Integrating Forgetting Behavior and Linguistic Features in Language Learning Models
Boxuan Ma, Sora Fukui, Yuji Ando, Shin’ichi Konomi · ACM Transactions on Knowledge Discovery from Data · 2025
Language learning applications usually estimate the learner’s language knowledge over time to provide personalized practice content for each learner at the optimal timing. However, accurately predicting language knowledge or linguistic skills is much more challenging than math or science knowledge, as many language tasks involve memorization and retrieval. Learners must memorize a large number of words and meanings, which are prone to be forgotten without practice. Although a few studies consider forgetting when modeling learners’ language knowledge, they tend to apply traditional models, consider only partial information about forgetting, and ignore linguistic features that may significantly influence learning and forgetting. This article focuses on modeling and predicting learners’ knowledge by considering their forgetting behavior and linguistic features in language learning. Specifically, we first explore the existence of forgetting behavior and cross-effects in real-world language learning datasets through empirical studies. Based on these, we propose a model for predicting the probability of recalling a word given a learner’s practice history. The model incorporates (1) three types of key information related to forgetting (time-gap, interaction, and word features), (2) question formats, and (3) similarities between words using the attention mechanism. Extensive experiments on two real-world datasets show that the proposed model improves performance compared to baselines. Moreover, the results indicate that combining multiple types of forgetting information and item format improves performance. In addition, we find that incorporating semantic and morphological features, such as word embeddings, to model similarities between words in a learner’s practice history and their effects on memory also improves the model. Our work indicates a potential future research direction for the knowledge tracing task in second language acquisition, which gives more instructive results for enhancing learning and teaching.