Data-Driven Decision-Making (DDDM) for Higher Education Assessments: A Case Study

S. Kaspi, S. Venkatraman
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Abstract

The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an important and powerful role in students’ educational experience and evaluation of their academic abilities. However, there are many stigmas associated with both “traditional” and alternative assessment methods. Rethinking assessments is increasingly happening worldwide to keep up with the shift in current teaching and learning paradigms due to new possibilities of using digital technologies and a continuous improvement of student engagement. Many educational decisions such as a change in assessment from traditional summative exams to alternate methods require appropriate rationale and justification. In this paper, we adopt data-driven decision-making (DDDM) as a process for rethinking assessment methods and implementing assessment transformations innovatively in an HE environment. We make use of student performance data to make an informed decision for moving from exam-based assessments to nonexam assessment methods. We demonstrate the application of the DDDM approach for an educational institute by analyzing the impact of transforming the assessments of 13 out of 27 subjects offered in a Bachelor of Information Technology (BIT) program as a case study. A comparison of data analysis performed before, during, and after the COVID-19 pandemic using different student learning measures such as failure rates and mean marks provides meaningful insights into the impact of assessment transformations. Our implementation of the DDDM model along with examining the influencing factors of student learning through assessment transformations in an HE environment is the first of its kind. With many HE providers facing several challenges due to the adoption of blended learning, this pilot study based on a DDDM approach encourages innovation in classroom teaching and assessment redesign. In addition, it opens further research in implementing such evidence-based practices for future classroom innovations and assessment transformations towards achieving higher levels of educational quality.
高等教育评估中的数据驱动决策(DDDM):一个案例研究
高等教育系统正在经历巨大的变革,以跟上数字技术的快速发展,并由于最近的COVID-19大流行迫使教育机构完全转向在线教学和评估。评估被认为在学生的教育经历和学术能力评价中起着重要而有力的作用。然而,“传统”和替代评估方法都有许多耻辱。由于使用数字技术的新可能性和学生参与度的不断提高,全球越来越多地进行重新思考评估,以跟上当前教学范式的转变。许多教育决策,如将评估从传统的总结性考试改为替代方法,需要适当的理由和理由。在本文中,我们采用数据驱动决策(DDDM)作为在高等教育环境中重新思考评估方法和创新实施评估转型的过程。我们利用学生的表现数据做出明智的决定,从基于考试的评估方法转向非考试的评估方法。我们通过分析信息技术学士(BIT)课程中27个科目中13个科目的评估转变的影响,作为案例研究,展示了DDDM方法在教育机构中的应用。在COVID-19大流行之前、期间和之后使用不同的学生学习指标(如不合格率和平均分数)进行的数据分析进行比较,为评估变革的影响提供了有意义的见解。我们对DDDM模型的实施,以及在高等教育环境中通过评估转换来检查学生学习的影响因素,是同类研究中的首例。由于许多高等教育提供者由于采用混合学习而面临一些挑战,这项基于DDDM方法的试点研究鼓励了课堂教学和评估重新设计的创新。此外,它还为未来的课堂创新和评估变革开辟了进一步的研究,以实现更高水平的教育质量。
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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