Predicting Robust Vocabulary Growth from Measures of Incremental Learning

Abstract

We report a study of incremental learning of new word meanings over multiple episodes. A new method called MESA (Markov Estimation of Semantic Association) tracked this learning through the automated assessment of learner-generated definitions. The multiple word learning episodes varied in the strength of contextual constraint provided by sentences, in the consistency of this constraint, and in the spacing of sentences provided for each trained word. Effects of reading skill were also examined. Results showed that MESA scores increased with each word learning encounter. MESA growth curves were affected by context constraint, spacing of practice, and reading skill. Most important, the accuracy of participant responses (MESA scores) during learning predicted which words would be retained over a 1-week period. These results support the idea that word learning is incremental and that partial gains in knowledge depend on properties of both the context and the learner. The introduction of MESA presents new opportunities to test word-learning theories and the complex factors that affect growth of word knowledge over time and in different contexts.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 55 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.