The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.

该研究考虑了文本挖掘技术在高等教育机构提供的学习计划课程分析中的应用。它提出了一种使用主题建模对模块手册进行高效且可扩展的定量内容分析的新程序。所提出的方法允许收集、分析、评估和比较来自任意学科的课程，作为传统上手动进行的定性内容分析的部分自动化、可扩展的替代方案。该过程以德国的 IS 学习项目为例进行说明，该项目基于包含 90 多个项目和 3700 个不同模块的数据集。该研究的贡献解决了几个不同利益相关者的需求，并提供了对所审查研究计划之间差异和相似之处的见解。例如，结果可能有助于学术管理更新 IS 课程，并可以纳入课程设计过程。关于雇主，结果提供了对不同大学和学位实现员工技能期望的见解。未来的学生可以将结果纳入他们关于在哪里学习和学习什么的决定中，而大学赞助商可以在他们的资助过程中利用这些结果。结果提供了对不同大学和学位实现员工技能期望的见解。未来的学生可以将结果纳入他们关于在哪里学习和学习什么的决定中，而大学赞助商可以在他们的资助过程中利用这些结果。结果提供了对不同大学和学位实现员工技能期望的见解。未来的学生可以将结果纳入他们关于在哪里学习和学习什么的决定中，而大学赞助商可以在他们的资助过程中利用这些结果。