The Impact on Individualizing Student Models on Necessary Practice Opportunities
The Impact on Individualizing Student Models on Necessary Practice Opportunities
复制标题
个性化学生模式对必要实践机会的影响
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
E. Brunskill
中科院分区：
文献类型：
--
作者：
Jung In Lee;E. Brunskill
在对学生学习进行建模时，使用知识追踪框架的导师通常假设所有学生都具有相同的模型参数集。我们发现，当对个体学生进行参数拟合时，个体参数之间存在显着差异。我们通过计算如果使用单个学生自己的估计模型参数与总体模型相比来评估掌握程度所需的预期练习机会数量的差异，来检查这种变化对于教学决策是否重要。在所考虑的数据集中，我们发现，如果使用基于人群的模型对学生进行建模，则很大一部分学生的实践机会预计是使用学生自己的模型参数时所需数量的两倍。我们还发现，另外很大一部分学生获得的练习机会可能会少于所需的机会，这意味着这些学生会过早晋级。尽管需要对其他数据集进行进一步的研究来更深入地探讨这个问题，但我们的结果表明，考虑学生参数的个体差异可能会对使用知识追踪模型的智能辅导系统中做出的教学决策产生重要影响。
When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual’s parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of practice opportunities required if mastery is assessed using an individual student’s own estimated model parameters, compared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student’s own model parameters were used. We also find an additional significant portion of students will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model.