Drivers of student satisfaction and student outcomes in K-12 online learning environments - Beaton, J (2021)
My thesis examines what drives student outcomes and student satisfaction in online schooling. Specifically, I examine whether psychometric matching between students and tutors can be used to enhance outcomes or satisfaction and in light of this, the key considerations for governments seeking to regulate the online schooling landscape. Two systematic literature reviews are conducted: firstly, what drives student outcomes and student satisfaction in online schooling and secondly (Chapter Two), the use of algorithmic matching between students and educators in schooling to improve student outcomes and/or student satisfaction (Chapter Three). I perform a qualitative analysis of leading virtual high schools, interviewing 21 students using NVivo qualitative analysis software (Chapter Four). I conduct cross-sectional analysis on a data-set from Crimson Education, an online education company, to evaluate the impact of psychometric characteristics on student satisfaction (Chapter Five). I run a randomized control trial to test the causality of a psychometric matching algorithm on student outcomes and student satisfaction (Chapter Six). My randomized control trial analysis finds that psychometric matching can lead to significant impact on writing scores and on student satisfaction within Western Europe and Latin America. My cross-sectional analysis also finds that student satisfaction scores can be impacted by the psychometric characteristics of students. Finally, I provide a set of recommendations for public policy makers looking to adapt legislation to the emerging online high schooling sector in light of my findings from earlier chapters, in particular my qualitative analysis and systematic reviews (Chapter Seven).