DSIR-SS 2: Data Science of Research and Postgraduate Education
DSIR-SS 1: Delineating Institutional Performance of a Research University towards Future Institutional Design
Special Session Organizer: Keigo Imai (Kyoto Univ., Japan), Ayako Fujieda (Kyoto Univ., Japan) and Keisuke Honda (The Institute of Statistical Mathematics, Japan)
Abstract: This session mainly focuses on how one can delineate the institutional performance of a research university using research-related metrics for the institution’s future design. Every research university needs a new revolution based on its results.
In order to ensure optimal performance and to enhance the potential of a research university, a comprehensive and accurate understanding of the current status of research universities is required. However, this is also a significant challenge. Especially in the case of a large-scale research university, there are many cross sections that one can capture via research-related metrics in order to determine its current status. Combinations of such cross sections for analyzing and monitoring the institutional performance will depend on the culture and the vision of each university. At this time, there are not a significant amount of global data resources for analysis related to research universities worldwide. One of the big challenges for analysts responsible for developing new strategies for a research university is to draw up and establish its scheme with structures of data maintenance along their own vision. There does not exist a universal solution that can be adapted to all!
situations of research universities; and its system will always change depending on the social context and political situation at the time.
Since the above challenge is undoubtedly one of the long-standing subjects in the context of university research administration, research universities have already been accumulating knowledge related to their future direction. Such a challenge itself does not change even at present, but one can say that the increase in some remarkable technologies might be useful for our purpose, especially in the field of data science. Various recent updates and further directions for tackling our challenge, sometimes with the help of data science, will be discussed.
In addition, concerning the research-related metrics, topics in this session include various kinds of metrics in the global context; typical examples are well-known bibliometrics, financial metrics, educational metrics, etc. We also focus on a metric that does not necessarily taking values in numbers. Our aim is how the usage of a metric actually contributes to developing new strategies in various contexts along own vision of a research university. We do not concern ourselves about whether the metric is old or new.
DSIR-SS 2: Data Science of Research and Postgraduate Education
Special Session Organizer: Michiyo Shimamura (Hokkaido Univ., Japan)
Abstract: The education scenario of graduate education has been changed significantly in the last four decades because of the globalization.
In the present “Knowledge-based Society”, the role of the universities becomes more important than ever with respect to the development and promotion of the society, economy and culture. And therefore, huge social resources have been spent to maintain the university education system. The universities get” Evaluation” by the society based on its results and contributions, namely “Quality” of “Research” and “Education”. Each university owe this duty called “Accountability”, and the evaluation system of this American model is gaining momentum in the last decade which is symbolized by the “World University Rankings” system. These rankings eventually have a big influence on the education and research impact for each country.
The graduate schools are considered to be the founding platform to provide highly specialized and research-oriented education based on the undergraduate-education for the students. Its significance in the society is continuously expanding in today’s world as a “Hot Spot of Knowledge Creation”. This knowledge creation leads to subdivision or creation of new discipline by the fusion of the research fields, and these phenomena complicate a uniform research evaluation fitting to all disciplines. Accordingly, the graduate school education seems to have a big difference between different disciplines, which affects the evaluation of efficiency and this makes it difficult to know the effect of the education beyond one’s discipline. Furthermore, the universal abilities such as the generic skill are not targeted for an evaluation or assessment. For example, in Japan, Ph. D holders who have low generic skill-high specialty become a problem in the compatibility with the labor market.
In this special session, I would like to have the “Evidence Based” discussion about Graduate School and its Research and Education. Also I hope to make this opportunity as a place to begin for graduate education improvement.
Topics of interest include, but are not limited to:
A) Management on Post-Graduate Education including learning outcomes and research outcomes, and its Data Science
– Educational Big-Data Science
– Investigation of Performance Indicators
– Analysis of Learning and Research Outcomes
– Enroll Management
B) Evaluation of Graduate Student’s Research Activities and its Data Science
– Monitoring of Student’s Research Project
– Assessment of field work, laboratory work, presentation and paper writing
– Activity as a Research and/or Teaching Assistant
C) Quality Assurance on Post-Graduate Education
– Faculty and Staff Development
– International and Inter-disciplinary Recognition of Quality Assurance
– Assessment of Learning Outcomes
– Monitoring for Improvement of Graduate Education
– Graduate Educational Reform
D) Practical Use of Resources in Graduate School
– Usage of Social Network among Faculties, University Staffs and Graduate Students
– Historical Changes of Graduate Education
– Potential of the Specialist Alumni Network
– Utilization of ICT Tools