Predicting the Performance of Rosetta Stone Language Learners with Individualized Models of Forgetting

Graduate Thesis Or Dissertation

Citations

Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/7d278t36z

Abstract

I explore the nature of forgetting in a corpus of 125,000 students using the Rosetta Stone® foreign-language instruction software on 48 Spanish lessons. Students are tested on a lesson after its completion and are then retested after a variable time lag. The observed power-law forgetting curves have a small temporal decay rate that varies from lesson to lesson. I 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 two tests. I then analyze which features best explain individual performance, and find that using these features the augmented model can predict about 25% of the variance in an individual's score on the second test.

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