Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent
This addresses a specific challenge for ESL learners in vocabulary acquisition, representing an incremental improvement over prior hand-crafted methods.
The paper tackles the problem of helping English-as-a-second language learners distinguish near-synonym words by proposing an inference-based learner-like agent to identify helpful example sentences, achieving the best performance in fill-in-the-blank and sentence selection tasks and improving scores for over 17% of students in a user study.
Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ. Prior work uses hand-crafted scores to recommend sentences but has difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways. We notice that the helpfulness of the learning material would reflect on the learners' performance. Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance. To enable the agent to behave like a learner, we leverage entailment modeling's capability of inferring answers from the provided materials. Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-in-the-blank (FITB) and good example sentence selection tasks. We further conduct a classroom user study with college ESL learners. The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently. Compared to other models, the proposed agent improves the score of more than 17% of students after learning.