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Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum

机译：与聪明的导师和工作实例一起学习：自适应地选择学习活动比固定课程能带来更好的学习效果

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摘要

The main learning activity provided by intelligent tutoring systems is problem solving, although several recent projects investigated the effectiveness of combining problem solving with worked examples. Previous research has shown that learning from examples is an effective learning strategy, especially for novice learners. A worked example provides step-by-step explanations of how a problem is solved. Many studies have compared learning from examples to unsupported problem solving, and suggested presenting worked examples to students in the initial stages of learning, followed by problem solving once students have acquired enough knowledge. This paper presents a study in which we compare a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adapts learning tasks to students' needs. The adaptive strategy determines the type of the task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received on the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problem solving. Novices from the adaptive condition learnt faster than novices from the control group, while the advanced students from the adaptive condition learnt more than their peers from the control group.
机译：智能辅导系统提供的主要学习活动是解决问题，尽管最近有几个项目研究了将解决问题的方法与实际案例相结合的有效性。先前的研究表明，从示例中学习是一种有效的学习策略，特别是对于新手学习者。一个可行的示例提供了有关如何解决问题的分步说明。许多研究将从示例学习到无支持的问题解决进行了比较，并建议在学习的初始阶段向学生提供示例，然后在学生获得足够的知识后进行问题解决。本文提出了一项研究，在该研究中，我们将固定顺序的交替工作示例和经过辅导的问题解决方案与根据学生需求调整学习任务的策略进行比较。自适应策略根据学生在先前问题上获得的帮助来确定任务的类型（工作示例，褪色示例或要解决的问题）。结果表明，在适应性条件下的学生比同龄人获得了更多的学习机会，他们的同学得到了固定的工作实例和问题解决顺序。适应性条件的新手比对照组的新手学习得快，而适应性条件的高级学生比对照组的同龄人学习得更多。

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