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Case study: Developing long-term knowledge with Sprego

机译:案例研究:与SPREGO制定长期知识

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In Hungary, K-12 informatics/computer science education focuses on mostly surface-based methods. This approach can be observed in the teaching of several topics in the subject, of which we focus on spreadsheet management. This is further emphasized by regulatory documents - the Hungarian National Core Curriculum and Hungarian Curriculum Frameworks -, where handling algorithms, calling schemata, and problem-solving in general are only assigned to the programming topic. In the process of fulfilling the requirements of the school curricula and the various tool-centered exams, students become familiar with the software interfaces and how to navigate them, instead of developing computational thinking skills and learning how to approach and solve real-world problems. Our educational system is based on a spiral teaching approach; therefore, spreadsheet management is taught throughout several grades in a small number of lessons. Prior research shows that students learning spreadsheet management with surface-approach methods do not build up a reliable knowledge structure. These students cannot solve problems in contexts differing to the ones in which they learned the topic and cannot use their surface navigation abilities in different software environments. Our research group focuses on spreadsheeting with an algorithm-building and problem-solving method at the center of the teaching-learning process. For this purpose, we have developed and introduced the Sprego (Spreadsheet Lego) methodology. Sprego is based on Polya's four-step concept-based problem-solving approach, and its efficiency has already been proved compared to traditional low-mathability surface-approach methods. In the comparison of the low- and high-mathability approaches, several further questions arise, and amongst them one crucial aspect is how the different methods support the schema-construction and knowledge built up in long-term memory. In this paper we discuss this question using a delayed post-test that was carried out one year after the treatment period. We focused on the students' achievement both in the experimental (Sprego) and control (traditional surface-approaches) groups based on the methods used one year prior to the administration of the delayed post-test. The results show that students who learned the spreadsheet management topic with Sprego achieved significantly better scores on the delayed tests than those students who used low-mathability approaches.
机译:在匈牙利,K-12信息学/计算机科学教育专注于基于地表的方法。在主题的几个主题教学中可以观察到这种方法,其中我们专注于电子表格管理。通过监管文件 - 匈牙利国家核心课程和匈牙利课程框架进一步强调这一点 - 其中处理算法,调用架构和一般问题解决仅被分配给编程主题。在履行学校课程要求和各种工具为中心的考试的过程中,学生们熟悉软件界面以及如何浏览它们,而不是开发计算思维技能,并学习如何接近和解决现实世界问题。我们的教育系统是基于螺旋教学方法;因此,电子表格管理在少数课程中介绍了几个等级。先前的研究表明,学生使用表面方法方法学习电子表格管理不会建立可靠的知识结构。这些学生无法解决与他们学习主题的内容不同的语境中的问题,并且不能在不同的软件环境中使用它们的表面导航能力。我们的研究小组在教学学习过程中心的算法建设和解决问题方法上侧重于电子表格。为此目的,我们已经开发并介绍了Sprego(电子表格乐高)方法。 SPREGO基于Polya的基于四步的概念的解决问题的方法,而其效率已经被证明是与传统的低Mathability表面方法方法相比。在比较低和高空和高度的方法方法中,出现了几个进一步的问题,其中一个关键方面是不同方法如何支持在长期记忆中建立的架构构建和知识。在本文中,我们使用在治疗期后一年进行的延迟测试后讨论这个问题。我们专注于实验(SPREGO)和对照(传统地表 - 方法)基础的学生的成就,基于在延迟测试后一年的方法使用的方法。结果表明,使用SPREGO学习电子表格管理主题的学生在延迟测试中取得了明显的分数,而不是使用低MATHALY方法的学生。

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