Introduction and Need for the Study

Unlike in traditional in-person classes, students in many online asynchronous courses may submit assignments in any order and at any time during the course. To support students in their courses, Michigan Virtual provides pacing guides that outline the recommended order in which students should complete course content, and that show students which assignments they should complete each week to stay on track within their course. Prior research on Michigan Virtual courses found that deviations from the course pacing guides in terms of the timing and order of assignment submissions were negatively associated with final course grades (Cuccolo & DeBruler, 2024; Cuccolo & Green, 2025).

Based on this research, Michigan Virtual took another step to help students stay on-track with their courses: they introduced a Student Pacing Tool and updated the way grades were calculated in the gradebook. The Student Pacing Tool displays a list of assignments, their due dates, submission dates, number of days late, points and grades. Prior to summer 2025, a student’s grade was calculated by dividing the total points earned by the total possible points in the course. Thus, a student’s grade started at zero and increased as assignments were completed. Starting in Summer 2025 with the new Student Pacing Tool, a grade-to-date was also calculated by dividing the total points earned by the points attempted. If a deadline passed without submission, an automatic zero was added and affected the grade-to-date. If the assignment was later submitted and graded, the zero was replaced with the actual grade.

Administrators at Michigan Virtual wanted to understand how this Student Pacing Tool and shift in gradebook calculations influenced students’ pacing and course grades. Michigan Virtual planned to use the results to inform their efforts to continuously improve course design through data analysis and feedback from the field. This memo focuses on the following questions:

1. On average, what percentage of assignments were submitted on-time during fall 2025?

2. How did assignment submission rates in fall 2025 after the introduction of the new student pacing tool compare to assignment submission rates in the same courses in fall 2024?

3. How did final course grades and pass rates in fall 2025 compared to final course grades and pass rates in fall 2024?

4. How did assignment submission rates and final grades vary based on enrollments’ submission rates during the first quarter?

Data

This analysis relied on two deidentified datasets that were merged based on research identification numbers: enrollment data and gradebook data. The enrollment data included information about the date of enrollment, course name and index, section index, course enrollment status (dropped, completed), final course grade, an indicator for the brick-and-mortar district, and district characteristics.

The gradebook data included information about the course assignments for each enrollment including assignment name, assignment type, due date, submission date, points earned, and points possible. Gradebook assignments that were missing a name or where the assignment type indicated that it was an automatic calculation of their course grade rather than an actual assignment were dropped. The analytic sample focused on 543,136 assignments in fall 2024 and 570,734 assignments in fall 2025. The assignment data were then summarized for each enrollment so that the unit of analysis was the enrollment. Some students were enrolled in more than one course.

The variables from the gradebook were used to determine which assignments were submitted and which assignments were submitted on-time. An assignment was considered submitted if there was an assignment submission date or if the student earned points for the assignment without an assignment submission date, which would indicate a manual change by the instructor.

Course pacing was dynamic, meaning that assignment due dates and time in the course were based on when enrollments first accessed and then exited the course. The calculations for on-time submissions were then based on timing of assignment submission relative to the enrollment’s assignment due dates (Appendix A). For enrollments in 2025, the percent of assignments that were on-time was equal to the number of assignments where the submission date was on or before the due date divided by the number of assignments with a due date. The number of assignments with due dates ranged from 0 to over 100 with some variation across enrollments within courses and sections. For each enrollment, the percent of assignment submissions that were on-time at the quarter-point of the course was equal to the number of assignments submitted on-time during the first quarter of the course divided by the number of assignments with a due date in the first quarter. This on-time submission rate was calculated separately for each enrollment overall and for each quarter of the course. Then, the average on-time submission rates were computed where the unit of analysis was the enrollment. Since the on-time submission rate calculations were only relevant for fall 2025 when the new Student Pacing Tool was introduced, we also calculated the percentage of each enrollment’s assignments that were submitted overall and by the end of each quarter of the course in order to make comparisons across years.

See Appendix A for a list of variables and their definitions.

Sample

This report includes data from enrollments in Michigan Virtual courses from fall 2024 and fall 2025 who did not drop their courses. The sample only included enrollments in courses where the new Student Pacing Tool was introduced. This sample excluded Advanced Placement courses because assignments in those courses already had due dates, and excluded Essentials courses because those courses did not use the Student Pacing Tool. The analysis focused on 132 of the 146 courses that were offered in both 2024 and 2025, and had a similar number of assignments to have comparable measures of pacing.

