From the three papers I read, I found the ideas of Embodiment, Embeddedness, and Dynamic Adaptation most intriguing (Winn, 2003). At first, they appeared to be very theoretical. However, it became clear these ideas had practical applications in teaching fundamental principles and concepts that can help computer science students deal effectively with learning algorithms and data structure. Let’s start with Embodiment. Being cognizant of algorithms and data structure are insufficient for triggering effective learning. Combining physical actions such as applying algorithms in real-life problem sets utilizing augmented reality can help students truly understand the subject. Then there is Embeddedness. Viewing a learning environment and the learner as one entity in which learning emerges as a property of the whole is immensely important for understanding how to design controlled scenarios that will enable effective learning. This latter process ties into Dynamic Adaptation that can influence the environment, the learner, or both. For example, a set of carefully designed learning scenarios that change the state of the environment in ways that require learners to adapt their knowledge in order to understand how algorithms (bubble sort, binary search, etc.) work and can also be used to prompt learners to introduce changes to the environment that will satisfy new sets of requirements that emerged from learners’ initial adaptation.
Lindgren and Johnson (2013) further reinforced the concepts of Embodiment, Embeddedness, and Dynamic Adaptation. In particular, number 5 and 6 – recommend Pair Lab Studies With Real-World Implementations and Reenvision Assessment – gave me valuable insights into how to achieve Embodiment and Embeddedness in the course of teaching students to visualize complex algorithms and data structure.
Dunleavy et al. ( 2009 ) informed my understanding of the affordances and limitations of AR environments. Such environments afford excellent collaboration and pattern matching but pose significant limitations, like the nascent stage of the software development and the inherent pedagogical and managerial complexity of an AR implementation (Dunleavy et al., 2009 ). Such factors need to be taken into consideration to achieve optimal Embodiment, Embeddedness, and Dynamic Adaptation.
What are some examples of current educational technologies that support embodied learning in computer science or math?
Could virtual and augmented learning environments harm social interactions in STEM education?
Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18(1), 7-22.
Lindgren, R., & Johnson-Glenberg, M. (2013). Emboldened by embodiment: Six precepts for research on embodied learning and mixed reality. Educational Researcher, 42(8), 445-452.
Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.