Date of Award
Master of Science (MS)
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.
Ridgeway, Karl, "Predicting the Performance of Rosetta Stone Language Learners with Individualized Models of Forgetting" (2014). Computer Science Graduate Theses & Dissertations. 2.