Teachers' Interpretations of and Approaches to Multimodal Learning Measures of Student Engagement: A Call for Learning Analytic Pedagogical Agent Support

• In Event: AERA Graduate Student Research in Progress Roundtable Session Two
  In Roundtable Session: Research Use and Data-Driven Decision Making to Improve Education (Table 12)

Abstract

Educators must adapt and improvise their instruction for each student's unique needs and capabilities across contexts by observing their students, evaluating their ongoing learning, and offer individualized scaffolding and feedback to foster sustained growth and development. The recent emphasis for data-driven instructional decision making further requires teachers to become both pedagogical and data experts who are capable of navigating and utilizing teacher dashboards to do so. While dashboards are increasing in popularity, we emphasize the need to understand educator data literacy and how educators are currently approach multimodal learning analytics (MMLA) that report on more than just performance-based outcomes. That is, are teachers capable of utilizing the sophisticated data visualization and analytics researchers are developing? And if so, under what conditions do they consult these visualizations and analytics to make real-time decisions about their instruction? In this on-going work, we describe the development of an in-progress study exploring how teachers currently use fictious MMLA on self-regulated learning (SRL) processes to interpret levels of student engagement and the emerging trends we see from their data. Finally, we postulate what these results suggest about the needs that embedded intelligent pedagogical companions may fill in future dashboard and agent design.

Author

• Megan Wiedbusch, University of Central Florida
  Presenting Author