A data-driven path model of student attributes, affect, and engagement in a computer-based science inquiry microworld

Work in recent years has shown that a student's goals, attitudes, and beliefs towards learning impact their level of engagement during learning, and that engagement during learning plays a key role in learning outcomes. In this paper, we investigate the mechanisms through which a student's goals, attitudes, and beliefs impact the student's engaged and disengaged behaviors. In particular, we study whether affect is a mediating variable between learner attributes and engagement/disengagement during learning. To this end, we conduct exploratory path analysis on data from middle school learners who were conducting inquiry in science a microworld. We find weak but significant relationships between variables related to attitudes and beliefs and variables related to affective states and engagement. We present a path model that highlights boredom as an important mediator between a tendency towards avoiding novelty and off-task behavior during learning.