Apply Now: A new data science training program to advance educational research and practice

With the rise of artificial intelligence (AI) in all aspects of society, there has been an increasing talent gap in AI machine learning, especially within data science in education. Through a collaboration between the UW College of Education, the UW eScience Institute and faculty from other higher education institutions including the University of Oregon and the University of Maryland, a training program called Innovation Science for Education Analytics (ISEA) will launch in January 2024 thanks to a 3-year grant from the Institute of Education Sciences (IES).

The current use of education technologies is generating large quantities of data at an unprecedented speed. These data are largely unstructured or noisy, meaning they are not arranged according to a preset data model or have many complicated attributes, which will make these data difficult to use with conventional analytic tools and techniques.

This new training program will prepare education researchers to use advanced supervised and unsupervised machine learning and natural language processing methods, along with human coding, to extract meaningful insights from education data. Program trainers will also address data ethics and professionalism. The program will recruit cohorts of 15 to 20 participants per year for three years (50-60 participants total) for an intensive 7-month training that includes 15 weeks of online, webinar-based learning, a 1-week in-person workshop at the University of Washington – Seattle campus and continuous virtual mentoring from the project team.

The training is recruiting education researchers, school district data analysts and education technology employees who have some background in statistics. Graduate students, post-doctoral students, early career researchers and assistant professors who are interested in deepening their knowledge of engineering and machine learning, specifically for education data science, are encouraged to apply. District and state data scientists as well as professionals who want to begin their careers in education technology are also encouraged to apply.

To apply, please visit: https://education.uw.edu/isea

Learn more about the ISEA training program from Dr. Min Sun, professor in the UW College of Education and principal investigator for the project.

Why is this data science training program needed now?

ISEA is actively working to address the talent shortage in the exciting age of AI and big data in education. It's long been known that education systems gather lots of detailed, long-term data on student traits, learning outcomes, teacher demographics and school finance. However, the recent rise in big data brings an explosion of unstructured, messy data, such as digital learning materials (text, videos, images), social media chatter and e-learning platform usage patterns. Traditional tools and methods aren't built to handle this kind of data, so to unearth valuable insights and fuel data-driven practice, we need to turn to modern data science methods like machine learning (ML) and natural language processing (NLP).

Our goal is to nurture skills in three crucial areas:

• K-12 data analysis: With schools increasingly leaning on AI and machine learning services from vendors like PowerSchool, Panorama Education, BrightBytes and Khan Academy, it's vital that they understand the basics of data science. This knowledge allows them to weigh the pros and cons of using computational analytics for evidence-based resource allocation and real-time interventions (such as adaptive learning and online tutoring). It's also crucial for sifting through the data to draw valuable, context-specific insights.
• Educational research: There are numerous fundamental methodological issues and deep-seated questions regarding the impact of ML and AI applications in education. We need researchers in higher institutions who can address these issues head-on.
• Education technology (EdTech): For ML/AI products to genuinely aid student learning, innovation in EdTech firms must be rooted in educational domain knowledge - think curriculum and instruction, learning science and child development, as well as understanding school systems and administration.

Our training program is designed to serve these needs, offering a fresh talent pool to EdTech firms, school districts and research institutions. These individuals will possess integrated expertise in engineering, statistics and education, enabling them to address K-12 specific needs and understand the ethical considerations of data use.

Are there misconceptions or misunderstandings about AI and machine learning that you hope to combat with this training program?

Absolutely, there are misconceptions about AI and ML that we aim to address through our training program.

One common misunderstanding is that AI/ML are sort of magic boxes - you feed in data and out comes perfect insights. These tools require careful management and understanding. The algorithms they use are only as good as the data they're trained on, and their results need to be interpreted using education domain knowledge. If the data is biased, the outcomes can be too. This is why we place a strong emphasis on teaching the basics of data science, to equip individuals with the ability to critically analyze and understand the data they're working with. This is also why we emphasize using educational domain knowledge to inform data analytic and data interpretation.

