D.I.G. D.E.E.P. TO ADDRESS LEARNING LAG: A DESIGN THINKING DISSERTATION IN PRACTICE FOR DEVELOPING A COMMUNITY-BASED MATHEMATICS SUPPORT MODEL

This Dissertation in Practice applies a human-centered design thinking framework to examine how I developed the D.I.G. D.E.E.P. GDII Learning Solutions (DIG DEEP) model, a community-based learning support program designed to address post-pandemic mathematics learning lag among secondary students. Grounded in my practitioner experience and the needs of the community I serve, I reframe this persistent educational challenge as a problem of practice requiring iterative, stakeholder-informed design rather than the evaluation of a fixed intervention.Guided by design thinking principles, I progressed through stages of framing the problem, empathizing with student and parent experiences, defining key learning needs, and iteratively designing and refining instructional supports. I drew on multiple data sources, including student and parent surveys, semi-structured interviews, and standards-aligned pre- and post-assessment data collected during program implementation. I analyzed both qualitative and quantitative data to identify patterns in student learning needs, engagement, and responses to instructional supports.My findings highlight several promising practices for supporting mathematics learning recovery, including diagnostic-driven instruction, small-group tutoring, relational academic coaching, and the integration of scaffolded technological supports. Student and parent feedback emphasized the importance of individualized attention, instructional clarity, and emotionally supportive learning environments, while assessment trends suggested improved accuracy and engagement under structured, scaffolded conditions. Importantly, I demonstrate that meaningful program design emerges through iterative refinement informed by stakeholder voice, rather than through static implementation. The DIG DEEP model evolved across three phases of implementation which included multiple cycles of design and feedback, resulting in a more responsive and contextually grounded learning support framework. This iterative refinement included the development of structured, grade-level aligned instructional materials, such as student workbooks with guided notes and practice tasks, as well as the integration of hands-on learning supports (e.g., foldables with anchor charts, graphing utilities, interactive notebooks) designed to strengthen conceptual understanding and retention. I also introduced transportation supports to increase access and attendance, while implementing formal surveys and interviews to systematically incorporate student and parent feedback into ongoing program design.Through this work, I contribute to the field of out-of-school-time learning by illustrating how design thinking can be applied to develop sustainable, community-based educational models that respond to the needs of under-resourced learners. I conclude by proposing a Phase IV design that outlines next steps for testing scalability, strengthening data collection, and expanding program impact. In essence, this work reflects my practitioner-driven commitment to designing meaningful, adaptable learning solutions that support both academic growth and student confidence in mathematics.