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Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students' knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling (Settles et al., 2018) provides students' trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students' learning traces revealdoi:10.18653/v1/w18-0543 dblp:conf/bea/ChenHH18 fatcat:xjfj2yy5nne2xe5khxms7r3d3i