Predicting the quality of robotics-enhanced lesson plans using motivation, academic standing, and collaboration status

Computer science can be included in Early Childhood Education (ECE) through the use of block-based coding and robots. But this requires adequate preparation of ECE teachers to work with coding and robots, and integrate such into high quality lesson plans. In this paper, we investigate predictors of lesson plan quality among preservice, early childhood teachers learning to teach with robots. Motivation variables, academic standing, and collaboration status during lesson planning were entered as predictors of overall lesson plan quality, front-end analysis quality, STEM and robotics integration quality, and instructional activities quality. Achievement emotions in STEM was a positive predictor and mathematics interest was a negative predictor of the overall lesson plan quality score. Achievement emotions in STEM was a significant positive predictor of front-end analysis score. Science and technology interest and individual lesson planning were significant positive predictors of teaching and learning activities design score. Instructional implications are presented.