Peter McPartlan

Department of Psychology
San Diego State University

I am currently a postdoctoral fellow at San Diego State University, having received my PhD. from the School of Education at the University of California, Irvine. My research is devoted to the idea that we can improve students’ academic motivation and persistence if we understand the social processes that motivate adolescents.

To that end, my research helps students who face barriers to social integration in school, including underrepresented students in STEM and online students.

My research focuses on underrepresented students in STEM and online learners because of what they have in common: a struggle with belonging, or social integration, in their classrooms. I am currently doing this through programs based at UCI and San Diego State. First, I have partnered with UCI’s Biology program to implement a learning community that has successfully raised the motivation, performance, and persistence of underprepared students. Simultaneously, I am exploring the ability of short, cost-effective interventions, like the Utility Value Intervention, that can raise STEM students' motivation. Our team at SDSU is studying what factors lead instructors to reject interventions like these despite evidence that they support motivation and diversity. Second, I investigate the motivational challenges and affordances of online environments, investigating students' perceptions of support, belonging, and anonymity in online courses. I also seek new ways to leverage click data from online courses to shed light on how motivation drives behavior. 

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Similarly, my philosophy as an instructor centers on supporting learning by fostering motivation. Learning activities are built around students' natural drives to socialize, assess their own competence, and connect material to their own lives. My expertise includes content on educational psychology and quantitative research methods. Content courses I teach focus on student motivation and technology, whereas methods courses I teach focus on introductory statistics, structural equation modeling, and pattern-centered analyses.