An editable learner model for text recommendation for language learning

IF 4.6 1区 文学 Q1 EDUCATION & EDUCATIONAL RESEARCH Recall Pub Date : 2021-06-30 DOI:10.1017/S0958344021000197
J. Lee
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引用次数: 5

Abstract

Abstract Extracurricular reading is important for learning foreign languages. Text recommendation systems typically classify users and documents into levels, and then match users with documents at the same level. Although this approach can be effective, it has two significant shortcomings. First, the levels assume a standard order of language acquisition and cannot be personalized to the users’ learning patterns. Second, recommendation decisions are not transparent because the leveling algorithms can be difficult for users to interpret. We propose a novel method for text recommendation that addresses these two issues. To enhance personalization, an open, editable learner model estimates user knowledge of each word in the foreign language. The documents are ranked by new-word density (NWD) – that is, the percentage of words that are new to the user in the document. The system then recommends documents according to a user-specified target NWD. This design offers complete transparency as users can scrutinize recommendations by reviewing the NWD estimation of the learner model. This article describes an implementation of this method in a mobile app for learners of Chinese as a foreign language. Evaluation results show that users were able to manipulate the learner model and NWD parameters to adjust the difficulty of the recommended documents. In a survey, users reported satisfaction with both the concept and implementation of this text recommendation method.
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语言学习文本推荐的可编辑学习者模型
课外阅读对外语学习很重要。文本推荐系统通常将用户和文档分为不同的级别,然后将用户与同一级别的文档进行匹配。尽管这种方法可能是有效的,但它有两个明显的缺点。首先,这些水平假设语言习得的标准顺序,不能根据用户的学习模式进行个性化。其次,推荐决策不透明,因为均衡算法对用户来说很难解释。我们提出了一种新的文本推荐方法来解决这两个问题。为了增强个性化,一个开放的、可编辑的学习者模型估计用户对外语中每个单词的知识。这些文档按照新词密度(NWD)排序——也就是文档中对用户来说是新单词的百分比。然后系统根据用户指定的目标NWD推荐文档。这种设计提供了完全的透明度,因为用户可以通过审查学习者模型的NWD估计来仔细检查建议。本文介绍了该方法在一款面向对外汉语学习者的手机应用程序中的实现。评估结果表明,用户能够操纵学习者模型和NWD参数来调整推荐文档的难度。在一项调查中,用户对这种文本推荐方法的概念和实施都表示满意。
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来源期刊
Recall
Recall Multiple-
CiteScore
8.50
自引率
4.40%
发文量
17
期刊最新文献
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