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Forgetting of Foreign‐Language Skills: A Corpus‐Based Analysis of Online Tutoring Software
Author(s) -
Ridgeway Karl,
Mozer Michael C.,
Bowles Anita R.
Publication year - 2017
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12385
Subject(s) - forgetting , predictive power , computer science , variance (accounting) , psychology , quality (philosophy) , software , foreign language , test (biology) , cognitive psychology , artificial intelligence , natural language processing , mathematics education , paleontology , philosophy , accounting , epistemology , business , biology , programming language
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.