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.

向学习者自动推荐内容资源是教育数字图书馆最有前途的功能之一。基本策略应考虑学习者的个人进步，以提供对学习者有意义的适当建议。如果得到适当的帮助，学习者将更有可能从事生产性学习策略，例如阅读概念和获取准备材料，并避免进行非生产性行为，例如猜测或抄写作业。在这个探索性案例研究中，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。从学习者未能解决特定问题开始的一系列事件，与其他在线资源进行交互，然后成功解决相同的问题，从而为将来的学习者确定潜在的有用资源。人们发现，这些“成功案例”确实可以提供具有可接受准确性的建议，实施这些建议可能会导致更富有成效的学习路径。