Electronic Theses and Dissertations

Identifier

6405

Davis Whaley

Date

2019

Document Type

Thesis

Degree Name

Master of Science

Major

Psychology

Concentration

General Psychology

Committee Chair

Andrew Olney

Committee Member

Philip Pavlik

Committee Member

Beth Meisinger

Abstract

This research studied whether computer-generated cloze items using natural language processing methods could promote learning and comprehension of science texts compared to human and random cloze items. Participants recruited from Amazon Mechanical Turk (N = 562) took a pretest on one of three science topics and then read a text on it. Participants then practiced cloze items about the text generated either by a computer (machine), human, or randomly. Cloze items were presented using the MoFaCTS adaptive practice system. After 24 hours participants took a post-test on the text. ANOVA showed a significant effect of cloze type on gain score, and pairwise comparisons found the human conditions had higher gain scores than machine or random conditions. A separate ANOVA on the circulatory system text showed machine had higher gain scores than random. Implications of these findings are discussed.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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