Detecting Learning Strategies with Analytics: Links with Self-Reported Measures and Academic Performance

Detecting Learning Strategies with Analytics: Links with Self-Reported Measures and Academic Performance. Gaševic, D., Jovanovic, J., Pardo, A., & Dawson, S. Journal of Learning Analytics, 4(2):113–128, 2017. Publisher: Society for Learning Analytics Research

Paper abstract bibtex

The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N = 144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.

@article{gasevic_detecting_2017,
	title = {Detecting {Learning} {Strategies} with {Analytics}: {Links} with {Self}-{Reported} {Measures} and {Academic} {Performance}},
	volume = {4},
	issn = {1929-7750},
	shorttitle = {Detecting {Learning} {Strategies} with {Analytics}},
	url = {https://eric.ed.gov/?q=detecting+learning+strategies+with+analytics&id=EJ1149109},
	abstract = {The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N = 144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface	strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.},
	language = {en},
	number = {2},
	urldate = {2020-05-25},
	journal = {Journal of Learning Analytics},
	author = {Gaševic, Dragan and Jovanovic, Jelena and Pardo, Abelardo and Dawson, Shane},
	year = {2017},
	note = {Publisher: Society for Learning Analytics Research},
	keywords = {Flipped Classroom, LMS Traces, learning analytics},
	pages = {113--128},
}

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