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PurposeThe purpose of this study is to investigate the effects of the Perceptions of coronavirus 2019 (COVID-19) questionnaire and the Information System Success Questionnaire on students when using the school's COVID-19 epidemic prevention website. The study is aimed toward analyzing both questionnaires as well as evaluating an epidemic prevention website.Design/methodology/approachThe school's COVID-19 prevention website and two questionnaires (Perceptions of COVID-19 and the Information System Success Questionnaire) are examined in order to investigate 73 students' COVID-19 perceptions. An open-ended question was used as the qualitative data to support quantitative data and evaluate a university's COVID-19 epidemic prevention website from a southern university in Taiwan.FindingsThe findings indicated that most students evaluated the school's COVID-19 website positively and were satisfied. In the open-ended questions, the majority of students rated the quality of the system positively and the need to fix some defects. Students have different COVID-19 perceptions and social distance compliance based on their current situations.Practical implicationsThis study provides researchers and website developers a broader understanding of the construction of the school's COVID-19 prevention website and a better understanding of student's COVID-19 perceptions.Originality/valueTo the best of the authors' knowledge, this is the first study examining a school's epidemic prevention website, which is measured by the Information Success Questionnaire and the Perceptions of COVID-19 Questionnaire for college students.

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Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence (AI) to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students (55 men, 10 women) in the Science (Sci)-student group and 39 students (20 men, 19 women) in the Humanities (Hum)-student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI.

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