Undergraduate STEM courses are increasingly using learning management systems to provide extra supports for students and instructors. However, little research has been conducted on how best to structure these systems to provide students with metacognitive and motivational supports that might facilitate learning. The project will conduct three studies that will 1) identify how students use the elements of the learning management system content and how that use relates to student performance outcomes in STEM content; 2) test embedded learning strategy training experiences for students and motivational interventions to examine the affordance for student motivation, behavior, achievement, and completion rates; and 3) test whether a behavior-based early warning system will identify problematic learning behaviors earlier and more accurately than existing systems so that interventions might be targeted to the students particular problems.
Four courses in biology, calculus and two engineering domains within the earliest semesters of study at the undergraduate level will be the context of this study. The researchers and instructors from the departments will collaboratively design the learning environments built around Blackboard, the most common learning management system currently in use. The project will collect large grain data currently available from Blackboard of student performances in the system, medium grain data from a secondary module within Blackboard that aggregates behavior and performance and reports at the student level, and small grain data, logs of each student's transaction with the materials hosted in the course. An experimental design will be employed during the project that randomly assigns students different interventions and specific training supports. Data mining techniques, regression and structural modeling approaches will be used to identify patterns and examine the impacts of the training and motivational supports on student learning outcomes in the specified STEM content areas.
