{"title":"客观地衡量信息技术辅助培训课程的学习效果","authors":"Gerald Schneikart, W. Mayrhofer","doi":"10.1108/ijilt-04-2022-0086","DOIUrl":null,"url":null,"abstract":"PurposeThe objective of the presented pilot study was to test the applicability of a metric to specifically measure performance improvement via a hands-on workshop about collaborative robotics.Design/methodology/approachCandidates interested in acquiring basic practical skills in working with a collaborative robot completed a distance learning exercise in preparation for a hands-on training workshop. The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).FindingsThe results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.Research limitations/implicationsThe low number of candidates available limited in-depth analyses of the learning outcomes.Practical implicationsThe study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.Social implicationsThe study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.Originality/valueThe development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.","PeriodicalId":51872,"journal":{"name":"International Journal of Information and Learning Technology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Objectively measuring learning outcomes of information technology-assisted training courses\",\"authors\":\"Gerald Schneikart, W. 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The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).FindingsThe results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.Research limitations/implicationsThe low number of candidates available limited in-depth analyses of the learning outcomes.Practical implicationsThe study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.Social implicationsThe study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.Originality/valueThe development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.\",\"PeriodicalId\":51872,\"journal\":{\"name\":\"International Journal of Information and Learning Technology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information and Learning Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/ijilt-04-2022-0086\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information and Learning Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijilt-04-2022-0086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Objectively measuring learning outcomes of information technology-assisted training courses
PurposeThe objective of the presented pilot study was to test the applicability of a metric to specifically measure performance improvement via a hands-on workshop about collaborative robotics.Design/methodology/approachCandidates interested in acquiring basic practical skills in working with a collaborative robot completed a distance learning exercise in preparation for a hands-on training workshop. The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).FindingsThe results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.Research limitations/implicationsThe low number of candidates available limited in-depth analyses of the learning outcomes.Practical implicationsThe study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.Social implicationsThe study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.Originality/valueThe development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.
期刊介绍:
International Journal of Information and Learning Technology (IJILT) provides a forum for the sharing of the latest theories, applications, and services related to planning, developing, managing, using, and evaluating information technologies in administrative, academic, and library computing, as well as other educational technologies. Submissions can include research: -Illustrating and critiquing educational technologies -New uses of technology in education -Issue-or results-focused case studies detailing examples of technology applications in higher education -In-depth analyses of the latest theories, applications and services in the field The journal provides wide-ranging and independent coverage of the management, use and integration of information resources and learning technologies.