Helping Children Learn Vocabulary during Computer Assisted Oral Reading

Greg Aist, Carnegie Mellon University

Vocabulary is fundamental to reading. As elementary students cross over from learning to read into reading to learn, vocabulary knowledge becomes increasingly important. The massive amount of vocabulary a student must learn precludes large amounts of time spent on any single word, except perhaps for some words that the student will read and write many times over the course of a lifetime. Therefore students must learn vocabulary from text. Project LISTEN’s Reading Tutor listens to children read aloud, and helps them learn to read. The Reading Tutor shows the child a story one sentence at a time, listens to the child read all or part of the sentence out loud, and responds with help in recorded human voices. When the Reading Tutor has heard the student read every content word, the Reading Tutor shows the next sentence. Besides reading, the student may click Go to see the next sentence, Back to move back, on a word or on Help to hear the word read by the Tutor or get other help, or Goodbye to log out. To learn new words from interacting with the Reading Tutor, a student must: 1. spend time reading, 2. read new material hard enough to have new words, and 3. learn the meaning of new words when encountered. We excluded the first factor -- time on task -- as outside the scope of this thesis. We addressed the second factor by modifying the Reading Tutor to take turns picking stories with students, to expose students to more new material than they would have read if they picked all the stories themselves. We addressed the third factor by designing, implementing, and evaluating ways to augment stories with extra help -- such as synonyms or glossary definitions -- to make the most of encounters with novel words. Results were as follows: Taking turns picking resulted in students reading more new material than with a previous version of the Reading Tutor that allowed only the student to pick. Augmenting stories with WordNet-derived synonyms made most sense for single-sense words, and resulted in improved learning on rare words. We discuss these results and what remains to examine.


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