Abstract

Faculty have conducted many studies on the relationship between learning mode and student performance but few researchers have evaluated final grades, grade distribution, and pass rates in a sophomore introductory statistics course with a non-traditional student population who self-selected the learning mode from among different course sections. Accordingly, we examined 307 end-of-course grades from four different modes of instruction: (a) online, (b) videosynchronous learning classroom, (c) videosynchronous learning home, and (d) traditional classroom in an introductory statistics course. All data on grades, which included pass rate and grade distribution, were collected from the nine-week January 2019 term. All learning modes used the same text, syllabus, assignments, quizzes, and tests. In this study, learning mode was not significantly related to end-of-course score, final grade distribution, or pass rate. Future researchers should explore the impacts of gender, instructor quality, different term lengths, and the standardized use of textbooks and syllabi on student performance when exploring the impact of learning mode on grades, grade distribution, and pass rates.

Highlights

  • For several years, the option to complete undergraduate coursework and degree programs online has been increasing in U.S institutions (Online Learning Consortium, 2016)

  • Because the literature has shown mixed results regarding equivalence in student outcomes based on modality (Jahng et al, 2007; Nguyen, 2015; Xu & Jaggars, 2013), it is vital to continue to study the influence of learning mode on student performance

  • Differences in student performance (n = 307 final course grades) based on learning mode did not appear to be statistically significant (α = .017) as shown in the ANOVA [F(3, 303) = 1.41, p = .24] conducted on end-of-course scores based on learning mode (Gay et al, 2006)

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Summary

Introduction

The option to complete undergraduate coursework and degree programs online has been increasing in U.S institutions (Online Learning Consortium, 2016). Comparisons between synchronous and asynchronous learning modes can be difficult due to possible confounding variables such as learning management systems, texts, syllabi, and other delivery variables. It is important to ensure that synchronous and asynchronous courses used for comparison share similar structures such as learning management systems, textbooks, syllabi, grade weightings for assignments, tests, quizzes, homework, and other assignments. Controlling for these variables is done through standardized course delivery so that data generated does not reflect the impact of different course delivery aspects

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