Using eye-tracking methods to understand attention and reasoning
Can catchy stories or emotional messages hijack our ability to attend to important details? And what strategies can help us combat being influenced by compelling but questionable information? We are currently using eye-tracking methods to understand how seductive information influence selective attention when reading and evaluating information. Additionally, we are interested in understanding how scientific reasoning skills explain differences in students' ability to search for and attend to evidence. This work is currently funded through UC Irvine's Education Research Initiative.
Using digital trace data to gain insights about self-regulated learning
The ability to engage in self-directed learning is often viewed as key to succeeding in online college courses. However, it's not well understood what specific combination of strategies lead to successful learning. Using data obtained from learning management systems and online tutoring platforms, we are developing metrics that capture different learning strategies (watching videos early, revisiting videos). We then turn to data mining techniques to build learner profiles and understand how these profiles predict passing or failing courses.
Developing training materials for clickstream data processing
Clickstream data, also referred to as digital trace data, is a valuable data source in learning analytics research. These time-stamped records of student click events within a learning management system provide researchers with finely detailed information about the learning process. This data however, is quite complex, requiring a nuanced understanding of the structure of the data, as well as advanced data processing techniques.
As part of the Learning Analytics and Knowledge Conference, we are developing training materials for processing, inspecting, and visualizing clickstream data using the R programming language. You can learn more about this workshop here.