Improving Reading Comprehension with Automatically Generated Cloze Item Practice
| Autor: | Andrew Olney, Jaclyn K. Maass, Philip I. Pavlik |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
060201 languages & linguistics
business.industry Computer science 05 social sciences 050301 education 06 humanities and the arts Retention interval Scheduling system computer.software_genre Reading comprehension 0602 languages and literature Artificial intelligence business 0503 education computer Natural language processing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319614243 AIED |
| Popis: | This study investigated the effect of cloze item practice on reading comprehension, where cloze items were either created by humans, by machine using natural language processing techniques, or randomly. Participants from Amazon Mechanical Turk (\(N=302\)) took a pre-test, read a text, and took part in one of five conditions, Do-Nothing, Re-Read, Human Cloze, Machine Cloze, or Random Cloze, followed by a 24-hour retention interval and post-test. Participants used the MoFaCTS system [27], which in cloze conditions presented items adaptively based on individual success with each item. Analysis revealed that only Machine Cloze was significantly higher than the Do-Nothing condition on post-test, \(d=.58\), \(CI_{95} [.21,.94]\). Additionally, Machine Cloze was significantly higher than Human and Random Cloze conditions on post-test, \(d=.49\), \(CI_{95} [.12,.86]\) and \(d=.71\), \(CI_{95} [.34,1.09]\) respectively. These results suggest that Machine Cloze items generated using natural language processing techniques are effective for enhancing reading comprehension when delivered by an adaptive practice scheduling system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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