Electronic Theses and Dissertations

Identifier

4868

Author

Ying Fang

Date

2017

Date of Award

4-21-2017

Document Type

Thesis

Degree Name

Master of Science

Major

Psychology

Concentration

General Psychology

Committee Chair

Xiangen Hu

Committee Member

Arthur Graesser

Committee Member

Philip Pavlik

Committee Member

Yonghong Jade Xu

Abstract

Students’ online persistence has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.), and was investigated as a dependent variable with predicting variables such as motivation, engagement, economical support, etc. This study examines students’ persistence in an online adaptive learning environment called ALEKS, and the association between students’ academic achievement and persistence. With archived data that included students’ online math learning log and standardized tests scores, we first explored students’ learning behavior patterns with regard to how persistent they were while learning with ALEKS. Three variables indicating three levels of persistence were created and used for cluster analysis. Hierarchical clustering analysis identified three distinctive patterns of persistence-related learning behaviors: (1) High persistence and rare topic shifting; (2) Low persistence and frequent topic shifting; and (3) Moderate persistence and moderate topic shifting. We further explored the association between persistence and academic achievement. Analysis of covariance (ANCOVA) indicated no significant difference in academic achievement between students with different learning patterns. This result seems to suggest that “wheel-spinning” coexists with persistence and is not beneficial to learning. This finding also suggests that ALEKS, and other intelligent learning environments, would benefit from a mechanism that determines when a student fails that takes into account wheel-spinning behaviors. This would allow for a more appropriate intervention to be provided to learners in a timely manner.

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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