Another misconception is that AI and machine learning can replace human decision-making in education. While these technologies can certainly assist in decision-making by providing valuable insights, they don't replace the need for experienced educators and administrators who understand the unique needs and context of their students and schools. AI and ML should be seen as tools to support and enhance human decision-making, not replace it.

Lastly, there's often a lack of understanding about the ethical considerations related to AI and big data in education. Issues such as data privacy and the potential for algorithmic bias are incredibly important. Part of our training program is focused on ensuring our students understand these data ethics considerations.

By addressing these misconceptions through our training program, we hope to help create a workforce that is not only technically skilled, but also thoughtful and ethical in its application.

Why is the collaboration with different departments and education institutions for this training program crucial for its success?

Great question! The collaboration with different departments like the College of Education and the eScience Institute, along with sectors like K-12 schools, higher education, and EdTech, is essential to the success of this training program.

First, each of these departments and sectors brings unique expertise and perspectives to the table. The College of Education has deep insights into the theories and practices of teaching and learning, while the eScience Institute brings in the technical know-how around AI, machine learning, and big data. The collaboration of these diverse fields enhances the richness and comprehensiveness of the training program.

Second, in terms of sectors, K-12 schools are where a lot of the data comes from and where the applied work is being done. Higher education institutions are not only sources of research and thought leadership, but they also provide a direct link to up-and-coming talent entering the field. And EdTech companies create most of the cutting-edge technology applications in education, distribute them to schools. Involving representatives from each of these sectors ensures the program stays grounded in real-world needs and challenges while staying abreast of the latest advancements.

Finally, we hope this multidisciplinary, multi-sector collaboration can serve as a national model for data science training in education. The future of educational big data and AI will inevitably involve collaboration across fields and sectors. By embedding this in our training, we are preparing our students for the real-world dynamics they'll encounter in their careers.

What do you hope participants will walk away with at the end of the program?

At the end of the program, the number one thing we hope our participants will walk away with is an integrated understanding of AI, machine learning and data science as it applies to the field of education.

We want them to not only be adept at the technical aspects of handling big data and utilizing AI tools, but also to fully grasp the unique context of educational systems and the ethical considerations surrounding data use. We aim for our participants to understand how these advanced tools can be applied to real-world educational challenges to enhance learning outcomes and support school improvements.

Acknowledgements

​​PI and Co-PIs

• Dr. Min Sun (PI), professor in the UW College of Education
• Dr. Lovenoor Aulck (Co-PI and managing director of ISEA), data scientist at the UW Provost’s Office and affiliate faculty at the UW Information School
• Dr. Sarah Stone (Co-PI), executive director of the UW eScience Institute
• Dr. David A. C. Beck (Co-PI), director of education and research at the UW eScience Institute, director of the UW Scientific Software Engineering Center and research associate professor in engineering
• Dr. Patrick C. Kennedy (Co-PI), senior research associate in teaching and learning, University of Oregon

Expert Faculty

Expert faculty will instruct individual webinar sessions pertaining to their interests and expertise, mentor fellows on their projects and engage in generative discussions.

• Dr. Jing Liu, assistant professor, University of Maryland
• Dr. David Knight, associate professor, UW College of Education
• Dr. Wei Ai, assistant professor, University of Maryland
• Dr. Christopher Candelaria, assistant professor, Vanderbilt

Advisory Board

We have recruited renowned scholars in education data analytics and industry leaders in K-12 and EdTech to guide the ISEA training and advise on program design, evaluate program success and offer suggestions for improvement.

• Dr. Susanna Loeb, director of the Annenberg Institute at Brown University and a member of the National Academy of Education and the American Academy of Arts and Sciences
• Dr. Zachary Pardos, associate professor of education at University of California - Berkeley
• Mr. Adam Geller, founder and CEO of Edthena
• Dr. Lief Esbenshade, senior product analyst with Google Education
• Dr. Eric Anderson, director of research and evaluation at Seattle Public Schools
• Dr. Kristin Tolle, former director of Data Science Initiative in Microsoft Research