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Building an intelligent recommendation system for personalized test scheduling in computerized assessments: A reinforcement learning approach
Behavior Research Methods ( IF 4.6 ) Pub Date : 2021-06-15 , DOI: 10.3758/s13428-021-01602-9
Jinnie Shin 1 , Okan Bulut 2
Affiliation  

The introduction of computerized formative assessments in the classroom has opened a new area of effective progress monitoring with more accessible test administrations. With computerized formative assessments, all students could be tested at the same time and with the same number of test administrations within a school year. Alternatively, the decision for the number and frequency of such tests could be made by teachers based on their observations and personal judgments about students. However, this often results in rigid test scheduling that fails to take into account the pace at which students acquire knowledge. To administer computerized formative assessments efficiently, teachers should be provided with systematic guidance regarding effective test scheduling based on each student’s level of progress. In this study, we introduce an intelligent recommendation system that can gauge the optimal number and timing of testing for each student. We discuss how to build an intelligent recommendation system using a reinforcement learning approach. Then, we present a case study with a large sample of students’ test results in a computerized formative assessment. We show that the intelligent recommendation system can significantly reduce the number of testing for the students by eliminating unnecessary test administrations where students do not show significant progress (i.e., growth). Also, the proposed recommendation system is capable of identifying the optimal test time for students to demonstrate adequate progress from one test administration to another. Implications for future research on personalized assessment scheduling are discussed.



中文翻译:

为计算机化评估中的个性化测试调度构建智能推荐系统:一种强化学习方法

在课堂上引入计算机化的形成性评估开辟了一个有效进度监控的新领域,并提供更方便的考试管理。通过计算机化的形成性评估,所有学生可以在同一时间接受测试,并且在一学年内进行相同数量的测试管理。或者,教师可以根据他们对学生的观察和个人判断来决定此类测试的数量和频率。然而,这通常会导致严格的考试安排,未能考虑到学生获取知识的速度。为了有效地管理计算机化的形成性评估,应根据每个学生的进步水平为教师提供有关有效考试安排的系统指导。在这项研究中,我们引入了一个智能推荐系统,可以衡量每个学生的最佳测试数量和时间。我们讨论如何使用强化学习方法构建智能推荐系统。然后,我们展示了一个案例研究,其中包含大量学生在计算机化形成性评估中的测试结果样本。我们表明,智能推荐系统可以通过消除学生没有显着进步(即成长)的不必要的考试管理来显着减少学生的考试次数。此外,建议的推荐系统能够确定学生的最佳考试时间,以证明从一个考试管理到另一个考试管理有足够的进展。讨论了对个性化评估调度未来研究的影响。

更新日期:2021-06-15
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