自动形成性评价在高等教育数学学习中的有效性

A. Barana, M. Marchisio, M. Sacchet
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引用次数: 3

摘要

2019冠状病毒病引发的卫生紧急情况凸显了数学学习和教学需要新的、灵活的数字方法,这些方法可以支持学生的个人需求,并有助于塑造教育。在本文中,我们建议使用基于形成性评估和反馈理论框架的创新模型设计的自动形成性评估(AFA)活动。该模型包括:活动的可用性和多次尝试;算法问题;开放数学答案;更符合实际的任务;即时和互动反馈。我们分析了96名生物技术一年级学生在数学混合模块中使用AFA的经验。我们从平台上收集了AFA活动的成绩和尝试次数数据,并与期末考试成绩进行了交叉核对。结果表明,从AFA中获得的反馈帮助学生提高了成绩,并为他们的考试准备提供了支持。根据这些结果,我们可以得出结论,在大流行期间和之后,类似的活动可以成为支持学生自学的有效解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effectiveness of Automatic Formative Assessment for learning Mathematics in Higher Education
The health emergency due to COVID-19 has highlighted the need of new and flexible digital methodologies for learning and teaching Mathematics, which can support the individual student’s needs and help shape education. In this paper, we propose to use Automatic Formative Assessment (AFA) activities designed according to an innovative model, framed on the theories of formative assessment and feedback. The model includes: availability of the activities and multiple attempts; algorithmic questions; open mathematical answers; contextualized tasks; immediate and interactive feedback. We analyzed an experience using AFA in a blended module in Mathematics for 96 first-year students in Biotechnology. We collected data from the platform on grades and attempts of AFA activities and cross-checked them with the final exam grades. The results show that the feedback gained from AFA helped students improve their performance and supported them in the exam preparation. In light of these results, we can conclude that similar activities can be an effective solution to support students’ self-study during and after the pandemic.
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