Anticipated Date of Graduation

Spring 2023

Document Type


Degree Name

Master of Science in Mathematical Sciences


Mathematical Sciences

First Advisor

Doug Darbro


Before the Covid-19 pandemic of 2020, much of the research being done on virtual education was focused on the postsecondary level of instruction. With the Covid-19 pandemic forcing most school districts to offer some form of virtual instruction, the need for research at the secondary level and below had become more apparent, and much research has been done to fill in the gaps of what we know. This research project initially sought to explore the effects of synchronous versus asynchronous instruction, and how that instruction related to student success in a virtual high school mathematics program. As this author gained access to pre-existing data sets from Wyoming Virtual Academy (WYVA), the research evolved beyond just looking at instructional methods and focused more generally on what the predictors for student success are. Data was compiled together from multiple sources about the high school students at WYVA over the five-year span from 2018 to 2022. Data was collected on end of course mathematics grade, end of year state assessment scores for math, prior year’s math assessment score, enrolled course, percent of live lesson attendance, as well as whether the student was enrolled in a course predominately taught synchronously or asynchronously. Lastly, data was collected on whether or not a student was enrolled in special education services, had a 504 plan, and whether or not they qualified for the free/reduced lunch program. Given the lack of uniformity in grading styles, amount of extra credit given, etc…, end of course grade was dichotomously coded as either ‘successful’ (grade of 70% and above) or ‘not successful’ (69% and below). Logistic regression techniques were then utilized to predict student success based upon end of course grade as a function of the multiple predictors outlined above. Finally, multiple iv regression techniques were employed to create a model to predict student success on state assessment tests as a function of the same predictors. When looking at student success based on course grades, this study concluded that the statistically significant predictors were the previous year’s math scaled score on the state assessment test, and what percentage of the time students attended live lessons. In this dataset, students were much more likely to have a C and above for their final course grade when enrolled in an asynchronous course than they were in synchronous courses. Finally enrolled course, special services, and free/reduced lunch eligibility were not significant in this study. When measuring student success as defined by end of year state assessment scores, this research found that previous year’s assessment scores and math course enrolled in were the only significant predictors. For the students at WYVA, synchronous vs. asynchronous, special services, and free/reduced lunch eligibility were not statistically significant at predicting future success. Based upon these results, this author would encourage students to attend live lessons when they’re offered, and would encourage virtual schools to consider offering both synchronous and asynchronous versions of their courses to best fit the needs of the students they service. For future researchers, this author would advocate for the use of a survey in conjunction with the pure data that would document why a student chose to be enrolled in a virtual program to begin with, level of self-motivation, and perceived level of math anxiety.