Fostering Learners’ Collaborative Problem Solving with RiverWeb

Roger AzevedoUniversity of Maryland

Mary Ellen VeronaMaryland Virtual High School

Jennifer G. CromleyUniversity of Maryland

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

Overview

Introduction Context - MVHS - NCSA - UMCP RiverWeb Framework and Curriculum Design Principles Research Questions

Present Study Method Results

Summary Future Directions

RiverWeb - Water Quality Web-based Simulation

RiverWeb

RiverWeb -Notebook

RiverWeb - Scatterplots & Help

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

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

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?

Method

Students 16 9th 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)

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

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.

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

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