Forgetting of Foreign‐Language Skills: A Corpus‐Based Analysis of Online Tutoring Software |
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Authors: | Karl Ridgeway Michael C Mozer Anita R Bowles |
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Institution: | 1. Department of Computer ScienceUniversity of Colorado;2. Department of Computer Science and Institute of Cognitive ScienceUniversity of Colorado;3. Rosetta Stone |
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Abstract: | 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. |
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Keywords: | Forgetting Big data Corpus analysis Computational modeling Second language learning |
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