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A difficulty ranking approach to personalization in E-learning

机译：电子学习中个性化的难度排名方法

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摘要

The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance.
机译：电子学习系统和在线课程的普遍性使得各种能力和背景的学生广泛访问的教育材料。因此，越来越需要适应电子学习系统的个体差异。本文介绍了一种名为Edurank的算法，用于将教育内容的个性化，用于将协作过滤算法与投票方式结合起来。 Edurank通过在普通问题上汇总相似学生的排名来为每个学生构建难度排名。这些方面包括等级，重试次数和时间花费解决问题。它是直接对每个学生的问题排名的困难，而不是根据学生的预测得分订购它们。 Edurank算法在包含数千名学生和一百万个记录的两个数据集上进行了测试。它能够优于最先进的排名方法以及领域专家。 Edurank被学生在课堂活动中使用，其中一个先前的模型被纳入预测系统中没有现有历史记录的学生的难度排名。它被证明，引导学生解决了比域专家的订购更困难的问题，而不会降低他们的表现。

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