Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge

We show how the novel use of a semantic representation based on Osgood's semantic differential scales can lead to effective features in predicting short- and long-term learning in students using a vocabulary learning system. Previous studies in students' intermediate knowledge states during vocabulary acquisition did not provide much information on which semantic knowledge students gained during word learning practice. Moreover, these studies relied on human ratings to evaluate the students' responses. To solve this problem, we propose a semantic representation for words based on Osgood's semantic decomposition of vocabulary [16]. To demonstrate our method can effectively represent students' knowledge in vocabulary acquisition, we build models for predicting the student's short-term vocabulary acquisition and long-term retention. We compare the effectiveness of our Osgood-based semantic representation to that provided by Word2Vec neural word embedding [13], and find that prediction models using features based on Osgood scale-based scores (OSG) perform better than the baseline and are comparable in accuracy to those using Word2Vec score-based models (W2V). By using more interpretable Osgood-based scales, our study results can help with better understanding of students' ongoing learning states and designing personalized learning systems that can address an individual's weak points in vocabulary acquisition. [For the full proceedings, see ED596512.]