To dig into massive datasets to understand students better

Research at NCEO is helping states analyze student data

Have you ever wondered how the education numbers on the news were calculated, or how the data were collected? If research design and data collection are not well planned, it is just “garbage in and garbage out.” That may seem harsh, but if the data analysis approach is not correct, results are meaningless. It’s important to know the data starting from the design, its purpose, and the collection process to choose the right method to analyze and interpret the data meaningfully. Yi-Chen Wu’s job is to help researchers at the National Center on Educational Outcomes (NCEO) identify and carry out appropriate study designs and data analyses.

Over decades, Yi-Chen Wu has worked with a variety of educational data, including formative assessment data, state assessment data, survey data, and more. Her goals are to help others understand the student stories the data present. Her expertise lies in data analyses, as well as in research design. Yi-Chen Wu has contributed to several projects to explore student’s performance longitudinally, including English learners (ELs) and students receiving special education services. Through her work she explores the characteristics of student achievement, unveiling how students are learning.

Learn more about educational assessment and intervention at ICI

Photo of two students using a computer
Photo of two young female students working in the classroom.
“We need to understand the data first to be able to tell a story about the data.”

In collaboration with the National Center and State Collaborative (NCSC), which is developing a multi-state comprehensive assessment system for students with significant cognitive disabilities, Yi-Chen Wu explores students’ characteristics and performance. The long-term goal of the project is to ensure that students with significant cognitive disabilities achieve increasingly higher academic outcomes and leave high school ready for post-secondary options. This goal makes all data analyses incredibly important and worthwhile.

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