SRL profiles in math problem-solving: The Essential Role of Monitoring and Translating for Outcomes and Beliefs

Abstract

Self-regulated learning (SRL) positively impacts learning, which has prompted efforts to detect and foster SRL strategies. We used Gaussian Mixture Modeling to cluster students on usage of five SRL strategies often employed simultaneously in problem solving (PS)—measured via SRL detectors grounded in Winne’s SMART model (2017). Seven distinct SRL profiles emerged from the data. We then examined how these profiles relate to math beliefs, anxiety, and PS measures. Findings showed that SRL profiles with high proficiency across multiple SRL skills achieved higher accuracy, spent more time on pre-tests, and reported more opportunities to share their math thinking than profiles with limited proficiency across strategies. These students also frequently engaged in monitoring and translation. Notably, profiles with strong assembling skills underperformed peers who balanced SRL strategies with relatively higher usage of monitoring and translating. Overall, these results highlight the dynamic relationship between SRL and math beliefs in PS, and suggest SRL profiles to design tailored interventions.