IIAI Letters on Institutional Research

Eduinformatics and the Universities’ Challenge for “Ri”

2025-01-20T01:18:38+00:00

This paper examines the transformation of higher education through the lens of Eduinformatics and the Japanese concept Shu-Ha-Ri. It analyzes the current state of universities, characterized by rigid academic divisions and external pressures, and proposes a path through interdisciplinary fusion and the introduction of absolute perspectives. The paper then envisions a future state 100 years from now that transcends constraints of time, place, and culture. It argues that a "Ri" part of universities creates new values through the fusion of Japanese cultural elements with global diversity by maximizing creativity and sensibility, and actively engages with society to solve real-world problems. The study concludes thatwhile the path to transformation is not unimpeded, it is essential for the future relevance and impact of higher education.

Categorical Database for Ensuring Data Integrity in Institutional Research

2025-01-10T02:56:55+00:00

Institutional research deals with large and different datasets from various departments, and this can make it hard to keep the data accurate. In this paper, we present categorical databases, which is a method based on category theory, that helps to maintain data integrity. By organizing the database as a category, we can see how the data elements are connected. This makes it easier to do meaningful and precise data analysis. The connections between data can be shown as simple sentences that still make sense, even when the data is updated. This way ensures that data remain consistent in both databases and data warehouses through natural transformations, which means that references to the data stay trustworthy. Categorical databases provide a solid way to manage complex data structures, and they make sure that data integrity is kept.

A Model for Understanding Student Status Using Attendance Data in the First Semester of University

2024-12-23T05:27:54+00:00

This study developed a Hidden Markov Model (HMM) to analyze attendance behaviors of first-year university students during their spring semester, aiming to identify distinct behavioral patterns and examine their impacts. Weekly attendance data was used to estimate latent states, and clustering revealed four representative attendance patterns, including stable attendance and increased absenteeism. The results highlight the potential impact of specific behaviors on academic outcomes, underscoring the importance of preventive interventions in student support and its applicability to future academic guidance.

Clinical Reasoning and Decision Making through Knowledge Networks and Abduction

2025-01-26T09:43:43+00:00

This study examines the integration of clinical decision making processes with knowledge networks and abductive reasoning in nursing practice, proposing a sustainable framework based on eduinformatics. While clinical reasoning traditionally relies on deductive and inductive approaches, the complexity of modern healthcare demands more sophisticated decision-making methodologies. Through analysis of clinical cases and reasoning patterns, we demonstrate how abductive reasoning complements traditional approaches, particularly in situations where complete information is unavailable. The knowledge network theory provides a structured framework for understanding how clinical knowledge is created, shared, and applied. By integrating these elements through eduinformatics, we develop a comprehensive approach that enhances clinical reasoning capabilities in nursing education and practice. This framework offers a systematic way to improve clinical decision-making while maintaining sustainability in increasingly complex healthcare environments.

Investigation of Latent Effects and Changes of Adult Learners at Colleges or Graduate Schools

2025-01-10T09:16:17+00:00

In contemporary society, where radical changes of social structures are being taking place, it is getting more important for adult learners to study to obtain new knowledge and skills. From these circumstances, what triggers adult learners to study and what prevents them from learning are greatly emphasized. There are several forms of study motivation: getting a certification, seeking new career paths, or simply academic interest, among others. However, Japan is behind other countries when it comes to adult relearning due to various hurdles along the way. Meanwhile, recurrent education is attracting attention along with the widespread use of various education methods to handle multiple demands. Therefore, this paper aims to analyze the potential factor of study motivation of adult learners and to grasp what motivates or prevents their relearning. As a first phase, we formulate four hypotheses regarding the relationships between study motivation and achievement. These hypotheses are then validated using questionnaire targeting adult learners. As a result of analysis, the relationships between before and after studying at college or graduate school can be observed. It could also be implied that the questionnaire targeting adult learners who completed college or graduate school would be effective in examining effects and changes.

The Practical Application of Cluster Analysis of Aca-demic Fields in Bibliometric Information to Enhance Research Performance Evaluation

2025-01-09T09:20:09+00:00

In recent years, the research capabilities of Japanese universities have declined compared to other countries, highlighting the need for effective evaluation of research performance. Due to the diversity of academic disciplines and the rise of interdisciplinary research, identifying comparable researchers presents a challenge. Therefore, this paper presents a method for identifying comparable researchers by classifying research fields through the application of bibliometrics. Specifically, using the Scopus database, this study conducted cluster analysis on the research topic profiles of researchers from multiple universities within the same field to group them. Additionally, this paper demonstrates how the results of cluster analysis can be applied to enhance the evaluation of research performance.