Join our Teachers for Research & Feedback community to receive (paid!) opportunities to contribute your instructional expertise to educational research studies, and to help pilot new ASSISTments features. This blog post is part of our series on Evidence-Based Instructional Practices that sheds light on teaching practices grounded in learning science. You can find all posts in the series here.
AUTONOMY: At one point or another, we have all experienced the sense of accomplishment that comes from both choosing our own paths as well as tackling the tasks and/or obstacles that have subsequently surmised. In these instances,, because the act was done with autonomy – from the perspective of the self, regardless of how or if the outcome is successful, the feeling of accomplishment holds fast. The positive effects of autonomy are well-studied andbridge domains that include philosophy, politics, ethics, medicine, computing, robotics, and human development.
SELF DETERMINATION THEORY: Self-Determination Theory (SDT) is a psychological theory that focuses heavily on the importance of autonomy, bringing together earlier organismic and dialectical theories to form a single motivational model. The SDT model is based on the idea that there are three basic psychological needs that are both universal to the human condition and critical for well-being: autonomy, belonging, and competence (Ryan & Deci, 2000). Of these needs, autonomy is considered the most central to well-being and growth (Koestner & Losier, 2002) but its effects are most obvious when belonging and competence are also supported. Efforts to support these three basic needs can help to internalize regulation and locus of control (as shown in Figure 1), which can lead to accomplishments big and small.
Figure 1. A visual representation of SDT, as adapted from (Ryan & Deci, 2000).
Something to note about SDT is that the model recognizes that intrinsic motivation (think “flow state”) is actually quite rare and difficult to achieve, especially for children, who are not as able to self-regulate as adults. Instead, the SDT model fosters internalized forms of extrinsic motivation. Autonomy rich environments are thought to increase internalization, which has been linked to improvements in task enjoyment, engagement, self-esteem, school satisfaction, long-term learning outcomes, and conceptual understanding, as well as reductions in attrition, anxiety, and maladjustment (Koestner & Losier, 2002).
AUTONOMY AND EDUCATION: CAN THEY COINCIDE? In educational contexts, autonomy is typically associated with having an internal locus of control for participation, valuing or finding interest in tasks and goals, and feeling a sense of personal initiative that aligns well with external regulations (Reeve & Jang, 2006). Educational goals tend to be externally regulated and academic environments often fail to support students’ autonomy in ways that breed internalized extrinsic motivation; goals that undermine basic needs can actually suppress well-being. Standardized testing is a perfect example – while standardized tests are used as norm referenced benchmarks, the use of external controls as critical milestones can damage perceptions of competence and increase stress. Plus, ‘teaching to the test’ can promote poor conceptual learning and long-term retention (Grolnick & Ryan, 1987). The SDT model would ultimately recommend moving away from externally regulated tests and evaluations that influence critical paths and placements and toward tasks that support students’ autonomy to benefit deeper learning outcomes.
WHAT ABOUT THE SHIFT TO ONLINE LEARNING? Online learning is making teaching more complicated for teachers but it is also making learning more complicated for students – especially those who were already falling behind. Math performance has already declined measurably during the Covid-19 pandemic and achievement gaps have worsened (Tarasawa, 2020). The pandemic is especially hurting lower SES students (Chetty et al., 2020) and students of color (Tarasawa, 2020). This is especially distressing because online learning has become a critical part of modern education, with pandemic related school closures impacting more than 60% of the world’s student population, or more than 1 billion learners in 143 countries (UNESCO, 2020). It is a crucial time to understand how to engage remote learners, and adding support for student autonomy may offer a low-cost and low- solution to win back some gains.
