I’m excited to be offering a new course on data science methods in education next semester.
A flyer for the course is here; some key deets:
- TPTE 595 (MA) and TPTE 695 (PhD)
- Day/time: Thursday, 2:50 - 4:05 pm
- Modality: Online, partially synchronous
- Designed for students with no prior experience with R or data science
- Focused on education, but open to any graduate student in TPTE, CEHHS, and across the University
Here is a bit more information from the proposal for the course:
The over-arching goal of the course is to support graduate-level students across the Department of Theory and Practice in Teacher Education (and the Bailey Graduate School of Education) to be empowered to use new data sources and research methods in their research. Accordingly, the course will provide students with a foundation in data science capabilities, defined as those that integrate computation with statistics and substantive expertise. Also, the course will include opportunities for students to gain experience with specific data science techniques that may be widely used across research projects.
Students will have the opportunity to bring their own data from their research projects for use in this class. In this way, they will have immediate application for the concepts learned in the course. Bringing in one’s own data will be highly encouraged, as the work done in this class could serve as the foundation of a future conference proposal or publication for the student. If no data is immediately available from the student’s research, students can use one of hundreds of freely available datasets to complete coursework or students can use datasets provided to them.
My goal in teaching this course will be to catalyze students’ interest in data science and to bolster their confidence in their abilities to use programming techniques to support their research programs. Many people who try to self-teach become overwhelmed by available resources. This course will provide scaffolding to help students become proficient in a few sophisticated data science techniques, and it will give students enough foundational knowledge to pick up new data science skills on their own after the course is through.
The objectives for the proposed course are for students to be able to:
- Install, set up, and use R and RStudio
- Use reproducible workflows (so that analyses can easily be modified and then carried out again by the analyst or others) with R Markdown
- Prepare and explore complex data sources for analysis using the tidyverse suite of R packages
- Create a social network data structure and create a network visualization
- Carry out an automated text analysis
- Access and analyze social media-based data related to a topic of interest
- Understand how issues of equity, privacy, and ethics are central to data science in education
- Develop a personal learning and development plan related to data science in education
These objectives will serve as a foundation for later data science in education-related courses, including data visualization, creating interactive web applications, and machine learning applications.
Textbook for the course (available open-access):
Estrellado, R. A., Freer, E. A., Mostipak, J., Rosenberg, J. M., & Velásquez, I. C. (2020). Data science in education using R. London, England: Routledge. Freely-available from: http://www.datascienceineducation.com/