Sijia Zhang
University of North Carolina at Charlotte, NC·Computer Science
Education researchers now have access to large and varied data sets. They could use these data to judge whether policies and teaching methods work and to help close achievement gaps.
- Fields
Computer Science
Education Research
- Topics
AI in education
artificial intelligence
machine learning
- Grants
- 1 known grant, through Aug 2028
What they work on
- Develop teaching materials on planning, configuring and using cloud computing resources and frameworks such as Hadoop and federated learning for educational analytics tasks.
- Build software tools for data quality checks, cloud monitoring, and cloud planning and configuration to help education researchers use cloud services.
- Design hands-on sample projects with accompanying real-world education data sets, and build a public repository of machine learning programs and data sets for education research.
More about the research
Artificial intelligence (AI) and cloud computing can help them analyze these data, but most education researchers, school administrators and policymakers are trained only in conventional statistics. This collaborative project, AI4EDU, aims to bridge that gap. It will train education researchers to use cloud infrastructure for AI-driven research and assessment, bringing computing resources to social scientists rather than only natural scientists.
Why it matters
Education researchers and policymakers often lack the computing skills to analyze large education data sets. Training materials, cloud tools and shared data sets from this project could help them use AI to evaluate education policies and teaching methods and to study achievement gaps more effectively.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sheikh Rabiul Islam
University at Albany, NY·Computer Science
Develop seven AI4SecureCI learning modules that teach fair and explainable AI for securing cloud cyberinfrastructure, covering topics from malware detection to adversarial attacks, and integrate them into undergraduate and graduate courses.
Brendan Eagan
University of Wisconsin-Madison, WI·Education Research
Train three cohorts of 30 mostly underrepresented early-career researchers in quantitative ethnography (QE) through the Accelerator, Incubator, Booster and Multiplier workshop sequence covering methods, feedback, writing and teaching.
Tahir Ekin
Texas State University, TX·Computer Science
Develop an Applied AI Summer School and new course modules in data analytics, Python, deep learning, natural language processing and computer vision for students in non-AI disciplines, using inclusive and contextualized teaching methods.
The school
University of North Carolina at Charlotte: 9 labs on PI Pages, $95M a year on research, 196 research doctorates a year.
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