Role Blending in a Learning Environment Supports Facilitation in a Robotics Class

Abstract

The open monitoring environment (OME) uses a novel data-mining approach to enhance teachers' pedagogical interventions in a robotics class. According to earlier studies, decision trees resulting from an open and semi- automatic data-mining process are practically useful when classifying subsequently data arising from a robotics class. The current study shows that data-mining features of the OME can be used to predict and, hence, support learning processes also with real-time data. Results show that the data-mining features of the OME are affected by the nature and amount of data when working with a small number of students. Furthermore, the results show that the robotics-class instructors are able to modify the learning environment to match to the current context in a way that goes beyond normal teacher activities in a classroom. This role blending between a teacher and a software developer in a learning environment provides a novel way to build a personalized and contextualized support environment for a robotics class.