Accuracy feedback improves word learning from context: evidence from a meaning-generation task
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The present study asked whether accuracy feedback on a meaning generation task would lead to improved contextual word learning (CWL). Active generation can facilitate learning by increasing task engagement and memory retrieval, which strengthens new word representations. However, forced generation results in increased errors, which can be detrimental for learning if they are not corrected. The goal of this study was to determine whether immediate feedback on response accuracy would ameliorate this risk. The study was conducted using an intelligent tutoring system, which presents target words in multiple contexts and prompts users to generate a target word meaning after each context. One group of participants (feedback group) received immediate feedback based on Markov Estimation of Semantic Association (MESA), which estimates the distance between the learner response and the target word meaning. The control group did not receive feedback. Results from conventional (pre/post-test) measures showed greater gains in accuracy and confidence for the feedback group. Moreover, when contextual support was decreased mid-way through the training (from trial 3 to trial 4), MESA measures showed a corresponding drop in accuracy, but only for the No-Feedback group. These findings suggest that accuracy feedback can improve outcomes in CWL, particularly when there is an increased risk of errors. This strengthens the case for meaning generation as a tool to build high-quality lexical representations.
KeywordsLearning Reading Vocabulary Context
We thank Theresa Maglio, Christina Yarbrough, Elizabeth Dlouhy and Jonathan McNair for their assistance with stimulus development, and recruitment and running of participants. We thank MacKenzie Bechtel-Hall, Yu Meng, and John Grese for their contributions to the design and implementation of the ITS. This research was funded by a grant from the Language & Literacy Initiative at Georgia State. It was also funded in part by an IES grant (R305A140647, Collins-Thompson & Frishkoff) and by a gift from Microsoft Research.
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