1. Fostering Learners’ Collaborative Problem Solving with RiverWeb Roger Azevedo University of Maryland Mary Ellen Verona Maryland Virtual High School Jennifer G. Cromley University of Maryland

2. Acknowledgements Maryland Virtual High School (MVHS) Susan Ragan, Stacey Pitrech, Marylin Leong National Center for Supercomputing Applications (NCSA) David Curtis National Science Foundation (NSF) University of Maryland Myriam Tron

3. Overview Introduction Context - MVHS - NCSA - UMCP RiverWeb Framework and Curriculum Design Principles Research Questions Present Study Method Results Summary Future Directions

4. RiverWeb - Water Quality Web-based Simulation

5. RiverWeb

6. RiverWeb - Notebook

7. RiverWeb - Scatterplots & Help

8. Framework & Curriculum Design Principles Context Meaningful problem space that provides intellectual challenges and sustains engagement Driving Q’s, sub-questions, anchoring event Standards based Larger community of experts that defines the language and methods of the larger community AAAS benchmarks, State & county science objectives Inquiry The accepted method of the scientific community for solving problems Asking Qs, data collection, organization, and data analysis, sharing and communicating data

9. Framework & Curriculum Design Principles Collaboration Interaction among students, teachers, and community members to share information and negotiate meaning e.g., small-group meetings Learning tools Tools that support students in intellectually challenging tasks Data collection, communication, modeling Artifacts Representations of ideas and concepts that can be shared, critiqued, and revised to enhance learning e.g., concept maps, scientific models Scaffolds Methods provided by teachers, peers, and on-line resources

10. Research Questions How do students use multiple representations (e.g., graphs, scatterplots) during scientific reasoning? How do students use math, biology, and chemistry concepts to reason about watershed problems? What is the nature of students’ misconceptions about dynamic systems? What is the nature of students’ discourse during scientific reasoning? (e.g., observations, explanations, use of supporting evidence) How does RiverWeb support collaborative scientific reasoning and argumentation? How and when do students utilize scaffolding provided by the teacher, peers and/or digital resources?

11. Method Students 16 9 th grade students, 2 Honors biology classes Introduction to the interdependence of living organisms Procedure Students audio- and videotaped on 2 separate occasions over a 1 week period 1 environmental science teacher - complete participant Regular classroom teacher and visiting teacher 2 researchers acted as complete observers 10 hrs of video and audio (2 student-pairs x 2 x 75 min)

12. Method (2) In-depth examination of students’ emerging understanding of science phenomena Data sources 10 hrs of video and audio (8 student-pairs x 2 x 75 min) notebook entries, prediction statements, pretest and posttests Data Analyses Quantitative (pre- and posttests, quality of notebook answers) Nature of collaborative problem solving (e.g., reasoning chains) Nature of teachers’ scaffolding during science activities

13. Results Overall, students exhibited the following difficulties: inability to establish whether the differences observed are due to cause-and-effect or are based on a relationship between variables lack of understanding of definitions and concepts (e.g., runoff) difficulty reading and comparing multiple representations incomplete co-construction of knowledge Students engage in long reasoning chains as they jointly solve problems presented in the work sheets and notebook by accessing multiple representations and other WQS features. Teachers provide individualized levels of scaffolding. Students create incorrect analogies and/or use incorrect visual representations of complex concepts. Engaged students are metacognitively aware of their performance and will address deficiencies by deploying various strategies.

14. Summary “Flexible” application of educational research Theoretically-based and empirically-driven approach Evolution and scaling-up of “computers as cognitive tools” theme Self-regulation learning model Role of modeling and visualization tools for science Teachers’ professional development

15. Future Directions Investigate the role of self-regulated learning (SRL) during students’ complex science learning with RiverWeb examine effects of teacher-set goals vs. learner-generated sub-goals on students’ emerging understanding of scientific phenomena Understand the nature and role of classroom discourse during science inquiry activities Build additional RiverWeb features Content assistants Hypothesis-testing area Explore the use of AI techniques model SRL and explanation-based coach