Automatic Competence Leveling of Learning Objects

Ricardo Kawase, Patrick Siehndel, B. Nunes, M. Fisichella
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引用次数: 3

Abstract

A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. However, the process of annotating learning objects with competence levels is a very time consuming task, ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.
学习对象的自动能力水平调整
能力是在一个领域内不同熟练程度的有效表现。教育机构运用能力来了解一个人是否具有特定水平的能力或技能。丰富了能力信息的教育资源使学习者能够在细粒度层面上确定学习哪些资源以达到特定的能力目标。然而,标注学习对象的能力水平是一个非常耗时的任务,理想情况下,这项任务应该由教育资源主题的专家来完成。因此,大多数在线教育资源都不包含能力信息。本文提出了一种基于能力水平的教育资源自动分配方法。为了解决这些问题,我们利用从Web上可用的外部存储库中提取的信息,这将我们引向一种独立于域的方法。我们通过对真实世界数据的评估和额外的用户研究来证明所提出方法的质量。结果表明,该方法对地真数据的能力等级自动分配精度达到84%。我们的方法的主要含义是:第一,它有效地帮助专家完成艰巨的能力分配任务;第二,它直接支持学习者检索适当的水平材料。
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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