https://www.iaiai.org/journals/index.php/IJIRM <p align="justify"><strong>International Journal of Institutional Research and Management (IJIRM)</strong> is a peer-reviewed/refereed international journal that is dedicated to the theory and practice in Institutional Research on higher education and research management. IJIRM strives to cover all aspects of working out new technologies and theories, and also case study for evidence-based institutional management, big data in universities, research administration, educational technology, and multidisciplinary topics on institutional research.</p> International Institute of Applied Informatics en-US International Journal of Institutional Research and Management 2432-3675 https://www.iaiai.org/journals/index.php/IJIRM/article/view/839 <p>A system has been established at the Kyushu Institute of Technology (Kyutech) that enables both students and teachers to reflect on their learning history and complete self-evaluations using e-portfolio systems. However, the university has not yet implemented institutional research (IR) for decision-making at the institutional level. To address this gap, the Learning Teaching Center at the university decided to establish an Educational IR Support Group in FY 2022, aiming to strengthen IR in education. In addition to e-portfolio systems, various data are accumulated across different systems within the university. However, there were no integrated systems to utilize these data comprehensively, as various departments managed them individually. To overcome this, we introduced an extract transform load (ETL) tool as part of the initial IR system. Simultaneously, we implemented a data lake system as a centralized repository for storing data. The Educational IR Support Group is leading this project. This study begins by providing an overview of IR and how it should be conducted. It then presents the IR system being promoted at our university, including the introduction of the ETL tool and the establishment of a data lake. Finally, we describe our future plans and developments, which involve the potential introduction of a data warehouse and business intelligence tools.</p> Tetsuya Oishi Hirofumi Fukumaru Akihiro Hayashi Hiroshi Sakamoto Copyright (c) 2024 International Journal of Institutional Research and Management 2024-03-10 2024-03-10 8 1 10.52731/ijirm.v8.i1.839 https://www.iaiai.org/journals/index.php/IJIRM/article/view/825 <p>This study provides results from a survey on enrollment projections, methods, metrics, timing, and model among public 2-year and 4-year higher education institutions in the United States. The data are from 127 public, 4-year and 73 public, 2-year institutions surveyed in spring and summer 2021. The results are summarized on various aspects of the process for developing enrollment projection numbers from the factors considered, the type of enrollment models used, methods and modeling techniques implemented, and the involvement of campus offices. These findings will help provide details on current enrollment models, methods and modeling techniques implemented, and campus offices' involvement in enrollment projections in higher education institutions. The study reveals, there is no vast difference in how public, 4-year and public, 2-year institutions oversee enrollment projections. Almost all institutions build and develop their enrollment models in-house. The most widely used software for modeling and presenting enrollment projections is Microsoft (MS) Excel. The top three modeling techniques implemented in enrollment projection are Time series models, Markov chain models, and Linear regression models. Multiple offices in the institutions participate in the process of producing enrollment projection numbers.</p> Emma Gyasi Felix Famoye Carl Lee Robert Roe Copyright (c) 2024 International Journal of Institutional Research and Management 2024-02-06 2024-02-06 8 1 10.52731/ijirm.v8.i1.825