A Fine-Grained Open Learner Model for an Introductory Programming Course

A Fine-Grained Open Learner Model for an Introductory Programming Course
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编程入门课程的细粒度开放学习者模型

DOI:
10.1145/3209219.3209242
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发表时间:
2018
期刊:
Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
影响因子:
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通讯作者:
Peter Brusilovsky
Peter Brusilovsky
中科院分区:
--
文献类型:
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作者:
Jordan Barria;Julio Daniel Guerra Hollstein;Peter Brusilovsky

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引导学生进行最适合其当前知识水平的学习活动是适应性教育系统在过去几十年中试图实现的目标之一。最近,已经进行了几次尝试,使用开放式学习者模型(OLM)作为实现这一目标的工具。虽然OLM的最初目标是帮助学生反思自己的学习过程,但通过扩展OLM的导航支持功能,学生可以立即采取行动来提高他们的知识。在这项工作中,我们试图通过开发一个细粒度的OLM,为学生提供知识可视化的主题和概念水平上的导航支持功能的OLM。细粒度的OLM使学生能够直接探索他们的知识和可用的学习活动之间的联系,对他们的下一个学习步骤做出明智的决定。为了评估新型OLM的影响,我们在课堂研究中评估了它的几个版本,同时还将其与我们早期研究中的粗粒度OLM数据进行了比较。我们的研究结果表明,细粒度的OLM大大影响学生的学习活动的选择,使学生的学习更有效率。我们还发现,细粒度OLM的具体设计特点可以影响学生的信心和持久性,而选择和尝试的学习活动。
Guiding students to the learning activities that are most appropriate for their current level of knowledge is one of the goals that adaptive educational systems tried to achieve during the last decades. Recently, several attempts have been made to use Open Learner Models (OLM) as a tool for achieving this goal. While the original goal of OLM is to help students reflect about their own learning process, extending OLM with navigation support functionality enables students to take immediate actions towards improving their knowledge. In this work, we attempted to extend the navigation support functionality of OLM by developing a fine-grained OLM that offers student knowledge visualization on both topic and concept levels. The fine-grained OLM enables students to directly explore connections between their knowledge and available learning activities, making an informed decision about their next learning steps. To assess the impact of the new type of OLM, we evaluated several versions of it in a classroom study, while also comparing it with data from our earlier studies that featured a coarse-grained OLM. Our results suggest that the fine-grained OLM considerably impacts student choice of learning activities, making student learning more efficient. We also found that the specific design features of fine-grained OLM could affect students' confidence and persistence while selecting and attempting the learning activities.