WAYS TO SUPPORT AUTONOMY IN YOUR LESSONS An influential meta-analysis of 41 studies providing students with choices revealed small to moderate effects in outcomes like intrinsic motivation, effort, task performance, and perceived competence (Patall, Cooper, & Robinson, 2008). These effects were stronger for children than for adults, when subjects were tested in lab settings, when choices were instructionally irrelevant, when more than one choice was provided in succession, when choices were not supplemented by rewards, and when experimental groups were compared to strict controls (Patall, Cooper & Robinson, 2008). With these findings in mind, for those hoping to add support for student autonomy to classroom practices whether teaching in person or from a distance, think about providing students with simple, low-risk, and repeated choices regarding:
Many of these choices can be initiated in ASSISTments assignments by making due dates flexible, making multiple versions of assignments that vary by difficulty (easy/medium/difficult) or problem dosage (4/5/6 problems), or altering assignments to include various interest-based themes that might increase engagement.
ASSISTments’ rich ecosystem of content and diverse users make it a unique environment for educational research that brings effective teaching and learning interventions to students and teachers in real time, helping to close the gap between research and practice. Through funding from Schmidt Futures, our teams at WPI and TAF are developing a tool for academic researchers that hopes to scale educational research in an unprecedented way. This tool, E-TRIALS (Ed Tech Research Infrastructure to Advance Learning Science), builds on practices that originated in 2014, empowering educational researchers to examine what works in online learning by using anonymized ASSISTments data. Early support from the National Science Foundation helped academics from around the country work with our WPI team to create and transform math content, build complex problem sets to answer their research questions, and interpret resulting anonymized data. ASSISTments has since hosted more than 150 studies and more than two dozen peer reviewed publications.
One such study specifically investigated student autonomy using models like SDT: Ostrow & Heffernan (2015) gave some students a choice up front about the type of hints they would receive on their nightly homework on simple fraction multiplication - video or text - while other students were randomly assigned a hint style. Video hints were designed to be as comparable to text hints as possible and were administered using short 15-30 second YouTube clips presented on demand at the students request. With 78 students who participated in this study, findings suggested that while the feedback style itself did not impact learning gains, being able to choose it led to significant performance outcomes, even if students did not end up using hints during their assignment. Students given a choice in hint style earned higher scores on average, used fewer hints and attempts, and persisted longer than those randomly assigned a hint style (Ostrow & Heffernan, 2015). The WPI and TAF teams are proud that ASSISTments and E-TRIALS help produce these types of insights because they have the capacity to directly improve the educational outcomes of students across the country.
Chetty, R., Friedman, J., Hendren, N., Stepner, M., & the OI Team. (2020, November). The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data. Reprinted in: https://www.economist.com/united-states/2020/08/27/disrupted-schooling-will-deepen-inequality-for-american-students
Grolnick, W., Ryan, R. (1987). Autonomy in children's learning: An experimental and individual difference investigation. Journal of Personality and Social Psychology. 52: 890-898.
Koestner, R. & Losier, G. F. (2002). Distinguishing Three Ways of Being Internally Motivated: A Closer Look at Introjection, Identification, and Intrinsic Motivation. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 101-121). Rochester, NY: University of Rochester Press.
Ostrow, K. & Heffernan, N. (2015). The Role of Student Choice Within Adaptive Tutoring. In Conati, Heffernan, Mitrovic & Verdejo (Eds.) Proceedings of the 17th International Conference on Artificial Intelligence in Education. Springer International Publishing. Madrid, Spain. June 22-26. pp. 752-755.
Patall, E. A., Cooper, H., & Robinson, J.C. (2008). The Effects of Choice on Intrinsic Motivation and Related Outcomes: A Meta-Analysis of Research Findings. Psychology Bulletin. 134 (2): 270-300.
Reeve, J., & Jang, H. (2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98, 209–218.
Ryan, R. M. & Deci, E. L. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. American Psychologist. 55(1): 68-78.
Tarasawa, B. (2020, December 1). Learning during COVID-19: Initial research findings and 5 things we can do. NWEA. Retrieved from https://www.nwea.org/blog/2020/learning-during-covid-19-initial-research-findings-and-5-things-we-can-do/
UNESCO. (2020). Education: From disruption to recovery. Retrieved from: https://en.unesco.org/covid19/educationresponse.