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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The use of data warehouses combined with online analytical processing (OLAP) platforms has become popular in Taiwan’s insurance market. However, most schools do not have an insurance data warehouse and OLAP platform for student learning in Taiwan. The researched courses are insurance information system courses for two university classes. Based on the teacher’s experience and innovativeness, those courses are integrated using the guided project-based learning approach. Students need to build a customer micro-database, analyze customer figures through pivot analysis charts, and plan marketing campaigns. The study finds a project-based learning approach is helpful to enhance students’ OLAP analysis abilities. Secondly, the research finds that the flexibility for students to choose the topic of their project is one of the key success factors. Thirdly, the evaluation share of the student’s learning scores is important for the completion of the project. Fourthly, the courses are accompanied with satisfaction questionnaires to monitor the learning results and analyze the learning satisfaction for students among course A, course B, and the college average. Those students in the two classes both have higher satisfaction scores than the college average, but there are still differences between the classes after the t-test.

Details

Title
The Project-Based Learning Study of Insurance Information Courses to Simulate the Application of Online Analytical Processing
Author
Yung-Cheng, Liao; Mei-Su, Chen
First page
47
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
25715577
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2806463550
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.