Jens Dörpinghaus, Johanna Binnewitt, Kristine Hein
{"title":"计算机科学教育继续职业训练课程的经验教训","authors":"Jens Dörpinghaus, Johanna Binnewitt, Kristine Hein","doi":"10.1145/3587103.3594169","DOIUrl":null,"url":null,"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.","PeriodicalId":366365,"journal":{"name":"Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lessons from Continuing Vocational Training Courses for Computer Science Education\",\"authors\":\"Jens Dörpinghaus, Johanna Binnewitt, Kristine Hein\",\"doi\":\"10.1145/3587103.3594169\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":366365,\"journal\":{\"name\":\"Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3587103.3594169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3587103.3594169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lessons from Continuing Vocational Training Courses for Computer Science Education
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.