Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?
Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?
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适应性支持与交替工作示例和辅导问题：哪个可以带来更好的学习？
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
B. McLaren
中科院分区：
文献类型：
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作者：
A. Najar;A. Mitrovic;B. McLaren
当第一次在一个新的领域学习时，从工作的例子中学习已经被证明是上级于不支持的问题解决。一些研究发现，从例子中学习的结果更快的学习相比，辅导解决问题的智能辅导系统。我们提出了一项研究，比较固定序列的交替工作的例子和辅导解决问题的策略，自适应地决定多少援助的学生需要。自适应策略根据学生在前一个问题中得到的帮助来确定任务的类型（工作示例，褪色示例或待解决的问题）。结果表明，学生在适应条件下学习显着超过他们的同龄人谁是一个固定的序列的工作例子和问题。
Learning from worked examples has been shown to be superior to unsupported problem solving when first learning in a new domain. Several studies have found that learning from examples results in faster learning in comparison to tutored problem solving in Intelligent Tutoring Systems. We present a study that compares a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adaptively decides how much assistance the student needs. The adaptive strategy determines the type of task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received in 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 problems.