Lessons from Continuing Vocational Training Courses for Computer Science Education

Jens Dörpinghaus, Johanna Binnewitt, Kristine Hein
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Abstract

The labor market heavily relies on both vocational and academic education and training, re-training and advanced vocational qualification to meet challenges, e.g. the advancing digitalization[2, 3]. Continuing education is a central prerequisite for securing skilled labor, for ensuring the employability of all employees and thus also for national competitiveness and innovation. From the perspective of education and labor market research, several approaches discuss how the impact of computer science education can be evaluated. Other research focuses on the needs of the labor market, by analysing job advertisements. In order to broaden the perspective on the entire range of CVET courses and to be able to gain new insights from this, our analysis is intended to provide an initial overview of the content of CVET courses in Germany. By that, we offer structured information on skills and competencies that are included in current CVET courses. In future research, this information can be compared to labor market needs, e.g. described in job advertisements, in order to identify education gaps. Since CVET courses are often described in unstructured natural language, text mining-methods are key to extract information on skills and competencies. Here, we present an analysis of 84,310 advertisements for CVET courses from 2023 that are divided into 83 different computer science (CS) related categories.
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计算机科学教育继续职业训练课程的经验教训
劳动力市场严重依赖职业和学术教育与培训、再培训和高级职业资格来应对挑战,例如不断推进的数字化[2,3]。继续教育是确保熟练劳动力、确保所有雇员的就业能力,从而也是国家竞争力和创新的核心先决条件。从教育和劳动力市场研究的角度来看,有几种方法讨论了如何评估计算机科学教育的影响。其他研究则通过分析招聘广告来关注劳动力市场的需求。为了扩大对整个CVET课程范围的视野,并能够从中获得新的见解,我们的分析旨在提供德国CVET课程内容的初步概述。通过这种方式,我们提供当前CVET课程中包含的技能和能力的结构化信息。在未来的研究中,这些信息可以与劳动力市场需求进行比较,例如在招聘广告中描述,以确定教育差距。由于CVET课程通常以非结构化的自然语言描述,文本挖掘方法是提取技能和能力信息的关键。在这里,我们对2023年以来的84,310个CVET课程广告进行了分析,这些广告被分为83个不同的计算机科学(CS)相关类别。
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