A great deal of research is available about online learning outcomes in such areas as achievement, persistence, retention, and satisfaction. Listed below are citations for numerous research studies in those areas, including meta-analyses.
Influence of satisfaction and preparedness on
online students' feelings of anxiety
Abdous, M. (2019). Influence of satisfaction and preparedness on online students' feelings of anxiety. The Internet in Higher Education, 41, 34-44.
Abdous analyzed survey data from over 4,000 students at a public university enrolled in online classes, researching whether demographic factors, prior experience with online classes, the online orientation class, and readiness after completing the online orientation affected their feelings of anxiety. Findings indicated that gender, course load, prior online experience, and feelings of preparedness after completing the online orientation significantly predicted whether students felt anxious.
Does inducing students to schedule lecture watching in online classes improve their academic performance?
An experimental analysis of a time management intervention
Baker, R., Evans, B., Li, Q., & Cung, B. (2019). Does inducing students to schedule lecture watching in online classes improve their academic performance? An experimental analysis of a time management intervention. Research in Higher Education, 60(4), 521-552.
Using data for 160 students enrolled in a 5-week online STEM class, Baker et al. created a randomized trial in which 76 students received an email from the instructor and an online scheduler the first two weeks of class suggesting they schedule their lecture video watching. The control group received an email from the instructor with a short technology survey instead. Students who scheduled their video lecture watching received higher scores than those in the control group the first week; achievement effects were smaller the second week of the intervention. There was no statistically significant difference in final course grades, perhaps because the intervention occurred in the first two weeks. Students self-reporting lower time management skills most benefited from the scheduling encouragement.
A meta-analysis of blended learning and technology use in higher education:
From the general to the applied
Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87-122.
Bernard et al. provided a meta-analysis of 96 studies of blended learning research based on 10,800 students and 117 effect sizes. Their findings indicated that achievement in blended classes was slightly better than in a traditional classroom, noting that interactivities and type of computer support may also increase achievement. The authors also provided detailed information for conducting meta-analyses of distance education research studies.
Examination of attributes that promote student satisfaction
Bickle, M. C., Rucker, R. D., & Burnsed, K. A. (2019). Online learning: Examination of attributes that promote student satisfaction. Online Journal of Distance Learning Administration, 22(1), n1.
Researchers surveyed students (N = 228) in several online classes at a major university to identify variables that contribute to the perception of a quality course. The study was developed using Social Presence Theory as a framework, and analyses suggested that student beliefs were related to engagement with the instructor, connections with other students and the ability to openly express their opinions.
Digging Deeper into the Data:
The Role of Gateway Courses in Online Student Retention
Bloemer, W., Swan, K., Day, S., & Bogle, L. (2018). Digging Deeper into the Data: The Role of Gateway Courses in Online Student Retention. Online Learning, 22(4), 109-127.
Researchers examined student outcomes from 549 courses at a Midwestern university identifying prior term GPA and previous withdrawals as the highest predictors of student success. Then they compared the predicted outcomes to the actual outcomes to determine the “gap” for each course and identify courses that may impede student success. Through a set of worked examples, the authors show how to achieve this analysis at most institutions in order to improve courses and student achievement.
A comparison of online and face-to-face approaches
to teaching introduction to American government
Bolsen, T., Evans, M., & Fleming, A. M. (2016). A comparison of online and face-to-face approaches to teaching introduction to American government. Journal of Political Science Education, 12(3), 302-317.
The authors compared achievement and engagement results for over 1,500 students enrolled in a core political science course in traditional lecture, breakout, blended and online sections that also used different textbook formats. Results included higher levels of engagement in the online sections and higher levels of political knowledge in the online and blended formats than in the traditional and breakout sections, although students in the online course sections had a higher drop rate than that of the other formats.
Interactive learning online at public universities:
Evidence from a six-campus randomized trial
Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2014). Interactive learning online at public universities: Evidence from a six-campus randomized trial. Journal of Policy Analysis and Management, 33(1), 94-111.
Bowen et al. randomly assigned 605 students at six public institutions to a hybrid or face-to-face introductory statistics course and found no significant difference in learning outcomes and achievement in the hybrid and face-to-face classes.
