Context Based Approach for Second Language Acquisition
Nihal V. Nayak, Arjun Rao · 2018
SLAM 2018 focuses on predicting a student's mistake while using the Duolingo application.In this paper, we describe the system we developed for this shared task.Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks -English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en).We conduct an ablation study with several features during the development of this system and discover that context based features play a major role in language acquisition modeling.Our model beats Duolingo's baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812).Our work makes a case for providing favourable textual context for students while learning second language.