case library
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Author(s):  
Yi Liang ◽  
Bingna Lou

The business management models are updated constantly, making it urgent to reform the teaching methods of business management courses (BMCs). Case-based teaching (CBT) is an important way to integrate the teaching and practice of business management. The construction and management of CBT resources has attracted the attention of scholars at home and abroad. This paper designed an information management system for the BMC case library. Firstly, the goals and content were summarized for the construction of the case library and the information management system. Next, the authors designed the BMC case library forms, as well as the function modules of the information management system. There are three core functions in the system: retrieval, cluster analysis, and course case allocation. At last, experimental results verified the effectiveness of the proposed case clustering algorithm and the good test performance of the established system.



2021 ◽  
pp. e1-e84
Author(s):  
Houda Bahig ◽  
G. Brandon Gunn ◽  
Steven J. Frank


Author(s):  
Shunye Wang ◽  
Dayong Liu ◽  
Ning Wang ◽  
Yanxiang Yuan

Based on the features of Python and the situation of case-based teaching, this paper develops and implements a complete online Python teaching case library. The teaching contents of a Python programming were divided into ten parts: basic knowledge, three basic structures, functions and modules, object-oriented thinking, file and database programming, network and multithreading programming, image programming, web crawling, as well as data analysis and visualization. Each part was provided with several basic teaching cases. Following the idea of software engineering, our case library was designed based on ChaoXing, a famous online learning platform in China. The designed case library offers a clear path from data acquisition, data storage, data processing, to data visualization. It also provides a wealth of up-to-date and extensible contents, enabling learners to apply the knowledge to practice. Through application of the proposed case library, the students all achieved excellent performance, published innovative academic papers, and selected suitable topics for graduation thesis. The library also greatly promotes the abilities of students in computational thinking, programming level, and solving complex engineering problems.



2020 ◽  
Vol 78 (3) ◽  
pp. 394-409
Author(s):  
Jongchan Park ◽  
Jinju Lee ◽  
Dongsik Kim

Researchers have suggested that case library learning is an effective instructional method that promotes argumentation skills for ill-structured problem-solving. However, little research has examined scaffolding strategies to implement effective case library learning. The aim of this research was to examine the effects of indexing prompts as scaffolds on argumentation skills during problem-solving in case library learning. A quasi-experimental design was employed for the research. A total of 111 first-year students in a university across three sections in a career development course participated in the experiment. Participants were provided with three different indexing prompts: explanation-based indexing prompts (focused on self-explaining anomalies of individual cases), difference-based indexing prompts (focused on self-explaining similarities and differences between cases), and no prompts (allowing spontaneous self-explanations when studying cases). Learners’ argumentation essays comprising initial arguments, counterarguments, and rebuttals were collected. The results found that learners who were prompted to generate mental indices outperformed the learners in the control group when constructing argumentation. More specifically, explanation-based indices were beneficial in making counterarguments and rebuttals, and difference-based indices were helpful when making rebuttals. These findings suggest that indexing cases based on both exploration of the anomalies of each case and comparisons between cases improves argumentation by facilitating case retention, retrieval, and reuse. Keywords: argumentation skills, case library learning, case-based reasoning, indexing prompts.



2020 ◽  
Vol 36 (5) ◽  
pp. 656-671 ◽  
Author(s):  
Jongchan Park ◽  
Chaeyeon Park ◽  
Hyojung Jung ◽  
Dongsik Kim






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