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Sao Pedro, Michael A.; Gobert, Janice D.; Betts, Cameron G. – Grantee Submission, 2014
There are well-acknowledged challenges to scaling computerized performance-based assessments. One such challenge is reliably and validly identifying ill-defined skills. We describe an approach that leverages a data mining framework to build and validate a detector that evaluates an ill-defined inquiry process skill, designing controlled…
Descriptors: Performance Based Assessment, Computer Assisted Testing, Inquiry, Science Process Skills
Gobert, Janice D.; Kim, Yoon Jeon; Sao Pedro, Michael; Kennedy, Michael; Betts, Cameron – Grantee Submission, 2015
Many national policy documents underscore the importance of 21st century skills, including critical thinking. In parallel, recent American frameworks for K-12 Science education call for the development of critical thinking skills in science, also referred to as science inquiry skills/practices. Assessment of these skills is necessary, as indicated…
Descriptors: Learning Analytics, Science Education, Teaching Methods, 21st Century Skills
Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D. – Grantee Submission, 2013
When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…
Descriptors: Educational Research, Data Collection, Data Analysis, Generalizability Theory