CHECKPOINT
Misconception-focused assessment item generation tool developed by VIABLE Lab
VIABLE Lab led by Dr. Anthony Botelho
Versatile Innovations in Affect, Behavior, and Learning Engineering
About the Project
CHECKPOINT is designed to generate assessment items that specifically target common misunderstandings and errors exhibited by students. This approach helps educators identify and address learning gaps more effectively.
For Educators
Helps identify and select content that is best suited to gauge student learning and pinpoint specific misconceptions
For Researchers
Supports the creation of psychometrically stable pretests and post-tests to evaluate learning interventions
See CHECKPOINT in Action
Watch our early concept video to understand how CHECKPOINT helps teachers create targeted assessments that reveal student misconceptions and support data-driven instruction.
CHECKPOINT: Early Concept Video for Misconception-Focused Assessment
Learn how CHECKPOINT supports teachers and researchers in generating targeted assessments that attend to common errors and misunderstandings.
Leadership Team
Principal investigators leading the CHECKPOINT project
Dr. Anthony Botelho
Principal Investigator
Assistant Professor
Spearheads the overall project vision and research direction, specializing in learning analytics and AI-driven educational technologies.
Dr. Jinnie Shin
Co-Principal Investigator
Assistant Professor
Leads the educational assessment and psychometric aspects of the project, bringing expertise in learning sciences and measurement theory.
Dr. Avery Closser
Co-Principal Investigator
Assistant Professor
Leads teacher engagement and professional development, while also contributing cognitive psychology perspectives and educational technology expertise to enhance the platform's learning effectiveness.
Research and Development Team
Team members contributing to CHECKPOINT development and evaluation
Seiyon M. Lee
Ph.D. Student
Develops evaluation frameworks and implements teacher experimental designs to assess learning outcomes and platform effectiveness.
Hongming (Chip) Li
Ph.D. Student
Leads platform development, AI infrastructure design, and user experience optimization to create an intuitive and powerful assessment tool.
Shan Zhang
Ph.D. Candidate
Contributes to experimental design and conducts comprehensive product evaluation and analysis to validate CHECKPOINT's effectiveness.
Zhongtian Huang
Ph.D. Student
Specializes in statistical analysis and data interpretation to derive insights from assessment data and improve platform algorithms.
Natalia S. Martín
Ph.D. Student
Designs and implements research experiments, conducting thorough evaluations to validate educational assessment methodologies.
Zhe (Henry) Li
Ph.D. Student
Conducts psychometric measurements and statistical analysis to ensure assessment validity and reliability in educational contexts.
Xintian Gao
Ph.D. Student
Focuses on learning sciences research, designs educational experiments, and conducts data analysis to understand student learning patterns and misconceptions.
Acknowledgment
We thank the Tools Competition for recognizing CHECKPOINT with the Catalyst Prize for its innovative approach to educational assessment technology.
Resources
Learn more about CHECKPOINT and the Tools Competition recognition.