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Evaluating Recommender Algorithms for Learning Using Crowdsourcing

机译：使用众包评估推荐算法进行学习

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摘要

Keeping focused on a certain goal or topic when learning with resources found on the Web is a challenge. Creating a hierarchical learning goal structure with activities and sub-activities can help the learner to keep on track. Moreover, providing useful recommendations to such activities can further support the learner. However, recommendations need to be relevant to the specific goal or activity the learner is currently working on, as well as being novel and diverse to the learner. Such user-centric metrics like novelty and diversity are best measured by asking the users themselves. Nonetheless, conducting user experiments are notoriously time-consuming and access to an adequate amount of users is often very limited. Crowd sourcing offers a means to evaluate TEL recommender algorithms by reaching out to sufficient participants in a shorter time-frame and with less effort. In this paper, a concept for evaluating TEL recommender algorithms using crowd sourcing is presented as well as a repeated proof-of-concept evaluation experiment of a TEL graph-based recommender algorithm AScore that exploits hierarchical activity structures. Results from both experiments support the postulated hypotheses, thereby showing that crowd sourcing can be successfully applied to evaluate TEL recommender algorithms.
机译：保持专注于某一目标或话题在网上找到资源学习时是一个挑战。以创建活动和子活动的层次学习目标结构可以帮助学习者保持正轨。此外，这样的活动提供有益的建议，可以进一步支持学习者。但是，建议需要是相关的具体目标或活动学习者目前正在研究，以及作为新的和多样化的学习者。例如以用户为中心的度量像新颖性和多样性，最好通过询问用户自己测量的。然而，用户在进行实验是众所周知的费时并获得用户的足够的量通常非常有限。人群通过接触在更短的时间框架足够的参与者更省力采购报价来评估TEL推荐算法的手段。在本文中，为了评价使用众包TEL推荐器算法的一个概念被呈现，以及它利用分层有效性结构基于图TEL推荐算法华擎的重复验证的概念评价实验。来自两个实验结果支持这样的假设假定的，从而显示可以成功地应用，以评估TEL推荐算法众包。

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