title: Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software.
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.
authors.affiliation: Department of Computer Science, University of Colorado.
authors.forename: Karl
authors.initials: K
authors.lastname: Ridgeway
authors.affiliation: Department of Computer Science and Institute of Cognitive Science, University of Colorado.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Mozer
authors.affiliation: Rosetta Stone.
authors.forename: Anita R
authors.initials: AR
authors.lastname: Bowles
mesh_terms: D001076:Aptitude; D006801:Humans; D007802:Language; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008962:Models, Theoretical; D019303:Multilingualism; D012984:Software
keywords: Big data; Computational modeling; Corpus analysis; Forgetting; Second language learning