The sample consisted of 10,680 enrollments from 8,447 students in Michigan Virtual courses in fall 2025, and 10,037 enrollments from 8,184 students in fall 2024. Approximately 87% of enrollments were from local education agency schools, 6% were homeschooled^[1] , and 7% were from other types of entities (e.g., PSA schools).  Approximately 46% of brick-and-mortar enrollments were from cities or suburbs and 45% from rural areas or towns. Approximately 77% of enrollments were from low- or mid-low poverty schools; homeschool enrollments did not have information on poverty levels or locale. There were differences between fall 2024 and 2025 in school type, locale, and experience with Michigan Virtual courses which were accounted for in the regression models described below. The analytic sample included 132 courses across 1,083 sections (see Appendix B for a list of courses).

Table 1. Sample Characteristics

*Significant differences between fall 2024 and fall 2025 based on chi-squared tests.

Methodology

To address Research Question 1, we calculated the mean percent of assignments submitted on-time in fall 2025 overall and in each quarter of the course. The same rates for on-time submissions could not be produced for 2024 because the assignments did not have due dates.

To address Research Question 2, we descriptively compared the mean percent of assignments submitted by the quarter-point, mid-point, three-quarter point, and end of the fall 2024 to fall 2025 terms. As noted in the previous section, there were some differences across years in the characteristics of the sample. Thus, a fixed-effects regression analysis was conducted where the outcome variable (percent of assignments submitted) was regressed on the key independent variable – school year – while controlling for enrollment characteristics. These characteristics included indicators representing free- and reduced-price lunch categories, indicators of homeschool enrollment where brick-and-mortar is the reference category, indicators for locale (rural or town, missing locale, and city or suburbs as the reference category), a binary indicator of whether the enrollment previously took a Michigan Virtual course, and a binary indicator for whether the enrollment was enrolled in more than one course. We used a course fixed effects model to account for all differences across courses so that the variation used to estimate the differences in outcomes between fall 2024 and fall 2025 was based on differences within a course across years. The standard errors were clustered at the section level because students were nested within sections within courses.

We also calculated the total number of assignments submitted each week of fall 2024 and 2025 from August through January. This provided information about the distribution of assignment submissions during the fall terms and were not based on each student’s enrollment dates to demonstrate how many assignments teachers received each week throughout the semester.

To address Research Question 3, we descriptively compared the mean final course grades and percentage of enrollments that passed their course (i.e., earned a final course grade greater than 60%) in fall 2024 and 2025. Similar to Research Question 2, we employed fixed effects models with the same control variables to complement the descriptive analysis.

To address Research Question 4, we examined outcomes for enrollments who completed less than 12.5% of their assignments in the first quarter (bottom quartile) compared to those who completed at least 29% of their assignments in the first quarter (top quartile). Enrollments in the middle two quartiles were not included in this analysis. We produced descriptive statistics for the outcomes and employed similar fixed effects models as Research Questions 2 and 3 with an indicator for being in the bottom quartile for submissions and an interaction term between school year and being in the bottom quartile. The interaction term indicates whether the changes across years are different for those in the bottom and top quartiles.

Limitations

The primary limitation of this study is that the introduction of the Student Pacing Tool and gradebook calculations occurred for all Michigan Virtual courses between the 2024 and 2025 school year. The differences in the outcomes between 2024 and 2025 may be attributable to a combination of these shifts and any other differences between those two terms. The results provide preliminary evidence of how these shifts in course design relate to student outcomes but should not be interpreted as causal.

Findings

The findings for this report are forthcoming.

References

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. 

Cuccolo, K., & DeBruler, K. (2024). Assignment submission patterns and performance in K-12 online STEM courses. Learning: Research and Practice, 1–19. https://doi.org/10.1080/23735082.2024.2441113

Cuccolo, K., & Green, C. (2025). Out of order, still out of reach: Navigating assignment sequences for Michigan Virtual World Language courses. Michigan Virtual. https://michiganvirtual.org/research/publications/navigating-assignment-sequences-for-mv-world-language-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/ 

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/

^[1] Homeschool enrollments included all enrollments where the parent or guardian enrolled the student in an online course and paid for their course. In some cases, the student may be enrolled in a brick-and-mortar school.