统计学和计算机科学的工作整合学习与真实项目的公平评估

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2022-07-04 DOI:10.52041/serj.v21i2.26

A. Bilgin, Angela M. Powell, Deborah Richards

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引用次数: 1

摘要

工作综合学习（WIL）一直是医学、师范教育和工程等学科的常态，但直到最近才在统计学中实施，并不是每个计算机科学教育学生都能实现。随着大学重心的转变，为毕业生做好“就业准备”，大学与行业的合作扩大到包括学习和教学。毫无疑问，来自行业的真实问题为学生在毕业前实践未来职业创造了机会。然而，课程的这种转变给学生和讲师带来了挑战。在本文中，我们将介绍统计学和计算机科学的评估结构和案例研究。统计学和数据科学的教师可以采用或调整我们的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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WORK INTEGRATED LEARNING IN STATISTICS AND COMPUTER SCIENCE AND FAIR ASSESSMENT OF AUTHENTIC PROJECTS

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.

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来源期刊

Statistics Education Research Journal Social Sciences-Education

CiteScore

1.30

自引率

0.00%

发文量

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.