Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software
Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software
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DOI：
10.1111/cogs.12385
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发表时间：
2017-05-01
影响因子：
2.5
通讯作者：
Bowles, Anita R.
中科院分区：
文献类型：
--
作者：
Ridgeway, Karl;Mozer, Michael C.;Bowles, Anita R.
我们在使用罗塞塔石碑（Rosetta Stone）外语教学软件学习西班牙语的125,000名学生的语料库中探索了遗忘的本质，共48节课。学生们在最初学习完一节课后进行测试，然后在不同的时间间隔后重新测试。我们观察到遗忘与幂函数衰减的速率在不同的课程中有所不同，但在不同的学生中没有。我们发现，一开始学得更好的课程遗忘得更慢，这种相关性可能反映了一个潜在的原因，比如课程的质量或难度。我们在遗忘模型中加入了一些特征，这些特征可以编码学生对课程的初始学习以及学生在初始测试和延迟测试之间参与的活动的特征，从而提高了遗忘模型的预测准确性。增强模型可以预测23.9%的个体延迟测试分数方差。我们分析哪些特征最能解释个人表现。
We explore the nature of forgetting in a corpus of 125,000 students learning Spanish using the Rosetta Stone((R)) foreign-language instruction software across 48 lessons. Students are tested on a lesson after its initial study and are then retested after a variable time lag. We observe forgetting consistent with power function decay at a rate that varies across lessons but not across students. We find that lessons which are better learned initially are forgotten more slowly, a correlation which likely reflects a latent cause such as the quality or difficulty of the lesson. We obtain improved predictive accuracy of the forgetting model by augmenting it with features that encode characteristics of a student's initial study of the lesson and the activities the student engaged in between the initial and delayed tests. The augmented model can predict 23.9% of the variance in an individual's score on the delayed test. We analyze which features best explain individual performance.