Page 9 - Campus Technology, January/February 2019

INDUSTRY TRENDS
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with automated scoring and feedback on essays. “The nice part about that is it helps students get some feedback early in a course when the instructor might not have the resources to give all the feedback they would like to,” said John Behrens, vice president of advanced computing at the company’s Data Science Lab. “It is a way to provide feedback in complex performances, which is one of the main ways we are going to see machine learning go in helping people through the learning process.”
Pearson’s MyLab and Mastering adaptive learning solutions have features based on machine learning that recommend different types of practices to optimize learning, as well as help instructors identify struggling students, he added.
Behrens is enthusiastic about the pace of change in the field. “I cannot tell you how exciting it is to be in this space at this time,” he said. “With the revolution that is happening in deep learning and other advances in machine learning, the speed of change and innovation is increasing exponentially. We have a lot more data than we ever had, and we have the ability to manipulate that data through networks and high- speed, scalable computing through the new cloud environment. Combine that with the new inferential techniques that are largely open sourced from the big technology companies like Google and Facebook, and the rate of change for technological improvement is just remarkable.”
When asked about creating virtual teaching assistants like Jill Watson, Behrens said Pearson is working on similar solutions, but he cautioned that it is not easy. Creating a chatbot for a specific course requires a certain set of tools and data, he explained, “but to scale that across disciplines, where each discipline has its own way of talking or thinking and its own professional standards, that takes another level of sophistication in machine learning, but also in understanding the educational and social ecosystem.”
David Raths is a freelance writer based in Philadelphia. CAMPUS TECHNOLOGY | January/February 2019
Most commercial software vendors have focused their initial machine learning work on recruiting and student success, because the predictive models address problems where the return on investment can be measured. But what about applying machine learning to pedagogy? “There is not a lot of work being done around use of AI and machine learning in teaching and learning,” said Kyle Bowen, director of education technology services at Penn State University. “We describe it as a moonshot. If we can figure this out, we could have a dramatic impact about how people think about open educational resources [OER], active learning and the design and development of courses. The goal is to support the faculty so they have time to be more creative.”
PSU has developed machine learning tools to help faculty choose appropriate OER materials and identify the prerequisite knowledge a person would need in order to understand a particular body of text. (See “How Machine Learning Is Easing OER Pain Points.”) The university also is working on a prototype algorithm that, given an OER chapter or a textbook, can suggest multiple-choice assessments and distractor questions.
One of the newest areas PSU is working on is something Bowen dubs a “Fitbit for Teaching.” His team has set up microphones in an experimental teaching classroom to capture audio levels and conversations like a lecture capture tool might do. “Using machine learning tools, we do an analysis of the interactions in the classroom to identify what types of activities are happening,” he explained. For instance, the system might measure the amount of time spent on direct instruction vs. other types of activities. The intent is to provide feedback to instructors on what actually happened in class, so they can compare that with their intentions and fine-tune their instruction.