Anthony E. Kelly, Office of Educational Technology, U.S. Department of Education, and George Mason University
Having attended sessions on and talked informally with people about learning analytics and big data, I am convinced that these techniques represent a major opportunity for education research. Marcia Linn’s presentation is an example of this.
Marcia Linn’s presentation, “Designing Assessments to Track Student Progress in Understanding the Complex Roles of Energy in Photosynthesis,” showed how it is possible to track whether students have understood foundational concepts that will support the comprehension of complex topics, such as photosynthesis. Pre- and post-tests that focus only on the narrow definitions of photosynthesis can miss the prior supporting, and more general ideas, such as energy. She showed how the Web-based Inquiry Science Environment (WISE) uses student-generated concept-mapping to track students’ understanding not only of photosynthesis, but of foundational concepts as well. From a big data perspective, her model allows the tracking of student knowledge across time and across concepts, whereas traditional summative measures average across items and focus on snapshot observations of learning. The value of her approach is clear once learning is thought about as multi-dimensional, and protracted over time.
We need to get out the message that more can be learned about how students learn outside of standardized tests, and pre-post test designs. It will take strategic research investments in training, and funding, to realize these goals, but this effort is key to bringing education research into the 21st century.