Japanese University Students Usage of Global Networks During the Pandemic

This research study provides a case study of how the COVID-19 pandemic affected Japanese university students' views and attitudes on international topics and motivations for living and working abroad. During the pandemic, digital information, including social media, played a promi-nent role in how these students interacted and kept up with the news. We surveyed 204 students at a private university in Tokyo to help under-stand how the landscape of internationalization changed during the unique circumstances brought about by the pandemic. © 2023 James Nicholas Publishers.

Cultivating Future Competencies Through Interdisciplinary Education in the Society 5.0 Era

In today's technologically advanced societies, advanced computing, communications, and information handling technologies have become thoroughly integrated into our everyday lives to the point where it is difficult to imagine otherwise. The Japanese government envisions the next societal revolution as Society 5.0, an environment where advanced technologies and service platforms are fully integrated to empower individuals in all sectors of a human-based society. This study will examine the societal changes being envisioned and how they pose new challenges for individuals in the workforce and education systems. With the shift toward knowledge-based societies, we must consider how to best utilize new technologies in the context of education and the potential benefits that can be gained. Many higher education programs are undertaking reform to prepare and cultivate students with the skills and competencies to be global leaders who can harness new technologies and leading-edge concepts for the benefit of society. Especially given the circumstances of the coronavirus disease 2019 (COVID-19) pandemic, there are many new developments issues in education, first and foremost being the shift toward online remote learning. This study will examine how can educators and students adapt to this learning style, and how diversified and globalized perspectives and critical thinking skills can be gained through technology. Through the collected voices of faculty and students, we aim to share findings obtained during pandemic era online teaching, about what we learned is possible and the challenges ahead. Based on the results of research, I argue that required competencies and skillsets are changing and that interdisciplinary education can be utilized as an effective response to these demands. Reflecting on pre- and post-COVID-19 contexts, this chapter will illustrate the challenges and changes necessary for future higher education goals and models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Introduction: Assessing Change in Asia-Pacific Higher Education on the Threshold of the Fourth Industrial Revolution: Facing the Challenges of the COVID-19 Era

The 11 substantive chapters that constitute this volume explore a range of activities taking place within an array of higher education institutions, primarily in Japan, but with attention to some of its dimensions and implications for the overall conduct of higher education at the university level. Together they suggest how the Fourth Industrial Revolution and what in Japan has been framed as Society 5.0 are affecting the existing structures of higher education and promoting new modalities for instruction, research, and community service. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Encouraging Crowd Avoidance Behavior using Dynamic Pricing Framework Towards Preventing the Spread of COVID-19

In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.