A number of studies have examined factors and characteristics that help online students’ entry, persistence, success and satisfaction in online courses. Three common factors identified as significant for online learning are engagement, self-regulation and motivation. Results regarding the significance of student characteristics such as gender (sex) and major have been mixed. Strategies to aid effective online learning are also provided.
Engagement
Predictors of instructor practices and course activities that engage online students
Bigatel, P., & Edel-Malizia, S. (2018). Predictors of instructor practices and course activities that engage online students. Online Journal of Distance Learning Administration, 21(1).
Bigatel and Edel-Malizia reported survey results from 485 students who had completed online courses through Pennsylvania State University’s World Campus in 2016. The authors asked about student engagement, as well as instructor behaviors, such as instructor-student communication during online discussions. Results indicated that students felt sharing their own knowledge and experiences with the class, using a variety of technologies to communicate, and activities that were meaningful yet challenging were rated the most effective engaging activities. .
Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222. doi: 10.24059/olj.v22i1.109
Martin and Bolliger reported survey results from 155 online graduate students from eight universities in the U.S. on the topic of interaction in online classes. Student-to-instructor interaction and engagement strategies were valued most, with lower importance given to student interaction, although icebreaker and community building activities were liked. Those surveyed also listed a number of engagement strategies they found helpful, such as video lectures, clear instructions, and small group discussions designed for understanding and reflection.
Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses
Martin, F., Wang, C., & Sadaf, A. (2018). Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses. The Internet and Higher Education, 37, 52-65.
Martin et al. analyzed survey data from graduate students (N=188) taking online classes in fall 2016 to identify engagement strategies students found the most helpful. In terms of engagement, learning and instructor presence, students rated prompt instructor feedback and responses to questions the highest, suggesting that those strategies were valued and contributed to their online learning and achievement. Students rated synchronous technology tools and visual interactive syllabi as least helpful in establishing a connection with the instructor or instructor presence.
Actively engaging students in asynchronous online classes
Riggs, S. A., & Linder, K. E. (2016). Actively engaging students in asynchronous online classes.
Riggs and Linder provided suggestions designed to foster active learning in online classes, including innovative use of discussion boards.
Instructional strategies to help online students learn: Feedback from online students
Watson, F. F., Bishop, M. C., & Ferdinand-James, D. (2017). Instructional strategies to help online students learn: Feedback from online students. TechTrends, 61(5). 420-427.
Researchers used data from an existing survey to understand how master’s degree students (N=624) believe online instructors can enhance the learning experience. Many themes were identified, but the top strategies that students said were most important for their instructors to focus on were being available and responsive to students, engaging and interacting with students, and providing prompt feedback.
Self-regulation
Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27(1).
Broadbent and Poon conducted a meta-analysis of 130 studies from 2004-2014 that looked at the relationship between self-regulated learning (SRL) strategies and academic achievement in online course work, ultimately choosing 12 studies that met their criteria. The results of their meta-analysis indicated that student use of SRL strategies was related to their academic achievement. Such SRL strategies included time scheduling or management, metacognition, critical thinking, and persistence, or effort regulation.
Self-regulated learning: The role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course
Cho, M. H., & Heron, M. L. (2015). Self-regulated learning: The role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36(1), 80-99.
Cho and Heron explored how aspects of self-regulated learning such as emotion, motivation, and learning strategies were related to students’ achievement and satisfaction with an online remedial math course. Motivation helped explain achievement somewhat; emotion and motivation were significantly related to course satisfaction.
A review of predictive factors of student success in and satisfaction with online learning
Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23.
Kauffman reviewed the literature in terms of student success and satisfaction; general findings indicated that self-regulated students tended to have more successful online learning outcomes.
Academic persistence of online students in higher education impacted by student progress factors and social media
Lint, A. H. (2013). Academic persistence of online students in higher education impacted by student progress factors and social media. Online Journal of Distance Learning Administration, 16(3).
Lint examined survey results for 170 students and determined that persistence in online course work was negatively influenced by external factors such as work and family commitments and positively influenced by academic integration, or interaction between the student and the instructor, other students, or the institution. GPA, prior online course experience, and online course grades also affected persistence. Lint suggested providing additional instruction or mentoring on time management.
Student locus of control and online course performance: an empirical examination of student success in online management courses
Rogers, P. R. (2015). Student locus of control and online course performance: an empirical examination of student success in online management courses. Academy of Educational Leadership Journal, 19(3), 261-271.
