Context Based Approach for Second Language Acquisition

WS 2018  ·  Nihal V. Nayak, Arjun R. Rao ·

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 plays 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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Language Acquisition SLAM 2018 Context Based Model AUC 0.821 # 1