Creating authentic assessment in mathematics

Ria Symonds, Lisa Mott
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Abstract

Assessment of students’ mathematics knowledge within higher education (HE) has normally taken a very traditional approach. Closed-book assessments have long been the favoured mode of assessment (Iannone Simpson, 2011) which often requires students to recall facts, formulae, and methods. One could argue that this type of assessment is limited in its ability to effectively assess how well a student’s ability to authentically use mathematics has developed. Due to the recent pandemic, many institutions were forced to rethink their assessment methods so that they could be delivered online and remotely. As such, there has been a renewed sense of need for more ‘authentic’ assessments for mathematics-based programmes.In this paper, we will discuss our journey of creating more authentic assessments for apprentices enrolled on a new Data Science Degree Apprenticeship, particularly in mathematics/statistics. We will compare two years of delivery of the course; the first year of delivery which comprised of traditional assessment methods (coursework/exam) and the second year of delivery that used more authentic assessment methods. We will discuss the pros and cons of each model by reflecting on our practice and drawing on apprentices’ feedback.
创建真实的数学评估
高等教育(HE)中对学生数学知识的评估通常采用非常传统的方法。长期以来,闭卷评估一直是最受欢迎的评估模式(Iannone Simpson, 2011),通常要求学生回忆事实、公式和方法。可以说,这种评估方式在有效评估学生真正运用数学的能力方面存在局限性。由于最近的大流行病,许多院校不得不重新考虑他们的评估方法,以便能够在线和远程进行评估。在本文中,我们将讨论我们为新数据科学学位学徒制的学徒创建更真实评估的历程,尤其是在数学/统计学方面。我们将对两年的课程实施情况进行比较:第一年的课程实施包括传统的评估方法(课业/考试),第二年的课程实施则采用更真实的评估方法。我们将通过反思我们的实践和借鉴学徒的反馈意见来讨论每种模式的利弊。
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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