A large sample comparison of grade based student learning outcomes
in online vs. face-to-face courses
Cavanaugh, J., & Jacquemin, S. (2015). A large sample comparison of grade based student learning outcomes in online vs. face-to-face courses. Online Learning Journal, 19(2).
Cavanaugh and Jacquemin evaluated data for over 140,000 students enrolled in about 6,000 courses taught by the same instructors online and face to face at a large Midwestern four-year public institution from 2010 to 2013, reporting no statistically significant difference between grades earned in those online and face-to-face sections based on student performance.
How people learn in an asynchronous online learning environment:
The relationships between graduate students’ learning strategies and learning satisfaction
Choi, B. (2016). How people learn in an asynchronous online learning environment: The relationships between graduate students’ learning strategies and learning satisfaction| Comment apprennent les gens dans un environnement d’apprentissage en ligne asynchrone. Canadian Journal of Learning and Technology/La revue canadienne de l’apprentissage et de la technologie, 42(1).
Among various learning strategies, metacognitive strategy (i.e., awareness, knowledge, and control of cognition, including planning, monitoring and regulating activities) and peer learning (i.e., students’ collaboration with peers) had a significant relationship with the students’ satisfaction with online learning.
A modest proposal:
An objective method to evaluate delivery options
Farrow, S. A., & Matin, C. (2015). A modest proposal: An objective method to evaluate delivery options. Distance Learning, 12(3), 1-7.
Tailoring delivery options based on learning goals in the online setting was suggested by the authors, including a diverse list of how to evaluate the delivery option based on goals.
How do online course design features
influence student performance?
Jaggars, S. S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270-284.
Jaggars and Xu explored factors that influence student performance in online courses, and their research results indicated that the quality of interpersonal interactions within a course related significantly to student grades. The authors hypothesized that frequent and effective student-instructor interaction created an online environment encouraging students to commit to online learning.
Feedback in technology‐based instruction:
Lefevre, D., & Cox, B. (2016). Feedback in technology‐based instruction: Learner preferences. British Journal of Educational Technology, 47(2), 248-256.
Lefevre and Cox studied the effectiveness of feedback in the online setting and asserted the importance of providing feedback based on learner preference such as simple to elaborative feedback on multiple-choice questions. Findings suggest that elaborative feedback would be helpful even if the correct response were chosen.
Trends in the design
of e-learning and online learning
Lister, M. (2014). Trends in the design of e-learning and online learning. Journal of Online Learning and Teaching, 10(4), 671-680.
The author conducted a literature review on the design of online learning and suggested components critical for student learning, including course structure, content presentation, collaboration and interaction, and timely feedback.
Is a quality course a worthy course?
Designing for value and worth in online courses
Youger, R. E., & Ahern, T. C. (2015). Is a quality course a worthy course? Designing for value and worth in online courses. Online Journal of Distance Learning Administration, 18(1).
In this article, Youger and Ahern reviewed Quality Matters standards and suggested blending quality with value by creating customizable learning experiences to increase the worth and value of a course.
Relationships among faculty training, faculty degree, faculty longevity,
and student satisfaction in online higher education
Kane, R. T., Shaw, M., Pang, S., Salley, W., & Snider, J. B. (2015). Relationships among faculty training, faculty degree, faculty longevity, and student satisfaction in online higher education. Online Journal of Distance Learning Administration, 18(4).
Kane et al. used hierarchical linear modeling (HLM) to evaluate nearly 1,200 course evaluations for 75 faculty using seven terms of data and concluded that student satisfaction was positively related to faculty longevity, or the length of time teaching online.
A structural equation model of predictors of
online learners’ engagement and satisfaction
Kucuk, S., & Richardson, J. C. (2019). A structural equation model of predictors of online learners’ engagement and satisfaction. Online Learning, 23(2), 196-216.
Kucuk and Richardson analyzed survey data collected from 123 graduate students enrolled in an online graduate program at a Midwestern university about their satisfaction, engagement, and the community of inquiry (COI) teaching, social, and cognitive presence elements present in their online classes. Survey results indicated that two components of COI – teaching and cognitive presence – along with emotional, behavioral and cognitive engagement significantly predicted student satisfaction, explaining the majority (88%) of the variance in satisfaction. In this study, the most influential determinant of satisfaction was teaching presence.
