Grant recipient: John McLevey, Department of Knowledge and Integration
Project team: John McLevey; Reid McIlroy-Young*, Physics; Sasha Graham**, Sociology
*Undergraduate research assistant
**Graduate Student
(Project timeline: September 2015 - August 2016)
The picture above illustrates a network of the relationships between academic journals based on millions of journal articles.
Project Summary
One of the primary learning outcomes of UW’s Bachelor of Knowledge Integration (KI) undergraduate degree is to develop a sociological, historical, and philosophical understanding of the production of disciplinary and interdisciplinary knowledge. Students are first introduced to this in “INTEG 120: Disciplines and Integrative Practices”, and then deepen their knowledge in subsequent courses. In my experience teaching INTEG 120, it seems clear that readings and lectures are not facilitating deep learning.
The goal of this project is to facilitate deep learning about disciplinary and interdisciplinary knowledge production by taking a more interactive and data-driven approach, and secondarily to teach skills for reproducible research. To accomplish these goals, I will design course material for two KI courses using new open source tools for interactive documents and data visualization: Jupyter Notebooks and R Shiny.
Questions Investigated
Does taking a more interactive and data-driven approach in the classroom (e.g. by emphasizing interpretations of data rather than textbook readings) improve student understanding and learning?
Findings/Insights
In my classes so far, the answer appears to be yes. I have not yet been able to conduct formal interviews or surveys, but the classes are small enough that I have been able to observe qualitative changes in how students learn. For most students, I have seen increased levels of focus and more sophisticated questions. For two offerings in a row, I have seen students develop a much stronger grasp of the way disciplinary and interdisciplinary knowledge is produced, and how it “travels” or diffuses. While there are some exceptions (2 students who felt they didn’t understand the point of using computers), the overwhelming consensus at the end of the semester was that we should spend more time working with the software and data. Of course, it is possible that the improvements are simply due to there being a new cohort of students. I need to continue doing this for a few years, and I need to introduce surveys and interviews for evaluation purposes. But so far, changing to a more data-driven approach has shown significant improvements in student engagement, understanding, and learning.
Dissemination and Impact
- At the individual level: I have discussed these methods and changes with colleagues extensively, most frequently with my department chair.
- At the Department/School and/or Faculty/Unit levels: The impact on my department is primarily in the form of better prepared and more knowledgeable first-year students. As the move through the KI core course sequence, they have a much stronger understanding of the scale, complexity, and basic organization scientific knowledge production.
Impact of the Project
- Teaching: Because I was uncertain about how effective this approach was going to be, I kept about 2/3 of the content of the first year course the same and introduced the new data-driven component for only 1/3. I now realize, especially comparing student engagement and learning across the two types of content, that the data-driven approach is far superior for the learning objectives in this specific course. As such, the fall 2016 version of the course will be entirely focused around the innovations that came from this LITE Grant funded research.
- Connections with people from different departments, faculties, and/or disciplines about teaching and learning: At the student level, I have worked extensively with a student from physics. He eventually became part of my lab group and his work on this project (and my other research) got him hooked on the science of science literature. His work on this project helped him get into his desired program in computational social science.
References
Project Reference List (PDF)