Estimating Student Knowledge from Paired Interaction Data
Estimating Student Knowledge from Paired Interaction Data
复制标题
从配对交互数据估计学生的知识
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
E. Brunskill
中科院分区：
文献类型：
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作者：
Anna N. Rafferty;Jodi L. Davenport;E. Brunskill
根据学生与基于计算机的导师的互动来估计学生的知识，有可能通过减少评估时间和促进个性化干预来改善学习。虽然在相对结构化的主题和导师方面存在良好的学生模式，但在开放式活动方面取得的进展较小。此外，学生们通常是两人一组完成活动，而不是单独完成，没有编码来指示谁执行了每个动作。我们调查是否对互动与开放式化学导师可以用来预测个别学生后测试的表现。使用L1正则化回归，我们表明，学生与导师的互动是预测的平均测试后的分数对个人的分数。为了更好地理解这种情况下的配对动态，我们还发现，对于由具有相似的前测试成绩的学生组成的配对，我们可以预测学生的后测试成绩的差异。
Estimating students’ knowledge based on their interactions with computer-based tutors has the potential to improve learning by decreasing time taking assessments and facilitating personalized interventions. Although there exist good student models for relatively structured topics and tutors, less progress has been made with more open-ended activities. Further, students often complete activities in pairs rather than individually, with no coding to indicate who performed each action. We investigate whether pair interactions with an open-ended chemistry tutor can be used to predict individual student post test performance. Using L1-regularized regression, we show that student interactions with the tutor are predictive both of the average post-test score for the pair and of individual scores. Towards better understanding pair dynamics in this setting, we also find that for pairs composed of students with similar pre-test scores, we can predict the difference in students’ post-test scores.