This project aims to create an innovative explanation-based classroom response system, ExplainIt, that provides real-time support to undergraduate STEM students and instructors by using natural language processing to analyze student explanations of STEM phenomena. Traditional classroom response systems, such as “clickers,” are ineffective at promoting deep and meaningful learning because students simply select an answer from a list of choices rather than enabling construction or generation of their own responses to questions, which is a key component of active learning. ExplainIt will address this challenge by enabling students to generate short-answer textual explanations to prompts posed by instructors during lectures, which could open a rich communication channel between instructors and students. Leveraging natural language processing, ExplainIt will support automatic assessment of student explanations of STEM phenomena, provide real-time formative feedback to each individual student and real-time summaries to instructors, and enable instructors to quickly adapt their instruction to meet the current needs of their students, thereby promoting undergraduate students’ STEM learning and engagement.
2. Guiding Questions
Three research questions guide this project: (1) How can we create the ExplainIt explanation-based classroom response system such that it accurately assesses student explanations and provides meaningful feedback? (2) How can we derive significant research results on adaptively supporting student learning and engagement with ExplainIt? (3) How can we develop online resources that enable instructors to easily integrate ExplainIt into their teaching? Each year, instructors in biology, computer science, and physics will contribute to the design, development, and evaluation of the ExplainIt software. The project will investigate conditions under which improved student learning occurs by evaluating ExplainIt in a wide range of undergraduate STEM courses.
The project will produce significant theoretical and practical advances in undergraduate STEM education. It will lead to a deeper understanding of how students learn with explanation-based classroom response systems, including the learning gains and improvements in student engagement. It will also lead to a set of effective instructional support principles for explanation-rich classroom interactions that will be broadly applicable in multiple STEM disciplines and in diverse institutional settings. Together, these advances will yield fundamental improvements in undergraduate STEM education.
4. Broader Impacts
In Year 1, the ExplainIt project will engage with 600 students through interviews, focus group studies, and pilot studies in introductory courses, lower-division courses, and upper-division courses. The evaluation will have a dual focus: evaluating ExplainIt’s effects on students’ STEM learning (conceptual knowledge, problem solving), and evaluating ExplainIt’s effects on improving students’ engagement in STEM (STEM self-efficacy, STEM interest). In addition, the project team will grow the ExplainIt community of practice through workshops and presentations at conferences on biology education, computer science education, and physics education, as well as through webinars and social media.
James Lester, North Carolina State University, Raleigh, NC; Gamze Ozogul, Indiana University Bloomington, Bloomington, IN