Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Impacts of students' experimentation using a dynamic visualization on their understanding of motion
ICLS'08 Proceedings of the 8th international conference on International conference for the learning sciences - Volume 2
Designing and assessing modeling and visualization technologies (MVT) enhanced learning
ICLS'08 Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3
Looking inside the black box: assessing model-based learning and inquiry in BioLogica™
International Journal of Learning Technology
Quiet captures: a tool for capturing the evidence of seamless learning with mobile devices
ICLS '10 Proceedings of the 9th International Conference of the Learning Sciences - Volume 1
Helping students make controlled experiments more informative
ICLS '10 Proceedings of the 9th International Conference of the Learning Sciences - Volume 1
Applying analytics for a learning portal: the Organic.Edunet case study
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Improving construct validity yields better models of systematic inquiry, even with less information
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
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We report on the Modeling Across the Curriculum Project (mac.concord.org), a 5-year study funded by the IERI program (IERI # 0115699). In this project we are using qualitative model-based tools for inquiry in three science domains (Genetics, Gas Laws, & Newtonian Mechanics) to promote scientific literacy on a broad scale, namely, content knowledge, process skills, and epistemologies of science (Perkins, 1986). The process skills we focus on in this project are model-based inquiry skills; epistemological constructs of interest here are students' epistemologies of models as a subset of epistemology of science (Gobert & Discenna, 1997; Schwartz & White, 1999). Our technological infrastructure, Pedagogica™ (Horwitz & Burke, 2002) logs all students' interactions with our hypermodels (Horwitz & Christie, 1999). We use students' log files as performance assessments of model-based inquiry on inquiry tasks called "hot spots" and track changes in inquiry skills over time both within and across domains.