Predicting successful completion using student delay indicators
in undergraduate self-paced online courses
Lim, J. (2016). Predicting successful completion using student delay indicators in undergraduate self-paced online courses. Distance Education, 37(3), 317-332.
Lim evaluated delay patterns and persistence data for over 200 students who had enrolled in online self-paced course work over a 2-year period at a private university in the U.S.; of the three variables studied, time to first submission, time between assignments, and time to complete, results indicated that the average number of days separating assignment submissions best predicted course grades and persistence.
Predictors of course outcomes:
Early indicators of delay in online classrooms
McElroy, B. W., & Lubich, B. H. (2013). Predictors of course outcomes: Early indicators of delay in online classrooms. Distance Education, 34(1), 84-96.
McElroy and Lubich studied registration and initial posting patterns for 255 graduate student enrollments in an online management accounting course from a 3-year period and found a negative relationship between course grades and the initial posting date, indicating that those who procrastinated and took longer to submit their first posting received lower course grades. The authors suggested that instructors should emphasize the importance of timely submissions to students and carefully observe initial patterns to serve as an early alert system with individual students.
The correlation of self-regulation and motivation with
retention and attrition in distance education
Peck, L., Stefaniak, J. E., & Shah, S. J. (2018). The correlation of self-regulation and motivation with retention and attrition in distance education. Quarterly Review of Distance Education, 19(3), 1-80.
Students either currently enrolled in (N = 91) or recently dropped out of (N = 22) an online distance program filled out a motivation and learning questionnaire. These scores were used to explore the relationships between motivation and retention, and the authors found that self-efficacy (a subset of the motivation scale) was positively related to retention in the online program. The authors provide ideas for instructional designers and instructors to improve motivation within their online classes.
In search of higher persistence rates
in distance education online programs
Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Internet and Higher Education, 6(1), 1-16.
Rovai summarized campus student models of attrition postulated by Tinto, Bean and Metzger and provided a suggested combined framework for persistence in distance education programs by incorporating factors relevant to distance education.
An evaluation of student outcomes
by course duration in online higher education
Shaw, M., Chametzky, B., Burrus, S. W., & Walters, K. J. (2013). An evaluation of student outcomes by course duration in online higher education. Online Journal of Distance Learning Administration, 16(4).
The authors compared student outcomes for 115 undergraduate students enrolled in 8- and 16-week sections of an abnormal psychology course that had the same content, assessments, and instructor; their findings indicated no significant difference in achievement between the course group/modalities, including the number of assignments submitted and final grade.
Factors that influence student attrition
in online courses
Shaw, M., Burrus, S., & Ferguson, K. (2016). Factors that influence student attrition in online courses. Online Journal of Distance Learning Administration, 16(4).
Shaw et al. evaluated student readiness scores for 2,400 new graduate students who took the assessment prior to their first online class. Findings suggested that attributes such as procrastination and physical and verbal learning styles were positively associated with the likelihood of dropping out, or attrition, whereas technology mastery, clarity in degree pursuit, and typing speed were negatively associated with withdrawing. Students with low scores on the readiness at-risk factors who received additional outreach exhibited higher levels of persistence (retention was 11% higher for that group), more on-time course work, and better grades than a control group with low scores on the at-risk factors that did not receive any interventions.
Assessing readiness for online education –
research models for identifying students at risk
Wladis, C., Conway, K. M., & Hachey, A. C. (2016). Assessing readiness for online education – research models for identifying students at risk. Online Learning, 20(3), 97-109.
Wladis, Conway, and Hachey used multilevel modeling to explore the relationships between student characteristics, achievement and persistence in online course work for over 9,600 students enrolled in the City University New York system and concluded that those students with at least one child younger than the age of 6 were predicted to complete online course work with a C- or above at a lower rate, when compared to their face-to-face or traditional class outcomes. Their findings also indicated that compared to outcomes for traditional classes, students born in the U.S. also had higher risk online than students born elsewhere.
The impact of online learning on students’ course outcomes:
Evidence from a large community and technical college system
Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46-57.
Xu and Jaggars analyzed achievement for over 18,000 students enrolled in Washington state’s community and technical college system during a 5-year time period and concluded that persistence and course grades were better for those enrolled face-to-face than online.