Improving reading comprehension with automatically generated cloze item practice
Abstract
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 posttest, d =.58, CI95[.21,.94]. Additionally, Machine Cloze was significantly higher than Human and Random Cloze conditions on post-test, d =.49, CI95[.12,.86] and d =.71, CI95[.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.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Olney, A., Pavlik, P., & Maass, J. (2017). Improving reading comprehension with automatically generated cloze item practice. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10331 LNAI, 262-273. https://doi.org/10.1007/978-3-319-61425-0_22
