An educator may utilize various pedagogies such as hands-on practice, practicum, and project-based learning to enhance a student's competency. Among various pedagogies, project-based learning is one of the well-known pedagogies that may provide similar on-the-job experience. In general, an educator may divide the students into small groups and assign tasks to check students' cooperation skills and achievements during project-based learning. However, an educator may experience difficulties operating project-based learning due to the COVID-19 pandemic. Most of the team activities are done in non-face-to-face meetings, and as a consequence, the educator may not find an underachieving team easily and cannot intervene appropriately. This study introduces a rigorous analysis method to evaluate team activities to analyze individual students' participation and contributions. First, this study develops evaluation rubrics by conducting questionnaires to professors and students to find an appropriate weight value for the evaluation scale. Then, this research introduces an analysis environment to evaluate students automatically. The analysis environment collects dialog data from social network services and measures interactions among students in a team. After measuring interactions, the environment generates a report to visualize the team activities. We applied the proposed method and environment to the capstone design course to show the effectiveness of the method and environment. Based on the case study, the environment showed that the analysis method could easily indicate the teams' activities and check the level of participation.

The purpose of this study is to propose a strategy model for engineering education for citizen through the connection between colleges of science and engineering and science museums as a way to achieve citizen science. For this model, the role of universities was redefined as social contributions through engineering education from the perspective of knowledge triangle and university entrepreneurship. In addition, the science museum was re-examined as an engineering education platform and selected as an institution that supports the contribution of colleges to society. For practical model development, the connection types of these two institutions were analyzed as case studies and interview to collect opinions from experts in the science museum. In this process, convergence education content development, reinforcement of college-science museum linkage, infrastructure construction, development of college resource utilization plans, and maintenance and expansion of educational programs diversification were derived as components for model development. Based on this, engineering education model for citizen was presented that matches educational programs according to the type of participation of colleges including key factors and considerations.

Students' questions are essential and important for learning, but previous research and experience of instructors shows that there is a lack of interaction between instructors and students in online classes. This research studies how learners can effectively ask questions in online classes at engineering colleges. Based on two axes of anonymity and openness, the four different types of questioning methods were suggested as 'onymous/public', 'onymous/private', 'anonymous/public' and 'anonymous/private.' In this study, seven communication channels were applied to check their effectiveness in an online class. The results showed that learners' satisfaction with learning outcomes increased compared to previous offline classes, while satisfaction with teaching methods was similar. Additionally, among the four types of questioning methods, the preference and effectiveness of 'anonymous/public' was highest, followed by 'onymous/private'. This study suggests several implications of educational approaches to online education in engineering colleges.

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.