IIAI Letters on Informatics and Interdisciplinary Research

Development of the Descriptive-Relational-Graphical (DRG) Model for Eduinformatics

Akira Nakamura — 2026-01-30

The Organization for Economic Co-operation and Development (OECD) Learning Compass 2030 envisions education as cultivating transformative competencies for navigating uncertain futures. This study proposes the Descriptive-Relational-Graphical (DRG) Model as a comprehensive framework for understanding mathematical learning through representational transformation. The DRG Model integrates three complementary modes: descriptive language for articulation through natural expression, relational language for quantitative and logical structures, and graphical language for spatial visualization. This framework emerged from data-driven education research within Eduinformatics, an interdisciplinary field integrating educational sciences with informatics methodologies. Building on Ainsworth's DeFT framework, Duval's semiotic representation theory, and Skemp's distinction between instrumental and relational understanding, this study demonstrates that mathematical learning develops through recursive movement across representational systems. Visualization functions as a central mediating pathway connecting internal cognition with external representation. Analysis of geometric examples illustrates how bidirectional transformations among descriptive, relational, and graphical representations foster relational understanding—comprehending both what to do and why. The DRG Model supports OECD Learning Compass 2030 goals by enhancing learner autonomy, metacognitive reflection, and agency in mathematics education and beyond.

Internet Fatigue as a Protective Signal in Visibility-Intensive SNS

Chie Kato — 2026-01-30

We examined “Internet Fatigue” as a “Protective Signal” in visibility-intensive social media. We analyzed 871 free-text responses from young Social Networking Sites (SNS) users in Japan (mean age = 21.9) using an embedding-assisted content analysis. Sentences were encoded with Multilingual E5 (Wang et al., 2022; 2023; 2024) and clustered with k-means. We inspected the elbow curve (k = 2–30) to delimit a candidate range, then selected k by the average silhouette within that range. Overall, 747/871 (85.8%) reported at least one fatigue experience. Seventeen interpretable clusters emerged—e.g., Alt-Account Exposure and Conformity Pressure; Problematic Use and Time Displacement; Relational Maintenance Pressure—many tied to heightened visibility and inflow. We argue that internet fatigue could function as a protective signal, prompting boundary-setting and other self-regulatory actions.

Reducing Student Hesitation through a Trial-and-Error Cyber Defense Exercise System for Security Beginners

Ichitoshi Takehara — 2026-01-30

Cyber defense exercises are important for developing cybersecurity personnel capable of re-sponding to increasingly sophisticated attacks. However, beginners often hesitate to execute commands during exercises because of anxiety about system failures. To address this issue, we developed a cyber defense exercise system that allows students to save and restore exercise states. Trial-and-Error function enables learners to retry operations safely and reflect on the results of their actions. In the evaluation experiment, students who had learned basic Linux commands were divided into two groups: one using the developed function and the other not using it. As a result, the former group tended to execute more commands than the latter. This result suggests that the proposed system reduces hesitation during operations. Interview responses also sug-gested that the system helped students feel psychologically comfortable and encouraged them to explore different defensive operations. These findings indicate that the proposed system can support learning for beginners by reducing hesitation and promoting reflective practice in cyber defense exercises.

Generative AI in University Programming Courses

Mio Tsubakimoto — 2026-01-30

This study explored the optimal design and execution of programming courses in the era of generative AI. This study was based on a survey of students enrolled in this course. Specifically, this study investigated students’ actual use of ChatGPT and their perceptions of the value of learning programming. The investigation targeted students enrolled in an applied course in the Mathematical and Data Science Minor at a comprehensive university. Subsequently, the findings were used to study the implications of the designs of examinations and assignments. The survey revealed that 80% of the respondents had experience using ChatGPT, and the purposes of use varied depending on their prior programming experience. Additionally, students were apprehensive regarding the fairness of evaluating assignments and examinations when generative AI tools are employed. In response, a series of countermeasures has been proposed, including the implementation of in-person assessments that prioritize coding aptitude and reasoning and comprehension skills. Conversely, another counterargument posits that programming education should assess the ability to use AI effectively as a practical tool.

Designing a Knowledge-Driven Team Development Framework Based on the Psychological Safety Capability Maturity Model

Yuhei Kotani — 2026-01-30

This study developed the Psychological Safety Capability Maturity Model (PS-CMM) as a knowledge-driven team development framework. PS-CMM standardizes efforts to improve psychological safety, which tend to become dependent on individual initiatives, by positioning them as team-based practices. It is structured so that teams can take a multifaceted approach to promote psychological safety. After applying the PS-CMM for four weeks to Japanese student sports teams—one type of team characterized by strong top-down decision-making and difficulties in knowledge management—psychological safety related to mental health showed a statistically significant improvement (p < .001). Qualitative analysis further indicated that team psychological safety improved and that members experienced reduced psychological barriers to communicating with coaches and core members. These findings contribute to the development of evidence-based intervention methods for improving psychological safety and propose a knowledge-driven team development framework that standardizes individual-dependent initiatives, thereby advancing knowledge management.

Reference Citation in High School Student Papers

Yuno Mikasa — 2026-01-30

Recently, the adoption of inquiry-based learning has grown in high schools, increasing student-led research activities. However, concerns remain about whether students are citing sources appropriately. This study investigates the actual state of reference citations in high school students' papers. The methodology involved collecting papers from high school students and analyzing the references in the bibliography. The survey targets were papers written by high school students from Super Science High Schools certified by Japan's Ministry of Education, Culture, Sports, Science, and Technology. The targets included citations of journal and E-journal Papers, books, web pages, and magazine articles. Findings revealed that only 4.0% of submissions included all essential bibliographic information. Citation deficiencies varied by the type of media referenced, and longitudinal comparisons indicated no significant improvement even after the introduction of new educational guidelines promoting inquiry-based learning. These results suggest that the current emphasis on inquiry does not necessarily enhance students’ citation competence. The findings imply that many high school students cite sources without fully understanding their purpose or value in academic writing. Hence, this study highlights the need to systematize practical instruction in academic writing, provide continuous feedback during the research process, and strengthen collaboration with school libraries.