| dc.description.abstract | STEM programming education has become a required course in domestic universities, and problem-solving instructional methods are suitable for cultivating students′ computational thinking ability. This study integrates chatbot-assisted instructional systems and problem-solving instructional methods into the STEM programming course to enhance learning performance, and we investigate the differences in students′ learning effectiveness, perceptions, and behaviors.
The experimental subjects are college students in Northern Taiwan, and the theme of the experiment is the STEM programming course. After the experiment, we collected data from tests, questionnaires, interviews, and system logs. According to the pre-test, the students were divided into three groups to compare the differences in learning effectiveness and analyzed the correlation between learning perceptions and behaviors. Next, we analyze the learning perceptions and learning behaviors using machine learning techniques to compare the results of verifying answers.
Results show that most students have a certain degree of background knowledge in STEM programming. Moreover, some students focus on practical abilities and do not care about theoretical knowledge, so their programming performance is limited. The cognitive process of low and high prior knowledge groups is similar. In the learning perception questionnaire, the effort, organization, and reference dimensions of students are significantly correlated with students′ learning behaviors. The high effort score of students can repeatedly improve the programming questions and submit the answer several times for validation. The high organization score of students can try different solutions, and the high reference score of students can get better programming performance in quizzes. In this study, the SVM performs the best in all machine learning algorithms, and the F1 score of SVM is 0.758. After classifying the highly correlated features on the learning perception questionnaire, the F1 score of SVM is 0.74. This finding indicated that the ideal dimension reduction is realized by removing low correlated features on learning perceptions. Finally, we find the best educational data mining approach in STEM programming courses. | en_US |