'Big Data' Engineers
Combining the disciplines of computer science, cognitive psychology, education, IT and design, Carnegie Mellon University is now offering a first-of-its-kind program through its Human-Computer Interaction Institute (HCII) to help create learning engineers of the future.
Through the use of "big data," graduates will be better equipped for developing and evaluating educational programs — giving them the edge over competition as they apply for key positions in schools, universities and corporations.
The new Learning Science and Engineering Professional Master's Degree Program at CMU will teach students how to use and analyze big data to develop and evaluate educational programs in a variety of settings, including schools, workplaces, museums and other locations.
It will equip students to better understand human learning and create educational technologies that increase student achievement.
The program builds on CMU's decades-long expertise in creating educational technology solutions. Students will be able to develop and implement advanced tools that use state-of-the-art technologies and methods, such as artificial intelligence and machine learning.
"Technology has really transformed how we teach," said Ken Koedinger, professor at HCII and director of the Pittsburgh Science of Learning Center.
"The availability of data on how people learn provides us with the opportunity to create more engaging and effective instruction. We want to create learning engineers — people who not only understand their subject area, but the science behind learning."
One problem with relying only on subject matter experts for course development is that experts can only articulate about 30 percent of their knowledge, Koedinger said. Using data, learning engineers can identify trouble areas for students and address issues that a subject expert may miss.
Through case studies and real-world applications, students will learn to engineer and implement innovative educational solutions employing "in vivo" experiments and educational data mining techniques. They will learn how to develop continual improvement programs that identify best practices as well as opportunities for change.
Students will gain depth in psychometric and educational data mining methods, interaction design, cognitive and social psychology principles, design, implementation and evaluation of educational interventions.
Photo: a 'big data' visualization of Wikipedia edits, created by IBM. At multiple terabytes in size, the text and images of Wikipedia are a classic example of big data.