The emphasis of using computing tools in scientific practice has called for new forms of scientific modeling. Therefore, researchers are paying increasing attention to computational scientific modeling in which students use the computational power of computers to model and learn about science phenomena. However, computational scientific modeling is challenging since it involves not only scientific concepts but also uses computational representations to model the science concepts. This study hypothesizes that prompting students to self-explain critical subgoals of computational scientific modeling before taking part in the construction of models would help them construct correct computational models. This study recruited 65 10th grade students in a 6-week program. They were randomly assigned to a self-explanation group ( = 29) where students learned with the self-explanation for subgoals of computational scientific modeling, and a demonstration group ( = 36) where the teacher directly demonstrated the modeling process before students took part in the modeling activity. This study collected the students' performance in a paper-based and hands-on modeling test, and also their modeling actions in the hands-on test to understand the impact of the self-explanation scaffolding on their computational scientific modeling. The results showed that the two groups demonstrated similar levels of improvements in the paper-based test, suggesting that both types of scaffolding are helpful for computational scientific modeling learning. However, the self-explanation group demonstrated significantly better modeling quality in the hands-on test. Furthermore, the spotlight analysis found the moderation effect of the modeling actions on the relation between the two treatments and the model quality. The self-explanation group constructed high quality models if they took only a low number of modeling actions. Conversely, frequent modeling actions are necessary for the demonstration group to construct quality scientific models. The results suggest that the self-explanation scaffolding is more effective since students did not simply rely on the trial-and-error strategy, but adopted a strategic approach to constructing scientific models.

中文翻译：

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具有子目标自我解释和演示支架的计算科学建模的学习分析

在科学实践中强调使用计算工具需要新形式的科学建模。因此，研究人员越来越关注计算科学建模，学生利用计算机的计算能力来建模和了解科学现象。然而，计算科学建模具有挑战性，因为它不仅涉及科学概念，而且还使用计算表示来对科学概念进行建模。本研究假设，促使学生在参与模型构建之前自我解释计算科学建模的关键子目标将有助于他们构建正确的计算模型。这项研究招募了 65 名 10 年级学生，参加为期 6 周的项目。他们被随机分配到自我解释组（= 29），学生通过对计算科学建模子目标的自我解释来学习，以及演示组（= 36），在学生参与之前，教师直接演示建模过程建模活动。本研究收集了学生在纸质动手建模测试中的表现，以及他们在动手测试中的建模动作，以了解自解释支架对其计算科学建模的影响。结果表明，两组在纸质测试中表现出相似的改进水平，这表明两种类型的脚手架都有助于计算科学建模学习。然而，自我解释组在动手测试中表现出明显更好的建模质量。此外，聚光灯分析发现建模动作对两种处理和模型质量之间的关系有调节作用。如果自我解释组只采取少量的建模操作，他们就构建了高质量的模型。相反，示范组需要频繁的建模行动来构建高质量的科学模型。结果表明，自我解释脚手架更加有效，因为学生不再简单地依赖试错策略，而是采用了构建科学模型的策略方法。