Since its creation in 2013 through 2020, the Michigan Virtual Learning Research Institute (MVLRI) at Michigan Virtual published approximately 20 research blogs and 75 research reports. This total does not represent everything published by MVLRI but rather only those publications including original research on K-12 blended and online learning. The nearly 100 resources represent research conducted internally by MVLRI staff, research conducted by partners at universities, colleges, and educational organizations, and covers a vast range of topics including, but not limited to, K-12 online best practices, online student motivation, K-12 blended teaching and professional development, and K-12 special populations.
This body of work is extensive, and while there is tremendous value in each individual publication, there is also value in how that work fits with other similar research and the narrative that emerges from the collective understanding. Toward this end, MVLRI sought to identify, review, and synthesize the original research published in the past 6 years. Again, not every blog or report published via the MVLRI.org website was included, only those containing original research.
Out of the synthesis of resources, 10 main themes emerged. Each theme is presented individually in the interest of brevity. A full reference list is provided at the end of this document noting the resources that contributed to this report.
Resources for inclusion in the synthesis were identified through the MVLRI.org website in the “Publications” and “Blogs” sections. All published blogs and reports were assessed to determine if they included original research. Those that did were included for synthesis. Once the approximately 100 resources containing original research were identified, each blog or report was reviewed and given up to three keyword tags. The following fields were also completed for each of the 100 resources: what we already know about the topic of research, what the resource adds, and implications for policy and practice. Resources were then thematically grouped and keywords were refined and combined. For example, K-12 online program evaluation and quality was combined with K-12 online program policy because although distinct, the themes were related and spoke to many of the same concepts and conclusions.
Once the 10 thematic categories were identified, the resources within that category were reviewed again, both for accuracy in interpretation and to determine its relationship to other resources in the same category. Out of this process, the core findings and practical implications were identified. What is presented below is the synthesized understanding from the original research included. Because of the process, not every finding of every resource could be included, rather resources were reviewed to form a broad understanding of each theme and to identify what MVLRI has contributed and learned in the 6 years since it was formed.
K-12 Online Learner Motivation Core Findings
- Michigan Virtual K-12 online learners matched motivation profiles found in face-to-face courses.
- A majority of K-12 online learners reported that their online course helped them learn better time management.
- Amotivated K-12 online learners, while a minority, struggled more in their online courses than any other motivation profile group and attributed their performance largely to external factors.
- The more K-12 online learners were interested in their online course, the higher their final course grades were on average.
While Michigan Virtual has only conducted or sponsored a limited amount of research on K-12 online learner motivation, there are still some worthwhile findings from that work. Learners in Michigan Virtual courses had motivation profiles that were similar to those found in learners in face to face courses (Wormington, 2017). Specifically, profiles of highly motivated by any means, intrinsically motivated and confident, and amotivated were found in Michigan Virtual courses. In another study on motivation, it was found that motivational profiles differed significantly across enrollment reasons (i.e., credit recovery, elective, or requirement) (Zhang & Lin, 2019).
Traditional performance-focused profiles were not found, which could be attributed to the change in context changing how performance indicators present. The highly motivated by any means, intrinsically motivated and confident, and average motivation group had equally high exam scores throughout the semester and reported similar engagement and self-regulation (Wormington, 2017). Lowes (2014) also found that a majority of learners indicated that the greatest benefit of taking an online course was that it helped them learn to manage their time well. Similarly, in foreign language courses, Lin, Zhang, and Zheng (2017) found that learning strategies, not motivation profiles, predicted learners’ course success. One area that may hold promise in helping both engage learners and develop self-regulatory skills is gaming (Beck, 2017).
It may be that some highly motivated learners with self-regulation skills are able to refine and practice those skills in their online course, while learners who are not as motivated or lack the necessary skills struggle to engage with their courses in a substantial way. Unsurprisingly, Rosenberg and Ranellucci (2017) also found that while there are a variety of motivational factors that influence learners, the more interested in the course learners were, the higher their final course grade tended to be.
Compared to the other motivation profiles, in Wormington (2017), the amotivated group had lower exam scores (by about 15 to 25 points) and reported more negative self-perceptions. This group of amotivated learners was less likely to attribute their poor performance to internal controllable factors (e.g.,effort, use of study strategies, time spent on course), which according to the researcher, suggests the learners felt like they were unable to change the factors influencing their performance (Wormington, 2017). Cozart (2014) suggested that when teachers identify learners who are seemingly withdrawn from the course or amotivated, it may be indicative of a deeper issue. Cozart (2014) also suggested that increasing teacher-learner, as well as learner-learner interactions may help learners manage their emotions more effectively and ultimately increase motivation.
K-12 Online Learner Motivation Practical Implications and Actionable Resources
- Learners who are interested in their courses and motivated to successfully complete their courses tend to have better course outcomes. These learners may even be able to refine their self-regulation skills in their online courses. However, there are a smaller group of amotivated learners who do not do as well in their online courses for a variety of reasons. This group of learners may benefit from increased learner-teacher interactions and self-regulated learning skill building, beyond what is offered in an orientation course. Further research is needed in the area of motivation in Michigan Virtual courses, specifically into the role of self-regulated learning in successful course completion.
- While it is not reasonable to expect all online learners to be interested in their online courses (e.g., courses required for high school graduation, courses to recover credit), online courses that are specifically of interest to learners may provide a space to acclimate to online learning and develop some self-regulated learning skills. Michigan Virtual offers such interest based courses, specifically the Minecraft summer learning course and summer language learning courses.
Beck, D. (2017). Games used in k-12 schools: A research perspective. Michigan Virtual University. https://michiganvirtual.org/research/publications/games-used-in-k-12-schools-a-research-perspective/
Cozart, J. (2014, November 14). New research on affective and motivational factors of learning in online mathematics courses. Michigan Virtual University. https://michiganvirtual.org/blog/new-research-on-affective-and-motivational-factors-of-learning-in-online-mathematics-courses/
Lin, C. H., Zhang, Y., & Zheng, B. (2017). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75-85. https://doi.org/10.1016/j.compedu.2017.05.014
Lowes, S. (2014, September 8). Learning to learn online: A work in progress in helping students to learn self-regulation. Michigan Virtual University. https://michiganvirtual.org/blog/learning-to-learn-online-a-work-in-progress-in-helping-students-to-learn-self-regulation/
Rosenberg, J. & Ranellucci, J. (2017, May 8). Student motivation in online science courses: A path to spending more time on course and higher achievement. Michigan Virtual University. https://michiganvirtual.org/blog/student-motivation-in-online-science-courses-a-path-to-spending-more-time-on-course-and-higher-achievement/
Wormington, S. (2017, March 8). Is there more than one path to success in math? Patterns and predictors of students’ motivation and achievement in online math courses. Michigan Virtual University. https://michiganvirtual.org/blog/is-there-more-than-one-path-to-success-in-math-patterns-and-predictors-of-students-motivation-and-achievement-in-online-math-courses/
Zhang, Y. & Lin, C. H. (2019). Motivational profiles and their correlates among students in virtual school foreign language courses. British Journal of Educational Technology, 51. https://doi.org/10.1111/bjet.12871