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Instructional Science最新文献

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Promoting learning transfer in science through a complexity approach and computational modeling. 通过复杂性方法和计算建模促进科学中的学习迁移。
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-01-01 Epub Date: 2023-03-22 DOI: 10.1007/s11251-023-09624-w
Janan Saba, Hagit Hel-Or, Sharona T Levy

This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. In this study, we investigated students' conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer-both near and far-with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities' properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities.

Supplementary information: The online version contains supplementary material available at 10.1007/s11251-023-09624-w.

本文关注科学学习、理解复杂性和计算思维(CT)之间的协同作用,以及它们对近距离和远距离学习迁移的影响。基于计算机的模型构建和知识转移之间的潜在关系还有待探索。我们研究了使用Much.Mater.in.Motion(MMM)平台对系统现象进行建模的中学生。这项工作的一个显著创新是基于复杂性的视觉认知结构,它支撑着Much.Mater.in.Motion(MMM)平台,该平台指导学生对复杂系统的建模。这种认识结构表明,可以通过定义实体并赋予它们(1)属性、(2)动作和(3)彼此之间及其环境的相互作用来描述和建模复杂系统。在这项研究中,我们调查了学生对科学、系统理解和CT的概念理解。我们还探讨了基于复杂性的结构是否可以跨不同领域转移。本研究采用准实验、前测干预、后测对照的对照组设计,实验组26名七年级学生,对照组24名。研究结果表明,构建计算模型的学生显著提高了他们的科学概念知识、系统理解和CT。他们还表现出相对较高的近距离和远距离迁移程度,学习的远距离迁移效果中等。对于远转移项目,它们的解释包括微观层面上实体的性质和相互作用。最后,我们发现学习CT和学习如何复杂思考对学习迁移有独立的贡献,而科学中的概念理解只通过系统中实体的微观行为来影响迁移。这项工作的一个核心理论贡献是提供了一种促进远距离转移的方法。这种方法建议使用我们想要支持的一般思维过程的视觉认知支架,如MMM界面上基于复杂性的结构所示,并将这些视觉结构纳入核心问题解决活动中。补充信息:在线版本包含补充材料,网址为10.1007/s11251-023-09624-w。
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引用次数: 0
Sixth graders’ selection and integration when writing from multiple online texts 六年级学生在多篇网络文章中的选择和整合
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-12-30 DOI: 10.1007/s11251-022-09613-5
Nina Kullberg, Carita Kiili, Ivar Bråten, Roberto I. González-Ibáñez, Paavo H. T. Leppänen
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引用次数: 2
Examining relations between math anxiety, prior knowledge, hint usage, and performance of math equivalence in two different online learning contexts 在两种不同的在线学习环境中,研究数学焦虑、先验知识、暗示使用和数学等效性表现之间的关系
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-12-10 DOI: 10.1007/s11251-022-09604-6
Alisionna Iannacchione, Erin Ottmar, Vy-Vy Ngo, C. Mason, Jenny Yun Chen Chan, Hannah Smith, Kathryn C. Drzewiecki, S. Shaw
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引用次数: 1
Computer versus longhand note taking: Influence of revision 电脑与手写笔记:复习的影响
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-11-28 DOI: 10.1007/s11251-022-09605-5
Abraham E. Flanigan, Kenneth A. Kiewra, Junrong Lu, D. Dzhuraev
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引用次数: 1
Generative learning supports learning from video lectures: evidence from an EEG study 生成式学习支持视频学习:来自脑电图研究的证据
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-11-24 DOI: 10.1007/s11251-022-09602-8
Zhongling Pi, Yi Zhang, Caixia Liu, Wei Zhou, Jiumin Yang
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引用次数: 3
Short pedagogical training in supporting university teachers’ professional vision: A comparison of prospective and current faculty teachers 支持大学教师专业视野的短期教学培训:未来教师和现任教师的比较
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-11-19 DOI: 10.1007/s11251-022-09603-7
Neea Heinonen, Nina Katajavuori, M. Murtonen, I. Södervik
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引用次数: 4
Teaching sourcing during online inquiry – adolescents with the weakest skills benefited the most 在线调查中的教学来源——技能最弱的青少年受益最大
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-10-27 DOI: 10.1007/s11251-022-09597-2
Elina K. Hämäläinen, Carita Kiili, Eija Räikkönen, M. Lakkala, Liisa Ilomäki, A. Toom, Miika Marttunen
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引用次数: 1
Promoting teachers' in-class SRL practices: effects of Authentic Interactive Dynamic Experiences (AIDE) based on simulations and video 促进教师课堂SRL实践:基于模拟和视频的真实互动动态体验(AIDE)的效果
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-09-15 DOI: 10.1007/s11251-022-09598-1
Orna Heaysman, B. Kramarski
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引用次数: 3
Spatial supports for comparison in educational science images 教育科学图像比较的空间支持
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-09-13 DOI: 10.1007/s11251-022-09599-0
Benjamin D. Jee, Bryan J. Matlen, Monica Greenlaw, Nina K Simms, D. Gentner
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引用次数: 1
Orchestrating the flow and advancement of knowledge artifacts in an online class 在线课堂中编排知识工件的流程和推进
IF 2.5 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2022-09-02 DOI: 10.1007/s11251-022-09596-3
Bodong Chen, Hong Shui, Stian Håklev
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引用次数: 3
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Instructional Science
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