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Developing Learning Analytics to Promote Knowledge Integration in a Technology-enhanced Learning Environment

  • Author(s): Wiley, Korah;
  • Advisor(s): Linn, Marcia F;
  • et al.
Abstract

In a context where classrooms are becoming increasingly enhanced with technology, a research priority is to develop learning analytics and teacher dashboards that support pedagogical actions that leverage students’ ideas as learning resources. While the field of learning analytics has made remarkable advances in developing and applying cutting edge technologies to support teaching and learning (e.g. machine learning-based predictive analytics), more progress is needed to connect these advances to the complex task of providing teachers with insight into student thinking (Baker et al., 2020). Additionally, the widespread adoption of the Next Generation Science Standards (NGSS) and the increased use of data-generating technologies in K-12 science classrooms makes the need for learning analytics that align with research- and theory-based pedagogy especially important. Taken together, this situation calls for the development of learning analytics and pedagogical supports that align with the current education reform efforts and leverage the unique perspectives and practices of teachers and students. My dissertation project addresses this situation by investigating the research question of how to develop and evaluate learning analytics and pedagogical supports that assist diverse teachers in supporting their students to build on their developing ideas towards integrated science knowledge.

Specifically, this design-based dissertation project uses mixed methods to develop learning analytics that support teachers in investigating their students’ developing understanding of complex ideas about energy and matter transformation in photosynthesis. Using the knowledge integration (KI) pedagogical framework, I: (a) developed an online inquiry science unit on photosynthesis; (b) developed analytics to reveal student thinking by analyzing system-logged data associated with student-generated artifacts using natural language processing and machine learning techniques; and (c) developed and refined a teacher dashboard, called the Teacher Action Planner.

While this dissertation project primarily focuses on a middle school science classroom using a technology-enhanced learning environment, the resulting development strategy and products have broad application across disciplinary domains, instructional contexts, and teacher and student populations.

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