STEM

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

alxbclrk@washington.edu — Wed, 22 Nov 2023 01:30:53 +0000

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

August 7, 2023

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 ��ߺ��, 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 ��ߺ�� – 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: /isea

Learn more about the ISEA training program from Dr. Min Sun, professor in the UW ��ߺ�� 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 ��ߺ�� 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 ��ߺ�� 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 ��ߺ��
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 ��ߺ��
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

Contact

Min Sun

Professor

misun@uw.edu

Research team led by Professor Min Sun wins $1.5 million NSF grant

alxbclrk@washington.edu — Mon, 20 Nov 2023 22:26:02 +0000

Research team led by Professor Min Sun wins $1.5 million NSF grant

August 31, 2023

An innovative new project that leverages machine learning to assess mathematics lesson plan quality in the middle grades has been selected by the National Science Foundation (NSF) to receive a $1.5 million grant. Through this project, a cross-university research team led by principal investigator Dr. Min Sun of the ��ߺ�� ߺ�� will identify ways to measure the quality of large quantities of open-source mathematics lesson plans using an integration of cutting-edge machine learning techniques, knowledge of effective mathematics education and human feedback.

As online instructional materials continue to proliferate, schools and educators are increasingly relying on them for lesson planning, underscoring the need to evaluate the quality of these materials. Planning and selecting instructional materials are among the most complex and important components of mathematics teaching, and this project aims to make it easier for middle school mathematics teachers to design lesson plans that are effective and support positive learning outcomes for students.

[W]e are addressing equity issues because junior teachers spend more time on lesson planning, as do teachers serving historically marginalized students and communities.
-Dr. Min Sun, UW ��ߺ��

“This project will democratize access to quality, inclusive and tailored learning materials that benefit students and support their teachers’ planning processes,” Dr. Sun shares.

“Beyond scientific contributions, we are addressing equity issues because junior teachers spend more time on lesson planning, as do teachers serving historically marginalized students and communities,” she elaborates. “It’s critical that teachers’ lesson plans, especially in mathematics instruction, effectively supports students with a wide range of academic performance levels, language and cultural backgrounds.”

Teachers are increasingly turning to online platforms and social media sites to supplement their district curricula, either for content enrichment or to make lessons more interactive and culturally relevant. Research reveals that a vast majority of teachers use search engines like Google and platforms like TeachersPayTeachers and Pinterest to source lesson materials. With the rise of ChatGPT, a recent survey conducted by the Walton Family Foundation shows that 40% of teachers use ChatGPT on a weekly basis for tasks such as lesson planning and building background knowledge for lessons. This increasing shift towards digital resources is also supported by the burgeoning movement of Open Education Resources (OER) under the Creative Commons License. This movement is backed by state agencies, including in Washington state, prominent non-profit organizations and even district or university initiatives. Consequently, many school districts nationwide have chosen OER materials as their primary curricula. These shifts highlight the importance of developing a quality checker of online content to better support teachers and student learning outcomes.

A big promise of AI is that it will help relieve teachers from many routine tasks, including lesson planning, so they can spend more time working with students.
-Dr. Jing Liu, University of Maryland ��ߺ��

“Lesson planning is a critical yet not well-studied component of teaching,” says Dr. Jing Liu of the University of Maryland School of Education and a co-principal investigator of this project. “Despite their busy schedules, teachers spend a lot of time developing their lesson plans, but there is little guidance on how to evaluate and identify high-quality lesson plans. A big promise of AI is that it will help relieve teachers from many routine tasks, including lesson planning, so they can spend more time working with students.”

“Importantly, teachers’ input and voices are centered in this work throughout, so machine learning techniques are used in a very responsible manner,” Dr. Liu adds.

The use of machine learning to measure lesson plan quality holds transformative potential for the field of mathematics education. The team has chosen to focus on assessing the quality of online instructional materials intended for middle-grade math because this is the period when math content starts to become more complex.

"Students need to learn foundational math concepts and skills and build connections among them, to set themselves up for success in STEM learning in high school,” Dr. Sun explains. “In conjunction with the unique challenges adolescents face in terms of their physical and social-emotional development, middle school math becomes a fruitful subject and grade area for this project to focus on.”

The team will develop artificial intelligence- and machine learning-powered algorithms, in collaboration with human expert judgement, to check a lesson plan’s content rigor, engagingness of activities and inclusivity for students with language and special education needs. The algorithms and data practices can be used to develop effective and responsible AI products for teachers.

