Pedagogical Word Recommendation: A novel task and dataset on personalized vocabulary acquisition for L2 learners
This addresses the inefficiency and demotivation in vocabulary learning for L2 learners, though it is incremental as it builds on existing recommendation and tutoring system methods.
The paper tackles the problem of personalized vocabulary acquisition for second language learners by proposing a novel task called Pedagogical Word Recommendation (PWR), which predicts whether a learner knows a word based on their prior word exposure, and reports evaluation results using a Neural Collaborative Filtering approach on a dataset from ~1M learners.
When learning a second language (L2), one of the most important but tedious components that often demoralizes students with its ineffectiveness and inefficiency is vocabulary acquisition, or more simply put, memorizing words. In light of such, a personalized and educational vocabulary recommendation system that traces a learner's vocabulary knowledge state would have an immense learning impact as it could resolve both issues. Therefore, in this paper, we propose and release data for a novel task called Pedagogical Word Recommendation (PWR). The main goal of PWR is to predict whether a given learner knows a given word based on other words the learner has already seen. To elaborate, we collect this data via an Intelligent Tutoring System (ITS) that is serviced to ~1M L2 learners who study for the standardized English exam, TOEIC. As a feature of this ITS, students can directly indicate words they do not know from the questions they solved to create wordbooks. Finally, we report the evaluation results of a Neural Collaborative Filtering approach along with an exploratory data analysis and discuss the impact and efficacy of this dataset as a baseline for future studies on this task.