A Mixed-Methods Study Exploring Learning Support Use in Digital Game-Based Math Learning (Poster 22)

• In Event: AERA Poster Session Nine
  In Poster Session: Learning With Games and Virtual Reality

Abstract

Literature on learning support in digital game-based learning (DGBL) rarely investigate learners’ support-use behaviors and profiles in relation to math learning. We addressed this research gap in this exploratory mixed-methods study. We designed and developed a package of learning supports (i.e., Task Planner and Math Story) in a math DGBL. With the data (from 53 participants) analyzed via mixed methods, we extracted six clusters of learning support use behaviors via an unsupervised machine learning technique (i.e., Gaussian Mixture Model). Qualitative multi-cases study revealed nuanced details regarding learners’ interactions with the learning supports in DGBL. Results showed that the designed in-game learning supports facilitated individual meaningful and mindful math problem-solving experiences. The findings informs the design of adaptive and effective DGBL.

Authors

• Chih-Pu Dai, University of Hawai‘i at Mānoa
  Presenting Author

• Fengfeng Ke, Florida State University
  Non-Presenting Author

• Yanjun Pan, Southern Methodist University
  Non-Presenting Author

• Yaning Liu, University of Colorado - Denver
  Non-Presenting Author