{"title":"WORK INTEGRATED LEARNING IN STATISTICS AND COMPUTER SCIENCE AND FAIR ASSESSMENT OF AUTHENTIC PROJECTS","authors":"A. Bilgin, Angela M. Powell, Deborah Richards","doi":"10.52041/serj.v21i2.26","DOIUrl":null,"url":null,"abstract":"Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics Education Research Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52041/serj.v21i2.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.
期刊介绍：
SERJ is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). SERJ is published twice a year and is free. SERJ aims to advance research-based knowledge that can help to improve the teaching, learning, and understanding of statistics or probability at all educational levels and in both formal (classroom-based) and informal (out-of-classroom) contexts. Such research may examine, for example, cognitive, motivational, attitudinal, curricular, teaching-related, technology-related, organizational, or societal factors and processes that are related to the development and understanding of stochastic knowledge. In addition, research may focus on how people use or apply statistical and probabilistic information and ideas, broadly viewed. The Journal encourages the submission of quality papers related to the above goals, such as reports of original research (both quantitative and qualitative), integrative and critical reviews of research literature, analyses of research-based theoretical and methodological models, and other types of papers described in full in the Guidelines for Authors. All papers are reviewed internally by an Associate Editor or Editor, and are blind-reviewed by at least two external referees. Contributions in English are recommended. Contributions in French and Spanish will also be considered. A submitted paper must not have been published before or be under consideration for publication elsewhere.
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