Rogers examined the influence of locus of control on academic performance in online education. It was shown that students with an internal locus of control had better academic performance in online courses because such individuals tend to be more analytical, organized, and task-oriented, as well as more active in seeking information.
Motivation
Analyzing the influences of course design and gender on online participation
Anthony, K. V. (2012). Analyzing the influences of course design and gender on online participation. Online Journal of Distance Learning Administration, 15(3).
Anthony explored components that affect students’ participation in their online class; his small sample study indicated that course design had a significant effect on participation, as the level of participation decreased significantly when critical assignments were due. Anthony suggested strategies to prevent this issue, such as tying aspects of those assignments to participation.
The role of student characteristics in predicting retention in online courses
Cochran, J. D., Campbell, S. M., Baker, H. M., & Leeds, E. M. (2014). The role of student characteristics in predicting retention in online courses. Research in Higher Education, 55(1), 27-48.
Using binary logistic regression, the authors explored student characteristics for over 2,300 students to evaluate persistence in online courses. Results indicated that overall, class standing or academic level and cumulative GPA were significantly related to retention. For certain subgroups, prior withdrawal experience and gender were also related to retention. Further implications and pedagogical strategies are provided.
Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics
Dunn, K. (2014). Why wait? The influence of academic self-regulation, intrinsic motivation, and statistics anxiety on procrastination in online statistics. Innovative Higher Education, 39(1), 33-44.
Using self-determination theory, Dunn examined whether the academic self-regulation, statistics or math anxiety, and intrinsic motivation of over 100 graduate students in an online statistics course significantly influenced their procrastination in online learning. Results indicated that higher levels of academic self-regulation and intrinsic motivation decreased procrastination, and higher levels of math anxiety increased procrastination.
A comparative study on the motivation and attitudes of language learners of online distance and traditional in-classroom education
Genc, G., Kulusakli, E., & Aydin, S. (2016). A comparative study on the motivation and attitudes of language learners of online distance and traditional in-classroom education. Turkish Online Journal of Distance Education, 17(4), 63-75.
The authors surveyed over 450 students who had taken an English foreign language course in either a traditional classroom or online and found that the results indicated no significant difference in attitude and motivation for students enrolled in those modalities. For this sample, female students were more motivated than males, and male students had more positive attitudes.
Structural equation modeling towards online learning readiness, academic motivations, and perceived learning
Horzum, M. B., Kaymak, Z. D., & Gungoren, O. C. (2015). Structural equation modeling towards online learning readiness, academic motivations, and perceived learning. Educational Sciences: Theory and Practice, 15(3), 759-770.
Horzum et al. surveyed over 400 students enrolled in online course work in order to explore the relationship between online-learning readiness level, academic motivation, and perceived learning. The results showed that academic motivation fully mediated the relationship between online-learning readiness and perceived learning; the authors emphasized the importance of enhancing online learning students’ motivations.
What motivates students in the online communication classroom? An exploration of self-determination theory
Jacobi, L. (2018). What motivates students in the online communication classroom? An exploration of self-determination theory. Journal of Educators Online, 15(2)
Jacobi interviewed 25 students with prior online class experience and ten online faculty to ask what motivates students in online classes. Strategies that provide autonomy, a sense of belonging, and perceived competence were factors that motivated students. Examples include the flexibility and convenience of the online format, providing relevant content with a rationale for its inclusion and related assignments, and discussion forums with clear guidelines, structure, feedback, and the opportunity for students to post their thoughts and relevant insights respectfully.
What characteristics of college students influence their decisions to select online courses?
Mann, J. T., & Henneberry, S. R. (2012). What characteristics of college students influence their decisions to select online courses? Online Journal of Distance Learning Administration, 15(4).
Mann and Henneberry reviewed survey responses for about 2,700 students in order to explore characteristics of college students that influence online course enrollment. Results indicated that major, specifically business, class standing, and residency status influenced the decision to take online courses.
From the periphery to prominence: An examination of the changing profile of online students in American higher education
Ortagus, J. C. (2017). From the periphery to prominence: An examination of the changing profile of online students in American higher education. The Internet and Higher Education, 32, 47-57.
Ortagus explored student characteristics as they relate to their decision to take online courses. The study showed that individuals who needed residential education (e.g., full-time employee, parent) were more likely to enroll in online courses, whereas economically and socially disadvantaged students were typically less likely to engage in online education.
