词
Teaching/learning strategies; Simulations; Pedagogical issues; Applications in subject areas; GRAPHICAL REPRESENTATIONS; FRACTIONS; SYSTEMS;
D O I
10.1016/j.compedu.2024.105149
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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 (eta(2) = 0.053, p < 0.001), were significantly correlated to student performance on a summative geoscience investigation.
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页数:12
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