The relative merits of transparency: Investigating situations that support the use of robotics in developing student learning adaptability across virtual and physical computing platforms

Abstract

This study examined whether developing earlier forms of knowledge in specific learning environments prepares students better for future learning when they are placed in an unfamiliar learning environment. Forty‐one students in the fifth and sixth grades learned to program robot movements using abstract concepts of speed, distance and direction. Students in high‐transparency environments learned visual programming to control robots (eg, organizing visual icons), and students in low‐transparency environments learned syntactic programming to control robots (eg, text‐based coding). Both groups received feedback and models of solutions during the learning phase. The assessment midway showed students in both conditions learned equally well when solving problems using familiar materials. However, a difference emerged when students were asked to solve new problems, using unfamiliar materials. The low‐transparency group was more successful in adapting and repurposing their knowledge to solve novel problems that required the use of unfamiliar high‐transparency materials. Students in the high‐transparency group were less successful in adapting their knowledge when solving new problems using unfamiliar low‐transparency materials. Both groups then proceeded to learn in the opposing transparency environments. The posttest revealed the benefits of initial learning in low‐transparency environments as students performed better on repeated and new inferential problems across virtual and physical platforms.