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Factors affecting learning of vector math from computer-based practice: Feedback complexity and prior knowledge

机译:从基于计算机的实践中,影响矢量数学学习的因素:反馈复杂性和先验知识

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In experiments including over 450 university-level students, we studied the effectiveness and time efficiency of several levels of feedback complexity in simple, computer-based training utilizing static question sequences. The learning domain was simple vector math, an essential skill in introductory physics. In a unique full factorial design, we studied the relative effects of a€?knowledge of correct responsea€? feedback and a€?elaborated feedbacka€? (i.e., a general explanation) both separately and together. A number of other factors were analyzed, including training time, physics course grade, prior knowledge of vector math, and student beliefs about both their proficiency in and the importance of vector math. We hypothesize a simple model predicting how the effectiveness of feedback depends on prior knowledge, and the results confirm this knowledge-by-treatment interaction. Most notably, elaborated feedback is the most effective feedback, especially for students with low prior knowledge and low course grade. In contrast, knowledge of correct response feedback was less effective for low-performing students, and including both kinds of feedback did not significantly improve performance compared to elaborated feedback alone. Further, while elaborated feedback resulted in higher scores, the learning rate was at best only marginally higher because the training time was slightly longer. Training time data revealed that students spent significantly more time on the elaborated feedback after answering a training question incorrectly. Finally, we found that training improved student self-reported proficiency and that belief in the importance of the learned domain improved the effectiveness of training. Overall, we found that computer based training with static question sequences and immediate elaborated feedback in the form of simple and general explanations can be an effective way to improve student performance on a physics essential skill, especially for less prepared and low-performing students.
机译:在实验中,包括超过450名大学级学生,我们研究了利用静态问题序列的简单,计算机训练的几个反馈复杂性的有效性和时间效率。学习域是简单的矢量数学,介绍物理学的重要技能。在独特的完整因子设计中,我们研究了一个€的相对效果?了解正确的答复€€?反馈和一个€?详细展示的反馈? (即,一般解释)单独和一起。分析了许多其他因素,包括培训时间,物理课程等级,传染媒介数学的先验知识,以及对传染媒介数学的熟练程度和重要性的学生信仰。我们假设一个简单的模型,预测反馈的有效性如何取决于先验知识,结果证实了这种逐个治疗的互动。最特别的是,详细的反馈是最有效的反馈,特别是对于具有较低知识和低课程等级的学生。相比之下,对于低性能的学生,对正确响应反馈的知识对较低的学生而言,并且与单独制定的反馈相比,两种反馈都没有显着提高性能。此外,虽然详细的反馈导致得分更高,但学习率最多只是略微更高,因为训练时间稍长。培训时间数据显示,在不正确的回答培训问题后,学生在阐述的反馈中花费了显着更多的时间。最后,我们发现培训改善了学生自我报告的熟练程度,并认为对学习领域的重要性提高了培训的有效性。总的来说,我们发现基于计算机的训练与静态问题序列和简单和一般性解释形式的立即详细的反馈可以是提高物理基本技能上学生表现的有效途径,特别是对于更少准备和低性能的学生来说。

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