#Japan #Nagoya #Harmony Plus #Daido University #Educational Data

Introduction

Daido University, located in Nagoya, Aichi Prefecture, has taken a significant step towards enhancing the quality of education and student support systems by implementing an advanced analytics feature within their learning outcome visualization system known as "Learning Outcome MOE." This new capability is designed to elevate analytical practices by providing intuitive dashboards for more efficient decision-making processes.

The Need for Advanced Data Analytics

In recent years, Daido University has faced a growing need to address diverse educational challenges and improve the speed of hypothesis testing in data analysis. The institution has recognized several key issues that have emerged within the academic community, including:

• - The individualization and inefficiency of data collection and processing in Institutional Research (IR) activities.
• - The challenges associated with analyzing trends in student dropouts and developing preventative measures.
• - The pressing need for visualizing the correlation between academic performance and employment results.
• - The substantial workload involved in preparing materials for external evaluation committees and high school briefings.
• - The dispersion of data across various departments, impeding effective utilization of information.

To tackle these challenges, Daido University decided to standardize the analytical environment and introduced the "Advanced Analytics Feature" to facilitate flexible hypothesis testing.

Overview of the Advanced Analytics Feature

The new feature introduced at Daido University boasts several unique attributes:

• - External Support for Data Preparation: To prevent the individualization of analytical tasks, preprocessing and integration of data are managed by Harmony Plus Co., Ltd., enabling users to focus on hypothesis formulation and data analysis.
• - Intuitive Dashboard Operations: Users can easily navigate the system with drag-and-drop functionality and flexible options for labeling and color adjustments in visual representations.
• - Versatile Output for Analyses: Reports can be exported in PDF or PPT formats, making them suitable for various applications such as IR meetings, high school presentations, and publishing in brochures.
• - Continuous Support and Collaboration: Post-implementation, regular meetings are scheduled to provide ongoing support for hypothesis construction and new graphing requests.

Expected Outcomes

With the introduction of this advanced functionality, Daido University anticipates several positive outcomes:

• - Automation of routine annual analysis, significantly reducing workload.
• - Establishing foundational analyses that minimize efforts for advanced evaluations.
• - Enhanced speed in hypothesis testing within the IR department.
• - Improved persuasive power in internal reports through visually comparative graph creation.
• - The ability to communicate the university's distinctive qualities numerically in student recruitment efforts.

Future plans include:

• - Analyzing trends among dropouts to devise preventive strategies.
• - Understanding the characteristics of high-achieving students to enhance educational frameworks.
• - Developing foundational analyses to reassess entrance selection systems.

Remarks from University Officials

Hidetaka Tanahashi, Vice President of Daido University, expressed the following: "The comprehensive support for data structuring, analysis, and visualization has significantly broadened our IR activities. Moving forward, we aim to conduct analyses based on our unique hypotheses and strategies, contributing to enhanced student support and educational improvements. We view the introduction of the 'Advanced Analytics Feature' as an opportunity to strengthen strategic decision-making fueled by analysis outcomes. Furthermore, we are assessing mechanisms for early detection of dropout risks and improved data visualization linked to diploma supplements. Our ongoing efforts will focus on enhancing educational quality and student satisfaction."

About Learning Outcome MOE

The Learning Outcome MOE system is crafted to visualize learning outcomes quantitatively and structurally, allowing for organized explanations of these results. This analytical framework facilitates not only the categorization and external communication of academic achievements but also innovative strategies for utilizing the information internally.

For more information on the Learning Outcome MOE, please visit: Harmony Plus.

Contact Information

For inquiries regarding this initiative:

• - Harmony Plus Co., Ltd.
• - Address: 7-5 Nibancho, Chiyoda Ward, Tokyo
• - Phone: 03-6261-5172
• - Contact: Harmony Plus Contact