International Journal of Institutional Research and Management

Effects and Limitations of University Information Disclosure: A Study on the Impact on Prospective Students' University Choice

Nozomi Yoshida, Rie Mori — Sun, 30 Mar 2025 11:22:12 +0000

It is important for universities to disclosure sufficient information for educational outcomes and quality to prevent prospective students from being disappointed after they start. However, the disclosure of the aforementioned information, which are strongly demanded by the "Educational Management Guidelines," is not yet sufficient. In order to improve the disclosure of university information, this study aims to clarify the impact of educational information on university choices of prospective students. We conducted a questionnaire survey targeting university students to examine whether the information required to be disclosed by universities was perceived at the time of university selection and to what extent this information was helpful in making their university choices. Results showed that the perception of university educational information was overall low, but for the information that was perceived, it was generally shown to help make career choices. Besides, differences in respondents' attributes such as post-graduation career aspirations, academic fields at the university, and entrance exam formats also showed variations in perception and usefulness. These results imply the value of the information provided for prospective students to choose the college and the major academic field.

A Mathematical Theory to Evaluate Disruptive Changes Deductively

Daisuke Ikeda, Kun Qian, Kengo Nawata — Mon, 24 Mar 2025 12:25:48 +0000

Digital transformation, known as DX, is gaining a lot of attention in a variety of organizations, and higher education institutions are experiencing such rapid changes. IR sections in them, therefore, have to support and evaluate their trials for such rapid changes. Although a popular approach to evaluating trials is to use data about them, in the case of trials aiming for disruptive changes, it is important to evaluate them beforehand since such a trial can be too costly and its impact can be serious damage to institutions. However, it is intrinsically difficult to deal with such changes since we need not have enough data beforehand. This paper is devoted to developing a framework, which can be used to evaluate such disruptive changes. The main idea for the framework is that our daily behaviors are defined as a theory from the perspective of information dissemination. Using the proposed framework, we derive some findings deductively, which are not obvious from existing approaches. Thus we can conclude that the proposed framework is fruitful. These findings include that in- formation dissemination is derived to create disruptive changes, although our basic notions do not include being disruptive. We can also show that dissemination can cause a succes- sive cascade and the proposed model can explain the reason why resistance to new changes occurs. These findings can not be obtained from existing defitions for disruptive changes, such as DX. The main contribution of this paper is to show a deductive approach, which is not popular in IR, is effective in evaluating such disruptive changes.

A Survey on Self-Perception of Institutional Research Skills and Knowledge: Results from Training Courses in 2023-2024

Tetsuya Oishi, Takashi Nishide — Tue, 18 Feb 2025 13:43:53 +0000

We conducted a series of studies from 2021 to 2024 to investigate the needs and effectiveness of institutional research (IR) training courses in Japanese higher education.
Initial investigations through web questionnaires (n=189) in 2021 and individual surveys in 2022 revealed a need for step-by-step training sessions based on participants' abilities.
Based on these findings, we implemented training courses in 2023 (n=20) and 2024 (n=29) to examine essential IR training requirements and participants' self-perceptions of their skills.
The 2024 course incorporated improvements including broader participant eligibility and more detailed attribute data collection.
This paper presents comprehensive results from these training courses, analyzing how participants' self-perception of IR skills changed through the sessions, with particular attention to differences between faculty and staff members, as well as variations based on years of IR experience.
Our findings suggest that role-specific training approaches may be more effective, as faculty and staff showed different patterns in skill self-assessment and learning preferences.

The Implementation and Systematic Reconstruction of the I-E-O-L Model for Streamlining and Advancing Student Surveys

Sun, 30 Mar 2025 11:41:31 +0000

This study proposes the I-E-O-L model, an extension of Astin’s Input-Environment-Output (I-E-O) model, as a comprehensive framework for managing student surveys. The I-E-O-L model introduces the "Life Career (L)" component to incorporate postgraduation data, enabling a holistic evaluation of student growth and long-term educational impacts. This model aligns with the Ministry of Education, Culture, Sports, Science and Technology’s (MEXT) Guidelines for Academic Management, emphasizing its relevance to higher education policy in Japan. Three case studies illustrate the utility of the model: 1) visualizing survey implementation promotes shared understanding and collaboration among stakeholders, 2) aligning survey frameworks with MEXT guidelines enhances institutional research (IR) activities, and 3) systematic reviews and “inventorying” of fragmented surveys reduce redundancy and improve survey reliability and validity. These practices collectively streamline survey operations, reduce respondent fatigue, and enable data-driven educational improvements. While the I-E-O-L model shows significant potential for application across diverse educational contexts, its adaptability extends beyond Japan, as supported by international research and practice. By leveraging this model, institutions can enhance survey efficiency, obtain deeper insights into student outcomes, and foster educational quality. These findings highlight the model’s effectiveness in improving survey-based decision-making in higher education.