Leveraging In-Context Online Discussion of Course Materials to Enhance Student Engagement and Learning

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With support from NSF's Improving Undergraduate STEM Education: Education and Human Resources (IUSE: EHR) program, this project aims to serve the national interest by enhancing the value of online learning environments. Teaching and learning are increasingly happening online. Interactive texts and videos delivered through the Internet have the potential to lower the cost of content delivery, promote greater learning than traditional methods of content delivery, and provide more personalized and equitable access to education. To be successful and broadly adopted, however, any new online educational platform must provide greater value for the students and instructors than the technologies that it replaces. One promising idea to enhance the value of online learning environments is to leverage their ability to enable interactivity between students and instructors by using "smart" interactive technologies that can provide customized feedback to both groups. In this project, researchers at the University of California, Davis and Massachusetts Institute of Technology will build new understandings of how student engagement with online content can be shaped to enhance learning. Student engagement will be captured in short written notes, comments, and questions that students place in the margins of documents, web pages, and videos. By capturing feedback about how students feel about and engage with online resources, instructors will be able to modify digital resources and restructure classroom time to be more effective. Students will get an opportunity to engage with and reflect more deeply on the course content, connect with other students to discuss course content, and engage the instructor in new ways. The project will improve students' and instructors' experiences with online content, increase the value of online content delivery, and contribute to the personalization of online resources.The investigators will build upon NotaBene, a system that allows students to discuss online course content (PDFs, websites, and YouTube videos) in the margins of those content sources. The investigators will gather information about student engagement by mining the discussion content in the margins and will present information about student engagement to instructors, help the instructors make use of this information for class design and for interacting with students, and test the hypothesis that increasing engagement leads to better learning outcomes. The analytical models in the research will consider three distinct types of engagement: emotional engagement (a measure of how students feel about what they are interacting with online; e.g., interest;curiosity; confusion; boredom); cognitive engagement (a measure of how deeply students are thinking about what they are interacting with); and temporal engagement (a measure of how much time students are spending interacting with different parts of the online content). Collecting these three measures of engagement will allow the investigators not only to look at each type of engagement independently but also to uncover how they are interrelated. Understanding the interplay between these types of engagement should enable instructors to respond with more finely tuned and effective interventions. The enhancements to NotaBene, the accompanying visualization tools, and the analyses that result from this project will give students and instructors greater insight into how students are learning from online content, and this insight will enable instructional and content corrections to overcome barriers to learning and facilitate greater student engagement online. NSF's IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.