{"title":"Promoting learning transfer in science through a complexity approach and computational modeling.","authors":"Janan Saba, Hagit Hel-Or, Sharona T Levy","doi":"10.1007/s11251-023-09624-w","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11251-023-09624-w.</p>","PeriodicalId":47990,"journal":{"name":"Instructional Science","volume":"51 3","pages":"475-507"},"PeriodicalIF":2.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031696/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Instructional Science","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s11251-023-09624-w","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Instructional Science, An International Journal of the Learning Sciences, promotes a deeper understanding of the nature, theory, and practice of learning and of environments in which learning occurs. The journal’s conception of learning, as well as of instruction, is broad, recognizing that there are many ways to stimulate and support learning. The journal encourages submission of research papers, covering a variety of perspectives from the learning sciences and learning, by people of all ages, in all areas of the curriculum, in technologically rich or lean environments, and in informal and formal learning contexts. Emphasizing reports of original empirical research, the journal provides space for full and detailed reporting of major studies. Regardless of the topic, papers published in the journal all make an explicit contribution to the science of learning and instruction by drawing out the implications for the design and implementation of learning environments. We particularly encourage the submission of papers that highlight the interaction between learning processes and learning environments, focus on meaningful learning, and recognize the role of context. Papers are characterized by methodological variety that ranges, for example, from experimental studies in laboratory settings, to qualitative studies, to design-based research in authentic learning settings. The Editors will occasionally invite experts to write a review article on an important topic in the field. When review articles are considered for publication, they must deal with central issues in the domain of learning and learning environments. The journal accepts replication studies. Such a study should replicate an important and seminal finding in the field, from a study which was originally conducted by a different research group. Most years, Instructional Science publishes a guest-edited thematic special issue on a topic central to the journal''s scope. Proposals for special issues can be sent to the Editor-in-Chief. Proposals will be discussed in Spring and Fall of each year, and the proposers will be notified afterwards. To be considered for the Spring and Fall discussion, proposals should be sent to the Editor-in-Chief by March 1 and October 1, respectively. Please note that articles that are submitted for a special issue will follow the same review process as regular articles.
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