Predicting the Quality of Preservice Early Childhood Teachers' Lesson Plans Using Cognitive and Motivational Challenges (Poster 28)

• In Event: AERA Poster Session Fourteen
  In Poster Session: Technology, Instruction, Cognition and Learning SIG Poster Session

Abstract

To use robots within early childhood education requires the preparation of early childhood teachers to use and teach block-based programming. We used a hierarchical linear model approach to address our research question: How can study cohort, cognitive challenge types, and motivational challenge types be used to predict lesson plan quality? Positive motivational challenge predictors were task value of programming, task value of teaching, mastery goals of programming, belonging in teaching, and autonomy in robotics. Negative motivational challenge predictors were mastery goals of teaching, belonging in robotics, self-efficacy in teaching, autonomy in programming, and autonomy in teaching. Positive cognitive challenge predictors were technical issues, problem solving - higher-order skills, and lesson design - other issues

Authors

• Brian R. Belland, Pennsylvania State University
  Presenting Author

• Yinqi Zhang, Pennsylvania State University
  Presenting Author

• Eunseo Lee, Pennsylvania State University
  Non-Presenting Author

• Afaf Baabdullah, Pennsylvania State University
  Non-Presenting Author

• Emre Dinç, Pennsylvania State University
  Non-Presenting Author

• ChanMin Kim, Pennsylvania State University
  Non-Presenting Author