
Transformative Undergraduate Self-regulated STEM Learning and Education Research
Effective Years: 2019-2024
This project, based at the University of North Carolina, will seek to advance fundamental knowledge about self-regulated learning (SRL) and, critically, how to assess it. SRL is a rough construct that includes cognitive strategy choice, metacognitive monitoring, motivation, and affect. SRL has been associated with STEM learning success; students benefit most from innovative instructional envirionments -- such as online learning -- when they are adept at regulating their own learning by actively and thoughtfully capitalizing upon these innovative learning environments. Research on SRL has slowed, in part because current measures of it are in need of improvement as they rely either on self-report measures or on Think Aloud Protocols, both of which are labor-intensive and therefore not amenable to the collection of large sets of data. The researchers will develop methods for identifying successful self-regulated learning by doing computational modeling of digital trace data. Such data are generated as students use learning management systems in active pedagogy STEM classrooms. The project team will collect data from students using data derived both from more established research methodologies such as Thinking Aloud Protocols as well as those derived from digital trace methods in order to validate their digital trace assessment models. The investigators hope eventually to be able to derive data at such a scale as to allow for refinement in SRL theories, which In turn, will lead to the next-generation large-scale investigations needed to help students self-regulate more effectively and efficiently. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.
Two decades of intensive study have implicated self-regulated learning processes and their effects on learning. This project seeks to refine what is known about SRL and in so doing hope to maximize the benefits of active learning pedagogies in terms of STEM learning, retention, and career success. The investigators will develop a new methodology for assessment of SRL that they hope will replace the dominant methodologies, such as self-report and Think Aloud Protocols. They will capture digital trace data from undergraduates engaged in online learning tasks commonly featured in university coursework in STEM domains. Concurrent think-aloud methods will be employed in laboratory studies to validate the modeling of simultaneously-collected the digital trace data and support inferences about their meaning. The proposed work will then seek to advance aspects of SRL theory. The investigators will test the assumptions of contingency and adaptation in self-regulated learning models that have proven to be intractable using analyses based on self-report data. It is hoped that findings will enable SRL theory to account for contingent, sequential, and dynamic self-regulated learning processing. It is expected that these advances, eventually, will shed light on real-time models of student learning, and afford new ways of designing instruction to promote adaptive learning, including how to develop interventions that bolster success in authentic STEM learning contexts such as introductory large classes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.