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Empirical evidence of deep learning in learning communities

Empirical evidence of deep learning in learning communities The purpose of this paper is to investigate whether learning communities (LCs), defined as a cohort of students jointly enrolled in two distinct courses, increase “deep learning” in either or both courses. This study focuses on the impact of learning communities in quantitative courses.Design/methodology/approachThe hypothesis is tested using a unique data set including individual student performance and characteristics collected from students enrolled in an LC of Principles of Microeconomics and Elementary Statistics. The sample also includes students enrolled in each course separately which allows for testing between groups. The final exam in each course contained questions designed specifically to test deep learning. The design facilitates the use of multivariate regression analysis to examine the correlation between learning in communities and deep learning, holding constant other possible elements of student success.FindingsDespite perceptions among the sample student population that learning increases in both courses as a result of the LC format, the empirical evidence does not reveal any statistically significant increase in deep learning as a result of learning in community. However, the sample is more introverted than the average college student which may meaningfully impact the results.Research limitations/implicationsThere are a number of important motivations for implementing an LC program that are not measured here. These include an increased sense of community among students, breadth (rather than depth) of knowledge, and awareness of the interconnectedness of learning across disciplines. However, to the extent that university instructors are motivated to ensure learning in their own discipline, this resource-intensive strategy may not be the most suitable approach in quantitative courses.Originality/valueLearning communities continue to be a popular pedagogical technique and curriculum requirement, particularly at teaching-focused universities. This research offers an empirical approach to measuring one aspect of their value which is typically left to conceptual or qualitative study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Research in Higher Education Emerald Publishing

Empirical evidence of deep learning in learning communities

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2050-7003
DOI
10.1108/jarhe-11-2017-0141
Publisher site
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Abstract

The purpose of this paper is to investigate whether learning communities (LCs), defined as a cohort of students jointly enrolled in two distinct courses, increase “deep learning” in either or both courses. This study focuses on the impact of learning communities in quantitative courses.Design/methodology/approachThe hypothesis is tested using a unique data set including individual student performance and characteristics collected from students enrolled in an LC of Principles of Microeconomics and Elementary Statistics. The sample also includes students enrolled in each course separately which allows for testing between groups. The final exam in each course contained questions designed specifically to test deep learning. The design facilitates the use of multivariate regression analysis to examine the correlation between learning in communities and deep learning, holding constant other possible elements of student success.FindingsDespite perceptions among the sample student population that learning increases in both courses as a result of the LC format, the empirical evidence does not reveal any statistically significant increase in deep learning as a result of learning in community. However, the sample is more introverted than the average college student which may meaningfully impact the results.Research limitations/implicationsThere are a number of important motivations for implementing an LC program that are not measured here. These include an increased sense of community among students, breadth (rather than depth) of knowledge, and awareness of the interconnectedness of learning across disciplines. However, to the extent that university instructors are motivated to ensure learning in their own discipline, this resource-intensive strategy may not be the most suitable approach in quantitative courses.Originality/valueLearning communities continue to be a popular pedagogical technique and curriculum requirement, particularly at teaching-focused universities. This research offers an empirical approach to measuring one aspect of their value which is typically left to conceptual or qualitative study.

Journal

Journal of Applied Research in Higher Education – Emerald Publishing

Published: Jun 19, 2018

Keywords: Deep learning; Knowledge integration; Learning communities; Introversion

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