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A user-transaction-based recommendation strategy for an educational digital library

机译：教育数字图书馆的基于用户交易的推荐策略

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摘要

The automated recommendation of content resources to learners is one of the most promising functions of educational digital libraries. Underlying strategies should take the individual progress of the learner into account to provide appropriate recommendations that are meaningful to the learner. If presented with appropriate assistance, learners will more likely engage in productive learning strategies, such as reading up on concepts and accessing preparatory materials, and refrain from unproductive behavior, such as guessing on or copying of homework. In this exploratory case study, we are analyzing transactional data within an educational digital library of online physics homework problems and learning content. The sequence of events starting with a learner failing to solve a particular problem, interacting with other online resources, and then succeeding on that same problem is used to identify potentially helpful resources for future learners. It was found that these "success stories" indeed allow for providing recommendations with acceptable accuracy, which, when implemented, may lead to more productive learning paths.
机译：内容资源的自动推荐给学习者是教育数字图书馆最有前途的功能之一。基本战略应考虑到学习者的个人进度，为学习者提供有意义的建议。如果提供适当的援助，学习者将更有可能从事生产性学习策略，例如阅读概念和访问预备材料，并避免不生产行为，例如猜测或复制作业。在这项探索性案例研究中，我们正在分析在线物理作业问题和学习内容的教育数字图书馆内的交易数据。从学习者开始的事件序列未能解决特定问题，与其他在线资源交互，然后在同一问题上取得成功用于识别未来学习者的可能有用的资源。有人发现，这些“成功故事”确实允许以可接受的准确性提供建议，这在实施时可能导致更高效的学习路径。

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