The goal of this project is to develop technology-based techniques to directly capture and analyze student learning steps and pathways, and then provide interventions based on that analysis to promote success in engineering courses. In this research, students will use smartpens and tablet computers to carry out learning activities in undergraduate engineering courses. The smartpens record problem-solving work, while the tablets record how students use instructional materials, such as e-books. This combination of technologies provides a fine-grained view of the learning process not available with conventional assessment methods, thereby enabling powerful tools for individualized assessment that can guide instruction.
Data mining techniques are used to examine the correlation between these learning activities and academic achievement as measured by exam performance, providing a means to distinguish between ineffective and effective learning strategies. These assessment techniques are used to create an early warning system that identifies students at risk of poor academic performance and recommends suitable learning strategies. The burgeoning field of educational informatics has begun to produce important insights about student learning. However, most research in this field has relied on data from artificial learning environments such as online tutoring systems. This project helps transform this field by creating techniques for capturing students' learning activities in existing engineering courses in a form suitable for data mining. This project helps detect the learning processes and suggest individualized interventions for the diverse students at a Hispanic Serving Institution, and thus helps to create increased opportunities for students from traditionally underrepresented groups to succeed in STEM careers.
