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ERIC Number: EJ1174702
Record Type: Journal
Publication Date: 2018
Pages: 14
Abstractor: As Provided
ISSN: ISSN-1939-1382
Predicting Contextual Informativeness for Vocabulary Learning
Kapelner, Adam; Soterwood, Jeanine; Nessaiver, Shalev; Adlof, Suzanne
IEEE Transactions on Learning Technologies, v11 n1 p13-26 Jan-Mar 2018
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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Publication Type: Journal Articles; Reports - Research
Education Level: High Schools
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
Identifiers - Location: South Carolina; Connecticut
IES Funded: Yes
Grant or Contract Numbers: R305A130467