Which STEM majors enroll in online courses, and why should we care? The impact of ethnicity, gender, and non-traditional student characteristics
Wladis, C., Hachey, A. C., & Conway, K. (2015). Which STEM majors enroll in online courses, and why should we care? The impact of ethnicity, gender, and non-traditional student characteristics. Computers & Education, 87, 285-308.
Wladis et al. explored student characteristics that may influence enrollment to online courses, specifically in STEM fields. The study showed that, when controlled for level of academic preparation, SES, citizenship and English-as-second-language (ESL) status, Hispanic and Black students were significantly less likely and female students were more likely to take online courses. In addition, nontraditional student characteristics strongly increased the likelihood of online course enrollment.
Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences
Yoo, S.J., & Huang, W.D. (2013). Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences. The Journal of Continuing Higher Education, 61(3), 151-164.
Yoo and Huang explored specific motivational factors that contributed to students’ selection of online programs. Results showed that intrinsic motivation, short-term extrinsic motivation, long-term extrinsic motivation, and technological willingness significantly influenced the selection of online programs. In addition, it was found that female students exhibited stronger intrinsic motivation for online learning.
A case study of American and Chinese college students’ motivation differences in online learning environment
Zhao, C., & Mei, Z. (2016). A case study of American and Chinese college students’ motivation differences in online learning environment. Journal of Education and Learning, 5(4), 104-112.
Zhao and Mei explored whether online students’ motivation in learning would be influenced by students’ demographics (e.g., nationality, gender). Study results indicated a significant online learning motivation difference between U.S. and Chinese online learners. Gender, employment status, and marital status also affected student motivation.
Success
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.
Identifying and addressing the mental health needs of online students in higher education
Barr, B. (2014). Identifying and addressing the mental health needs of online students in higher education. Online Journal of Distance Learning Administration, 17(2).
The author cited several studies concerning how student mental health may affect academic performance and suggested that with less visual interaction between instructors and students in an online course, instructors should be sensitive to a significant reduction in performance, postings or responses from individual students, including late or missing assignments, as those may be indications of mental health problems. Ways to provide appropriate interventions are also provided.
An investigation into web-based presentations of institutional online learning orientations
McGowan, V. F. (2018). An investigation into web-based presentations of institutional online learning orientations. Journal of Educators Online, 15(2), n2.
Researchers analyzed webpages of a stratified, strategic sample of 65 higher education institutions to identify themes and regular practices when providing an orientation to online learning. Although there were many differences in the orientations, the researchers believed the content analyzed suggests most institutions attempt to go beyond simply displaying the online environment, but rather to assist students with best practices. They concluded that effective orientations should include modules on using the online environment, technical requirements, success strategies, help topics, and institution-specific information.
Student success factors in graduate psychology professional programs
Newhouse, N. (2016). Student success factors in graduate psychology professional programs. Online Learning, 20(1), 70-91.
Newhouse and Cerniak examined factors contributing to online graduate students’ success. Data showed that being placed on academic probation at any time during enrollment was associated with both a lower likelihood of graduation and a lower final program GPA. Stopping out and prior graduate school experience were associated with a lower probability of graduation, while failing any course during matriculation was linked to final program GPA.
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, 19(3).
Shaw et al. examined ways to reduce attrition of online students. Researchers recommended that the following steps may significantly enhance retention: students should develop college readiness and technology readiness skills and articulate their motivation for engaging in an online program during the enrollment process; and faculty should develop mentoring skills to support students’ progress, foster quality student-faculty relationships, and provide regular feedback to students.
Comparison of motivation and learning outcome achievement in shortened, online summer courses versus their full-term counterparts
Simunich, B. (2016). Comparison of motivation and learning outcome achievement in shortened, online summer courses versus their full-term counterparts. Summer Academe: A Journal of Higher Education, 10.
Simunich explored whether the course length/length of course term influenced student achievement and motivation. Study results indicated that there were no significant differences in achievement and motivation among students in online classes based on the course length/length of course term.
Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning
Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302-323.
Wang et al. examined the relationship between students’ personal and academic characteristics and their academic achievement. Results showed that students with previous online learning experiences had better learning strategies, higher levels of motivation in their online course, higher technology self-efficacy, higher satisfaction in the course, and higher grades than those who did not have prior experiences in an online setting.