Modern society needs to think of new approaches for solving problems with computing. Computational thinking is the process of abstracting and automating a variety of problems using computational technology. A system that expresses, manages, and processes knowledge such as computational thinking is called a knowledge-based system. This paper proposes to examine students’ knowledge about computational thinking when they want to develop a Python project, and the correlation/association between these concepts. To achieve our goal, a field study was designed and data were collected from a computer programming lecture. Through this data analysis, we try to identify the factors through the correlation between data and clustering technique in order to express and discover the knowledge about the learner’s computational thinking. For the verification of the factors identified, we analyzed the correlation between computational thinking and the pre- and post-test results of the LightBot. In addition to the regression analysis of the proven factors, the probability of the research model was analyzed through the structural equation to process the knowledge discovered. In this paper, we present various problems in the domain of programming education and analyze the means to diagnose and improve knowledge based on computational thinking by finding various problem-solving methods. To pre-examine the learner; he/she was diagnosed using a test paper and the LightBot execution test. We checked the learner’s current knowledge state by analyzing the correlation between the test site and the results of the LightBot. To analyze the level of knowledge improvement of learners, we designed an experiment to analyze the correlation between learning and the actual test results through a system that applied the problem-solving learning method. An analysis of the experimental results demonstrated that there was a correlation between the test results for a learner and the pre-test results of the LightBot. Additionally, the group mean scores of the learners who learned as per the proposed technique were observed to be significant. During this process, we analyzed the effects of problem-solving and system application on academic achievement through factor analysis, regression analysis, and structural equation modeling. The ability to pinpoint various problem scenarios and solve problems more effectively using computational technologies will become more important in future. For this purpose, applying our proposed technique for deriving and improving knowledge based on computational thinking to software education will induce the interest of students and increase the learning effect.

中文翻译：

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使用知识发现计算进行计算思维的知识表示

现代社会需要考虑解决计算机问题的新方法。计算思维是使用计算技术抽象化和自动化各种问题的过程。表达，管理和处理诸如计算思维之类的知识的系统称为基于知识的系统。本文建议检查学生在要开发Python项目时对计算思维的知识，以及这些概念之间的相关性。为了实现我们的目标，设计了现场研究，并从计算机编程讲座中收集了数据。通过此数据分析，我们尝试通过数据与聚类技术之间的相关性来识别因素，以表达和发现有关学习者的计算思维的知识。为了验证所确定的因素，我们分析了计算思维与LightBot的测试前和测试后结果之间的相关性。除了对已证明因素进行回归分析外，还通过结构方程分析研究模型的可能性，以处理发现的知识。在本文中，我们提出了程序设计教育领域中的各种问题，并通过找到各种解决问题的方法来分析基于计算思想的诊断和提高知识的方法。预先检查学习者；他/她使用试纸和LightBot执行测试被诊断出。我们通过分析测试站点和LightBot结果之间的相关性，检查了学习者的当前知识状态。为了分析学习者的知识进步水平，我们设计了一个实验，通过应用问题解决学习方法的系统来分析学习与实际测试结果之间的相关性。对实验结果的分析表明，学习者的测试结果与LightBot的预测试结果之间存在相关性。另外，观察到按照所提出的技术学习的学习者的小组平均分数是显着的。在这个过程中，我们通过因子分析，回归分析和结构方程模型分析了问题解决和系统应用对学业成绩的影响。将来，使用计算技术来确定各种问题场景并更有效地解决问题的能力将变得越来越重要。以此目的，