Factors Affecting Learning of Vector Math from Computer-Based Practice: Feedback Complexity and Prior Knowledge.
Factors Affecting Learning of Vector Math from Computer-Based Practice: Feedback Complexity and Prior Knowledge.
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影响基于计算机的实践学习向量数学的因素：反馈复杂性和先验知识。
DOI：
10.1103/physrevphyseducres.12.010134
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发表时间：
2016
影响因子：
3.9
通讯作者：
Brendon D. Mikula
中科院分区：
文献类型：
--
作者：
A. Heckler;Brendon D. Mikula
在实验中，包括超过450名大学水平的学生，我们研究了几个层次的反馈复杂性的有效性和时间效率在简单的，基于计算机的培训利用静态问题序列。学习领域是简单的矢量数学，这是入门物理学的基本技能。在一个独特的全因子设计中，我们研究了“正确反应知识”反馈和“精心设计的反馈”（即，一般性解释）分别和一起。一些其他因素进行了分析，包括培训时间，物理课程成绩，先验知识的矢量数学，和学生的信念，他们的熟练程度和重要性的矢量math.We假设一个简单的模型预测反馈的有效性如何依赖于先验知识，结果证实了这种知识治疗的相互作用。最值得注意的是，详细的反馈是最有效的反馈，特别是对学生的低先验知识和低课程成绩。与此相反，知识的正确反应反馈是不太有效的低性能的学生，包括这两种反馈并没有显着提高性能相比，精心制作的反馈。此外，虽然详细的反馈导致更高的分数，但学习率充其量只是稍微高一点，因为训练时间稍微长一点。训练时间数据显示，学生在回答错误的训练问题后，花了更多的时间在详细的反馈上。最后，我们发现，培训提高了学生自我报告的熟练程度，并认为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，基于计算机的静态问题序列和即时详细的反馈，以简单和一般的解释形式的培训可以是一种有效的方法，以提高学生的物理基本技能的表现，特别是对于准备不足和表现不佳的学生。
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 “knowledge of correct response” feedback and “elaborated feedback” (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.