Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?
Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?
复制标题
哪些用户交互可以预测工作集成学习的专业水平？
DOI：
10.1007/978-3-642-40814-4_15
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Barbara Kump
中科院分区：
文献类型：
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作者：
Tobias Ley;Barbara Kump
从用户与自适应系统的隐式交互来预测知识水平是一项困难的任务，特别是在日常工作任务的上下文中使用的学习系统中。我们已经收集了六个人的互动与自适应工作集成学习系统APOSDLE在两个月的时间内，找出是否自然发生的互动与系统可以用来预测他们的专业水平。一组交互基于他们执行的任务，另一组基于许多额外的知识指示事件KIE。我们发现，除了KIE显着提高了预测相比，仅使用任务。这两种方法都上级只使用事件频率的模型。
Predicting knowledge levels from user's implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events KIE. We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.
维持 TEL：从创新到学习和实践
DOI：
10.1007/978-3-642-16020-2_2
发表时间：
2010
期刊：
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影响因子：
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作者：
Anastopoulou S
通讯作者：
Anastopoulou S