Online Learning Persistence and Academic Achievement (bibtex)
by Ying Fang, Yonghong Jade Xu, Benjamin Nye, Arthur Graesser, Philip Pavlik, Xiangen Hu
Abstract:
Student persistence in online learning environments has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.). The current examines student persistence in an adaptive learning environment, ALEKS (Assessment and LEarning in Knowledge Spaces). Specifically, the study explores the relationship between students' academic achievement and their persistence during learning. By using archived data that included their math learning log data and performance on two standardized tests, we first explored student learning behavior patterns with regard to their persistence during learning. 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. No significant differences were observed between academic achievement and the different learning patterns. We interpret this result in addition to a preliminary exploration of topic mastery trends, to suggest that wheel-spinning" behaviors coexist with persistence, and is ultimately not beneficial to learning.
Reference:
Online Learning Persistence and Academic Achievement (Ying Fang, Yonghong Jade Xu, Benjamin Nye, Arthur Graesser, Philip Pavlik, Xiangen Hu), In Proceedings of Educational Data Mining (EDM) 2017, EDM 2017, 2017.
Bibtex Entry:
@inproceedings{fang_online_2017,
	address = {Wuhan, China},
	title = {Online {Learning} {Persistence} and {Academic} {Achievement}},
	url = {http://educationaldatamining.org/EDM2017/proc_files/papers/paper_114.pdf},
	abstract = {Student persistence in online learning environments has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.). The current examines student persistence in an adaptive learning environment, ALEKS (Assessment and LEarning in Knowledge Spaces). Specifically, the study explores the relationship between students' academic achievement and their persistence during learning. By using archived data that included their math learning log data and performance on two standardized tests, we first explored student learning behavior patterns with regard to their persistence during learning. 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. No significant differences were observed between academic achievement and the different learning patterns. We interpret this result in addition to a preliminary exploration of topic mastery trends, to suggest that wheel-spinning" behaviors coexist with persistence, and is ultimately not beneficial to learning.},
	booktitle = {Proceedings of {Educational} {Data} {Mining} ({EDM}) 2017},
	publisher = {EDM 2017},
	author = {Fang, Ying and Xu, Yonghong Jade and Nye, Benjamin and Graesser, Arthur and Pavlik, Philip and Hu, Xiangen},
	month = jun,
	year = {2017},
	keywords = {Learning Sciences},
	pages = {312 -- 317}
}
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