Ridgeway Karl, Mozer Michael C, Bowles Anita R
Department of Computer Science, University of Colorado.
Department of Computer Science and Institute of Cognitive Science, University of Colorado.
Cogn Sci. 2017 May;41(4):924-949. doi: 10.1111/cogs.12385. Epub 2016 Jun 27.
We explore the nature of forgetting in a corpus of 125,000 students learning Spanish using the Rosetta Stone 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.
我们在一个由12.5万名使用罗塞塔石碑外语教学软件学习西班牙语的学生组成的语料库中，对48节课的遗忘本质进行了探究。学生在初次学习某节课后接受测试，然后在经过不同的时间间隔后再次接受测试。我们观察到遗忘符合幂函数衰减规律，其速率因课程而异，但不因学生而异。我们发现，最初学习效果较好的课程遗忘得更慢，这种相关性可能反映了一个潜在原因，比如课程的质量或难度。通过用编码学生对课程的初始学习特征以及学生在初次测试和延迟测试之间所参与活动的特征来增强遗忘模型，我们提高了该模型的预测准确性。增强后的模型可以预测个体在延迟测试中分数方差的23.9%。我们分析了哪些特征能最好地解释个体表现。
