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The Diagnosis of Programming Misconceptions Based on Data Mining
Most of the research related to the concept of the misconception programming in the past faced the same limitations, including too few research participants, only locking specific programming languages in research, and not being able to apply the results to a wide range of people. Most of these restrictions are caused by research methods. Most of the past research is based on the self-judgment of researchers as the basis for the research results of misconception. Therefore, it takes a considerable amount of time to organize and analyze the data. Therefore, the scope of the research data is limited. Most of the restrictions on the masses occur, and most of the programming tests or tools used for diagnosis only develop into specific programming languages, which limits the scope of application of the research results. Therefore, this study is different from the past research methods and uses data mining in the program. Programming misconception of data mining is to use algorithms to find similar clusters between feature vectors, and to find out the misconception types and misconception symptoms. Through interviews, the correctness of the misconception data mining results is confirmed, and the programming learning platform for the misconception diagnosis is developed. The results of the study are based on the three main research objectives, including the narrative of the misconception of programming and the corresponding symptoms of the program, as well as the diagnostic mechanism of the development of the minconception data mining, and pointing out the relationship between misconceptions. The misconception of programming is a topic of education that has been discussed for a long time. The misconception does cause learning problems in the programming to learners. This study is based on the established misconception, adding the factors of data mining, and obtaining research. In the design of misconception and the research unit is locked into process control. The results can be applied to a wide range of programming languages. Teachers and learners have substantial support in teaching and learning. They can obtain more appropriate teaching guidelines and learning corrections to enhance the effectiveness of programming learning.
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