Self-Study as Continuous Mapping

This collaborative self-study examines the authors' ongoing inquiry into deep learning practices across undergraduate teacher education and doctoral educational leadership programs. As a longstanding professional collaborative group from multiple institutions in the southwestern U.S., we came to this inquiry amid the continuing challenges of post-COVID educational landscapes and the rapid proliferation of generative artificial intelligence (AI) to collectively wonder what it means to engage students, and ourselves, in deep learning. Such learning allows for meaningful growth as thoughtful and reflective educators and leaders who can transfer their learning into lived professional contexts. Drawing on Darling-Hammond and Oakes (2019), Mehta and Fine (2019), and Nilson and McGuire (2013; 2018), we frame deep learning as requiring both intellectually rich experiences and explicit support for students' development as self-regulated learners. Aligning with the conference theme of self-study as continuous mapping, we embrace Dewey's (1938) understanding that knowledge is provisional and must be continually re-examined in light of new experience. Following LaBoskey's (2004) collaborative self-study methodology, we collected data across two academic years, including research journals, course artifacts, student feedback, and transcribed collaborative meetings. Our analysis reveals four interconnected themes: meaning-making through personal and professional connection, questioning grounded in deep listening and critical thinking, learning as embodied experience requiring intentional preparation and reflection, and teaching as deep work that requires active resistance to surface learning. Findings reveal that meaningful learning emerges through sustained engagement, inquiry, embodied experience, and explicit rejection of coverage or surface approaches, and that deep learning requires intentional and ongoing engagement by the instructor every semester, regardless of experience.