结合语义波与算法思维提升学生信息学教育能力

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
Frauke Ritter, Bernhard Standl
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引用次数: 4

摘要

我们生活在数字时代,尤其是COVID-19大流行加速了这一时代。在我们的社会中,学生学习和掌握算法思维的关键能力,以理解每一个数字现象背后的信息学概念,从而能够积极地塑造未来,这一点尤为重要。为了取得成功,必须确定能够向所有学生传达这一关键能力的概念,以使算法思维融入信息学主题-而不仅仅是纯粹的编程课程。此外,基于合法性代码理论,语义波提供了一种制定和审查教案的方法。因此,我们计划了一个研讨会,该研讨会遵循语义波的各个阶段,使用基于块的编程语言解决算法问题。考虑到这一点,我们提出了所谓的SWAT概念(语义波算法思维概念),并在与学生的研讨会中进行了分析。该研讨会是在冠状病毒封锁期间在一所高中的八年级以在线形式进行的。算法思维的水平是通过治疗组和对照组的前测和后测来测量的,并借助近似调整的分数贝叶斯因子来统计地测试信息假设,并通过对学生的工作结果(工作表和创建的程序)进行简化的定性内容分析来评估。语义波概念是在研讨会期间使用学生的几个认知负荷评分来测量的,并且还使用近似调整的分数贝叶斯因子进行统计评估,以测试信息假设,以及对工作表进行定性内容分析。该初步研究的结果首次表明,swat概念可以在未插拔和插拔部件的组合中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Promoting Student Competencies in Informatics Education by Combining Semantic Waves and Algorithmic Thinkingv
We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics - beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a blockbased programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students’ work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.
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来源期刊
Informatics in Education
Informatics in Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.10
自引率
3.70%
发文量
20
审稿时长
20 weeks
期刊介绍: INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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