In recent years, the landscape of education has undergone a significant transformation, with online learning becoming increasingly prevalent. Like many other states, Michigan has witnessed a surge in virtual education, with approximately 14% of K-12 students engaging in at least one virtual course during the 2021-2022 academic year, boasting a pass rate of 69% (Freidhoff, 2023). However, while the accessibility and flexibility of online learning are undeniable, ensuring student success in this domain presents its own set of challenges.
The Crucial Role of Pacing
Research has shed light on one key aspect that significantly influences student outcomes in online courses—pacing. Pacing refers to how students progress through course material over time, and it has emerged as a crucial determinant of success (DeBruler, 2021; Michigan Virtual Learning Research Institute, 2019). Studies indicate that students’ early engagement, such as submitting assignments within the first week, correlates positively with final grades, suggesting a strong link between pacing and engagement (Zweig, 2023).
Consistency in pacing throughout the course is also pivotal. Students who maintain a steady pace are more likely to succeed compared to those who exhibit erratic pacing behaviors, such as cramming assignments toward the end of the course (DeBruler, 2021). The significance of pacing becomes even more pronounced in online courses that lack firm deadlines, where students have the flexibility to progress at their own pace (Martin & Whitmer, 2016; Wakeling & Robertson, 2017).
Guidance and Structure: Pacing Guides
To assist students in navigating the challenges of pacing, some online course providers, like Michigan Virtual, provide pacing guides. These guides offer a structured roadmap of assignments and activities for each week or sequence, serving as a benchmark for students to evaluate their progress. While not mandatory, adhering to these pacing guides can significantly aid students in managing their workload, particularly in the absence of strict deadlines (DeBruler, 2021).
Understanding Engagement: Learning Trajectories and Sequencing
Delving deeper into the realm of online learning, growth mixture modeling (GMM) has revealed various learning trajectory profiles in K-12 mathematics courses. These profiles offer valuable insights into how students engage with course material and how their engagement patterns are intertwined with pacing and overall performance (Kwon, 2018; Kwon & DeBruler, 2019).
The first profile illustrates nearly linear, on-pace progression, prevalent in advanced placement (AP) courses characterized by a cohort model. This underscores the importance of structured pacing and collective progress (Kwon, 2018).
Conversely, the second profile depicts a steep increase in learner scores near the end of the semester, often unattainable for many learners, particularly those in foundation courses like Algebra and Geometry, or courses for credit recovery (Kwon & DeBruler, 2019).
The third profile portrays minimal progress over the semester, indicative of disengagement leading to eventual withdrawal from courses (Kwon, 2017).
Lastly, strong early achievement signifies a group with a linear progression from the beginning to the end of the semester, often characterized by a surge in time investment towards the final weeks (Kwon & DeBruler, 2019).
In essence, these learning trajectory profiles serve as a testament to the intricate dance between engagement patterns and course pacing in online learning environments. By recognizing the pivotal role of pacing in shaping students’ online learning journeys, educators can implement strategies to scaffold pacing, provide timely interventions, and foster a supportive learning environment conducive to student success.
MVLRI also investigated student course sequencing and its relationship to student achievement. This study sheds light on the impact of assignment sequencing on student performance. It reveals a significant negative correlation between submitting assignments out of order and final grades. Specifically, students who adhered to the prescribed sequence achieved final grades averaging 9.5 points higher than their counterparts who did not (Cuccolo & DeBruler, 2024). Students were also grouped into one of four groups based on the proportionality of out-of-sequence submissions.
Students who stayed the most in sequence had final grades averaging approximately 13 points higher than the most out-of-sequence student group. The findings underscore the importance of emphasizing the significance of pacing guides and adherence to sequential assignment submissions to enhance student academic success. Educators should consider incorporating strategies to promote adherence to course pacing and assignment sequencing to optimize student outcomes.
Implications for Success
Cumulatively, these findings bring to light the nuanced relationship between engagement patterns, pacing, and student success in online learning. While linear progression and early achievement are indicative of positive outcomes, they may not be representative of the entire student population. The prevalence of late surges in engagement suggests varied approaches to pacing, with no one-size-fits-all solution (Kwon, 2017b).
Navigating the Path to Success
In light of these insights, it’s evident that fostering success in online learning requires a multifaceted approach. Institutions must prioritize providing students with the necessary guidance and structure, such as pacing guides, to facilitate steady progress through course material while still offering the flexibility afforded by online learning. Moreover, educators and policymakers must recognize the diverse learning trajectories exhibited by students and tailor support mechanisms accordingly.
Conclusion
As online learning continues to shape the educational landscape, understanding the intricacies of pacing and engagement is paramount. By leveraging insights from research and best practices, we can empower students to navigate the complexities of virtual education successfully. Ultimately, by fostering a supportive and adaptive learning environment, we can ensure that all students have the opportunity to thrive in online learning.
Course Pacing Blog Series
In our Course Pacing Blog Series, we discuss pacing and how it impacts student success with input from several different subject matter experts. Our hope with this series is to bring to light how different organizations and experts approach course pacing, share their insights and struggles, provide relevant research and resources, and determine areas for future research. Stay up to date on future blogs in this series by signing up for email notifications!
References
Cuccolo, K. & DeBruler, K. (not published). Charting the course: Understanding student sequencing and achievement.
DeBruler, K. (2021). Research On K-12 Online Best Practices. Michigan Virtual. https://michiganvirtual.org/blog/research-on-k-12-online-best-practices/
Freidhoff, J. R. (2023). Michigan’s k-12 virtual learning effectiveness report 2021-22. Michigan Virtual. https://michiganvirtual.org/research/publications/michigans-k-12-virtual-learning-effectiveness-report-2021-22/
Kwon, J. B. (2017a). Growth modeling with LMS data: Data preparation, plotting, and screening. Michigan Virtual University. https://michiganvirtual.org/research/publications/growth-modeling-with-lms-data-data-preparation-plotting-and-screening/
Kwon, J. B. (2017b). Course engagement patterns in mathematics and non-mathematics courses. Michigan Virtual University. https://michiganvirtual.org/research/publications/course-engagement-patterns-in-mathematics-and-non-mathematics-courses/
Kwon, J. B. (2018). Learning trajectories in online mathematics courses. Michigan Virtual University. https://michiganvirtual.org/research/publications/learning-trajectories-in-online-mathematics-courses/
Kwon, J. B. & DeBruler, K. (2019, September 26). Pacing Guide for Success in Online Mathematics Courses. Michigan Virtual. https://michiganvirtual.org/blog/pacing-guide-for-success-in-online-mathematics-courses/
Martin, F., & Whitmer, J. C. (2016). Applying learning analytics to investigate timed release in online learning. Technology, Knowledge and Learning, 21, 59-74. https://doi.org/10.1007/s10758-015-9261-9
Michigan Virtual Learning Research Institute. (2019). Pacing Guide For Success In Online Mathematics Courses. https://michiganvirtual.org/blog/pacing-guide-for-success-in-online-mathematics-courses/
Wakeling, V., & Robertson, P. R. (2017). A comparison of student behavior and performance between an instructor-regulated versus student-regulated online undergraduate finance course. American Journal of Educational Research, 5(8), 863. https://digitalcommons.kennesaw.edu/cgi/viewcontent.cgi?article=5075&context=facpubs
Zweig. J. (2023). The first week in an online course: Differences across schools. Michigan Virtual. https://michiganvirtual.org/research/publications/first-weeks-in-an-online-course/
