This EAGER project, Transformative Robotics Experience for Elementary Students (TREES), explored the affordances and constraints of a complex humanoid robot platform for students’ learning of robotics and computational thinking in inclusive classroom settings. Using a design-based approach, the project developed and implemented an innovative robotics curriculum and has benefited a total of more than 400 fifth grade students in multiple cycles. Our research questions focused on the effectiveness of the curriculum, assessment of computational thinking, and the relationship between students’ disciplinary engagement, learning of robotics and computational thinking, and interactions with the humanoid robotics platform. Overall, the major outcomes of the project included one doctoral dissertation, 2 published journal publications, 2 submitted journal publications, 8 national/international conference presentations, and a set of instructional materials including curriculum, assessment tools, and instructional videos. 

Our data showed that as a result of the curriculum, students’ computational thinking improved in both everyday reasoning and programming contexts, but the degrees of improvement were associated with different factors. For instance, students’ self-determination and intrinsic interest were correlated positively with their performance. Besides the curriculum, another major product produced from the project is an instrument assessing students’ computational thinking. Our results showed that the instrument has high psychometric qualities and is appropriate for fifth graders. Furthermore, the computer-based version is capable of logging student interactions and provides a powerful means to study students’ CT processes and patterns. Qualitatively, our interview analysis further revealed that as students were participating in a variety of coding and problem-solving practices, they were interpreting and navigating information within the code window, across the code window and task instructions, across the code window and physical robot, and across all three representations.

Computer science is a core academic subject that leads to careers in computing, STEM fields, and across the 21st century economy. Humanoid robots are naturally engaging, and have been implemented with students in educational and health related settings; however, utilization of a humanoid robotics platform has not yet been applied to student learning of core computer science and robotics content. This project developed a novel elementary humanoid robotics course that centers around the construct of computational thinking in engaging elementary school students in learning computer science and specifically robotics. This project can advance our understanding of how to create learning opportunities for elementary students in robotics and computer science that would increase their content knowledge and motivate them for future learning in robotics, computer science, and STEM fields. It has been conducted in inclusive classroom settings, allowing us to reach a diverse population that has historically been underrepresented in STEM fields.

Taking steps towards developing a valid and reliable instrument to measure elementary students' CT is challenging. First, there is a lack of consensus in the field in terms of CT definition. Second, many elementary students have limited programming experience. Our work has contributed to help solve this problem in several ways. We developed an instrument on CT based on operationalizable components of a CT framework. We developed specific coding rubrics for each item based on each component of the framework. Also, our instrument used two contexts (robotics programming and everyday reasoning) to assess students’ CT application, which sheds light on the near and far transfer aspect of CT. This is critically important as CT is a valuable skill for all learners, may apply to daily problem-solving activities, and may apply to many other STEM learning areas. Our work also revealed the potential of using logged data to understand students’ application of CT in problem-solving process toward a better understanding of their learning.

Last Modified: 07/13/2018
Modified by: Ji Shen