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IEEE Final Year Project Topic for CSE

Base Paper Title

Predicting Contextual Informativeness for Vocabulary Learning

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IEEE Project Abstract

Vocabulary knowledge is essential to educational progress. High quality vocabulary instruction requires supportive contextual examples to teach word meaning and proper usage. Identifying such contexts by hand for a large number of words can be difficult. In this work, we take a statistical learning approach to engineer a system that predicts informativeness of a context for target words that span the range of difficulty from middle school to college level. Our database (released open source) includes 1,000 hand-selected words associated with approximately 70,000 contextual examples gathered from the Internet. Our training data included each context rated by 10 individuals on a four-point informativeness scale. We process the text of each context into a novel collection of approximately 600 numerical features that captures diverse linguistic information. We then fit a nonparametric regression model using Random Forests and compute out-of-sample prediction performance using cross-validation. Our system performs well enough that it can replace a human judge: for a target word not found in our dataset, we can provide curated contexts to a student learner such that most of the contexts (54 percent) feature rich contextual clues and confusing contexts are rare ( <; 1 percent). The quality of our curated contexts was validated by an independent panel of high school language arts teachers.

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