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.
1. In my post, I suggested the utilization of Desmos Calculator and its slider functions as a way of promoting embodied learning. The slider tool is a good way of allowing students to physically use their keyboard to manipulate the shapes of graphs. As for computer science, I believe it would be possible to teach students flow control (loops, etc) using manipulatives.
2. I think virtual and augmented reality learning environments can only bolster STEM education because it allows learning to be more collaborative and promotes greater discussion of topics. Students have better opportunities to physically manipulate objects in VR/AR spaces, and these physical interactions will lead to different types of interaction between students. The interactions encouraged by VR/AR technology will be different than those encouraged by traditional worksheets and labs.
I agree with you there, Gary. I think that the collaborative aspects are currently the driving force of utilizing VR and AR in the classroom and it will only continue to improve as we learn more about these technologies. The enhanced interactions the VR and AR allow us to manipulate, help build a student’s understanding on levels we never thought possible before. This will only improve communication between students and teachers and hopefully lead to some interesting discoveries in the field of science and math.
Hi Gary and Jocelynn
I agree with you that VR and AR can improve collaboration in teaching and learning. Those technologies have great potentials as remote collaborations among teachers and students around the world. I also believe that advanced technologies like AR and VR can help students alleviate misconceptions in STEM education by providing real-world examples and settings.
In STEM education, the utilization of both VR and AR are such opportunities that more K-12 educators should explore. As amazing as it was when I dissected a cow’s eye in grade 8, having more opportunities like that are much more accessible with digital technologies. In regards to your second question, I think that social interactions can be helped through VR and AR is it can help students to interact with not only their peers within their classroom or community but also all around the world. The idea that students can collaborate with others in different areas of the world can bring a whole new perspective to the way they think and begin to look at problems as a global citizen.
In regards to collaboration, that Lindgren article gave a great example. The SMALL lab is an interactive floor display (15 feet x 15 feet) that allows students to interact with it using wands. It can track up to 4 students at the same time, fostering a collaborative learning environment. In the article I read by Kamarainen et al (2013), the AR environment itself was not collaborative, but there was a lot of student-student interaction as a result of using this tool. I think if anything, AR environments can foster collaborative learning in STEM.