Contextual Research-Empirical Research--Detecting, Tracking, and Modeling Cognitive, Affective, and Metacognitive Regulatory Processes to Optimize Learning with MetaTutor

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This 3-year project conducts laboratory based experimental research to extend and apply a multiagent intelligent hypermedia-based learning environment to detect, track, and model college students' multiple self-regulatory processes while learning biology. The hypothesis examined by the researchers is that many college students may be hampered in learning biology by their inability to self-regulate themselves. The researchers have developed a Tutor that is expected to provide cognitive, affective, and metacognitive (CAM) support. The proposed research will test experimentally different versions of the Tutor program to examine how self-regulatory processes emerge and the effects of program variations on self-regulatory behaviors.The investigators will conduct experiments in a laboratory with college students in Memphis Tennessee and Montreal, Canada. The experiment will detect, track, and model the CAM processes in college students' learning about a complex biology topic. During the experimental session they will collect data using a remote eye-tracker to record participants' eye gaze, fixations, saccades, and regressions. The participants' verbalizations will be recorded with a headset microphone. They will use a Pressure Mouse to capture the amount of pressure placed on the mouse throughout the activity, and the Body Pressure Measurement System (BPMS) to assess gross body movements. They will revise and develop new pretest and posttest learning measures for the circulatory system which will include approximately 15 multiple-choice questions, 10 inference questions, labeling tasks, and mental model essays. The measures will assess declarative, procedural, inferential, and mental models of the circulatory system. The research investigators will examine several theoretical, empirical, and educational questions about self regulation intended to forge new directions of science learning. The team includes psychologists, computer scientists, psychometricians, and electrical engineers. Methodologies will be incorporated from psychology, education, computer science, and electrical engineering to detect, trace, model, and assess students' CAM self-regulatory processes during learning about a complex and challenging science topic. The proposed research activities are intended to advance the science of learning, methodologies, and quantitative analysis of complex sensing data, and education, research, and evaluation, and demonstrate the power of multi-method research tools, software, and sensing devices capable of analyzing and predicting students' self-regulated learning about complex science topics.