As teachers increasingly turn to the Web for resources, our research is pivotal in addressing information overload and guiding them toward high-quality lesson plans.
-Dr. Wei Ai, University of Maryland College of Information Studies

"Our project combines the collective wisdom of educational experts with the power of machine learning in processing large quantities of data. As teachers increasingly turn to the Web for resources, our research is pivotal in addressing information overload and guiding them toward high-quality lesson plans. The recent rise of large foundational models (such as those used behind ChatGPT) only intensifies the urgency of this initiative, as it enables the machine learning community to develop their models using only the best in educational content," says Dr. Wei Ai, co-principal investigator and faculty member at the University of Maryland College of Information Studies.

Findings from this project will be shared through the AmplifyLearn.AI Center website, which will launch in October 2023. Led by Dr. Sun, the center is a cross-institution collaboration that explores AI research in education, AI-powered EdTech product development and education data science training and outreach. Additionally, all educators will soon have open access to Colleague, a K-12 online lesson planning platform developed by a multidisciplinary team at the ��ߺ�� led by Dr. Sun that hosts a comprehensive collection of vetted lesson plan materials. The Colleague platform will be launching beta testing this fall and educators and schools are encouraged to stay tuned for updates and opportunities to be part of this testing phase.

In addition to Drs. Ai and Liu, Dr. Sun’s co-principal investigators under the NSF grant include Dr. Lorraine Males of the University of Nebraska – Lincoln, ��ߺ�� and Dr. Melissa Boston of Duquesne University School of Education.

This project is supported by the NSF’s EDU Core Research (ECR) Program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments; broadening participation in STEM; and STEM workforce development.

Contact

Charleen Wilcox

Director for Marketing & Communications

wilcoxc@uw.edu

Stem Is a Culture, Not a Curriculum — Liza Rickey

alxbclrk@washington.edu — Thu, 16 Nov 2023 23:57:45 +0000

Stem Is a Culture, Not a Curriculum — Liza Rickey

Published

April 8, 2019

Liza Rickey (Danforth ’17), assistant principal at Sartori Elementary, describes how her school is building a culture where students are empowered to see themselves as problem solvers and advocates for positive change in their community.

Gift supports efforts to diversify the educator workforce

Anonymous — Fri, 16 Jun 2023 03:45:10 +0000

Gift supports efforts to diversify the educator workforce

January 24, 2020

We firmly believe that investing in human capital — well-prepared teachers who represent the students in their community — is one of the best ways to decrease the opportunity and achievement gaps that exist today.

An endowed fellowship created by the Satya and Rao Remala Foundation will focus on supporting future educators with a desire to teach STEM-related subjects and a demonstrated commitment to diversity, equity and inclusion.

Born and educated in India, Rao Remala worked on many of Microsoft’s best-known products, including Windows 1.0, Windows 2.0 and Office 95. Rao and Satya Remala’s daughter, Srilakshmi Remala, currently manages the family foundation’s education portfolio and has an extensive career within education systems change work including STEM, personalized learning, college success and social emotional learning.

“We firmly believe that investing in human capital — well-prepared teachers who represent the students in their community — is one of the best ways to decrease the opportunity and achievement gaps that exist today,” said Srilakshmi Remala. “We hope this will inspire others to come forward and support future teachers who reflect the diversity of our schools.”

All students benefit from a diverse educator workforce — and for students of color the benefits can literally be life-altering. Exposure to even one teacher of the same race can significantly improve the odds a student of color will be placed in a gifted education program, graduate from high school and attend college. Yet in Washington state, where nearly half of K-12 students are people of color, only 11 percent of teachers are people of color.

One of the biggest barriers to efforts in Washington state and nationally to diversify the educator workforce is the debt load aspiring teachers take on to enter the profession, a burden that falls most heavily on aspiring teachers of color.

The Satya and Rao Remala Foundation Endowed Fellowship for Equitable Teaching, created with a $105,000 gift, will support one UW teacher candidate annually, starting immediately.

“There are many people who want to give back to their communities, but they’re not able to access graduate-level preparation without financial support,” said Rao Remala. “We want individuals who feel the call to teach to have that opportunity to pursue their dreams.”

Mia Tuan, dean of the UW ��ߺ��, said the fellowship will strengthen the College’s ongoing efforts to bolster the recruitment and retention of teachers of color in Washington state and beyond.

“We are grateful to the Remala family for their generous gift,” Tuan said. “Expanding the pipeline of skilled educators into socioeconomically diverse schools is a priority for the College because we know teachers make a huge impact on their students and the opportunities they have to achieve their potential.”