The core research question of this proposal is how interactions between learners (i.e., individual differences), instructional materials (i.e., the text), and learning activities (i.e., the task) modulate engagement during learning of critical thinking skills and scientific reasoning. The proposed research will address this goal by: (a) systematically investigating the mechanisms that facilitate or hinder engagement, and (b) leveraging these insights towards the development of interventions that promote persistent and productive engagement trajectories during deep learning. The work will be conducted at the University of Memphis. The research subjects will be undergraduate students.
The research design includes four experiments in which learning gains and self-reported engagement, physiological arousal, eye gaze patterns, and facial features will be tracked while learners study instructional texts. Comprehending these texts for mastery requires active engagement as learners generate inferences, understand causality, identify problems, discriminate the quality of experimental designs, and ask diagnostic questions. Analyses will be conducted using nonlinear time series analysis techniques, such as recurrence quantification analysis. The project evaluation will include an expert advisory committee that will be used to critically review the investigators' findings and interpretations. In addition, the investigators will develop and validate a web-based computer program that dynamically tailors both the instructional text and the learning activity to the needs and learning styles of individual learners to enhance engagement. The proposed research will balance the theoretical goal of theory building and model testing via systematic experimentation with the practical goal of developing innovative advanced learning technologies that aspire to promote engagement and learning of difficult subject matter.
This research is important in the STEM education field's ongoing efforts to increase engagement and the productivity of learning of STEM subject matter. If the research is successful, the derivative knowledge and tools will be significant contributions and could be applied widely in other intelligent tutoring systems, other instructional technologies, and in our understanding of student learning in general. This research is potentially transformative in two ways. First, it will provide a detailed understanding in real time of engagement at the micro, relational level of student, task, and materials. Second, the creation of an intelligent tutoring system based on these findings holds the possibility of being able to 'correct' low levels of student engagement on a moment-to-moment basis and therefore boost learning productivity while increasing student satisfaction and engagement with the experience. Dissemination will include the public availability of the technological tools as well as contributions to the scholarly literature.
