Suggestions for using data to improve science education Rajul Pandya DLESE Program Center Unidata Program Center NVODS Meeting September 2003.

Suggestions for using data to improve science education

Rajul Pandya DLESE Program CenterUnidata Program Center

NVODS MeetingSeptember 2003

Overview

• Science Education should be revised• NVODS/OPeNDAP can contribute

technical infrastructure to that revision

• Technology is not enough• Ideas from the Educational Research

Community• A framework for integrating

technology and educational research: Compound Documents

Goals

• Highlight key issues regarding the educational use of data

• Provide a concrete illustration using cyberinfrastructure to enable meaningful educational use of data

• Introduce DLESE as educational cyberinfrastructure and collaborator

A need to improve Science Education

• Even after studying a phenomena in class, students had a fragile and incomplete understanding of the underlying physical processes

Recommendations for Reform

• Adopt an inquiry-based approach • Emphasize scientific principles and

their applicability to everyday life• Use scientific tools• Present an integrated, Earth-

Systems perspective

Recommendations from: Shaping the Future; Geoscience Education: A Recommended Strategy; Geosciences: Beyond 2000; Science for All Americans

More Succinctly

…faculty may come to interact with undergraduates in ways that resemble how they interact with their doctoral students today…

National Academy of ScienceNovember, 2002

Challenges in Implementing Reform

• Students have difficulty using scientific tools and data, especially in inquiry environments

• Professors encounter practical and technological hurdles when implementing reform recommendations

• As a result, most classes are still taught in traditional ways

More Challenges

• “A consideration of how people can use computers and the Internet to further the process of social inclusion is paramount in any effort to install new technology into an environment lacking it.”1

• Time scales of change for technology, institutions, organizations, and society differ - how to maintain synergy?

1 Mark Warschauer: Demystifying the Digital Divide, Scientific American August 2003

Learning Theory Revolution

• Behavioral View - Learning involves the transmission of fixed knowledge that can be measured precisely.

• Cognitive View - Learning is contextual, effortful, developmental and can only be estimated through triangulation of assessments.

Technology as a cognitive tool for learning.

• Cognitive tools enhance powers of humans during thinking, problem-solving, and learning.• Written language,

mathematical notation, and more recently, computer programs are examples of cognitive tools.

Using Data as a Cognitive Tools

• Data access needs to be linked to appropriate tools and guided by relevant educational context

• Digital Libraries can provide a vehicle for discovering and using data in educational settings.

From the DLESE Developers’ Workshop, 2003

Compound Documents

• TEXT: • A curriculum to model and guide inquiry

• TOOLS: • Scientific visualization tools, data access tools• Intellectual models to inform student data

exploration

• DATA:• Multiple data sets to enable student discovery

(cataloged in THREDDS)

An Example: The VGEE

• Web-based environment in which students use authentic data and tools to investigate a real scientific issue

• It include:• A learner-interface to a scientific

visualization tool• Concept models that support physical

insight• A curriculum to guide inquiry• A catalog of data, with services to use that

data

An Example: The VGEE

Students notice that the Western Pacific is considerably warmer than the East.

Identify RelateExplainIntegrate

The VGEE

Learners construct visualizations showing that upward motion, above average precip, and warm SST all occur together

Identify RelateExplainIntegrate

Concept models are used to explore relations in an idealized context.

The VGEE

Identify RelateExplainIntegrate

The VGEE

Concept models can be used to probe data. This helps students ‘see’ basic physics in real data and apply theoretical understandings to real geophysical phenomena.

Identify RelateExplainIntegrate

Classroom Testing

-0.5

0

0.5

1

1.5

2

1 1.5 2 3 4 5 6 7 8 8.5 9 10

Non VGEEVGEE

Question Number

Ave

rage

Sco

re

Tools in the VGEE

• VGEE content developers use THREDDS catalogs to serve data

• UNIDATA IDV reads these catalogs and then loads data from remote servers

• Advantages:• Convenience: Avoids time-consuming data

downloads• Scalability: Future access to growing catalogs of

research and real-time data• Interoperability: THREDDS can negotiate protocols

allowing IDV to visualize multiple data types

Advantages of Compound Documents

Discovery in Digital Libraries

• The VGEE curriculum is catalogued in the Digital Library for Earth System Education (DLESE)

• Is discoverable in the National Science Digital Library (NSDL)

• Advantages• Dissemination: DLESE has over 100000 hits a

month• Credibility: DLESE can serve as a “reputations

broker”• Collaborations: DLs include intellectual commons

Starting in DLESE

Discovery of VGEE

VGEE Curriculum

Launching the Visualization Tool

The IDV-based Visualization Tool

Using THREDDS to Access Distributed Data

The IDV can read THREDDS catalogs and locate and load the cataloged data set.

Concept Models/Probes

Advantages of Compound Documents

Connecting Data, Tools, and Curriculum

Learners &Educators

Ocean Data

GIS Data

Solid Earth

THREDDS

DLESEImages

Simulations

Curricula

Concept Models

IDV

Curricula

VGEE

VGEE Data

Concept Models

Researchers

Advantages of Compound Documents A network of expert contributions

• TEXT: • The inquiry curriculum implements pedagogical

knowledge, integrates assessment, connects to scientific content, uses technology to launch & configure tools

• TOOLS: • A customized interface to support discovery of specific

understandings incorporates scientific knowledge, learner-centered design, pedagogical theory and classroom practice

• Concept models depend on scientific understanding, technological skill, instructional design

• DATA:• Multiple data sets to enable student discovery. And also

support adding domain knowledge, relevant services, etc.

Advantages of Compound Documents

A Developers Toolkit

• An educational materials developer• can find data sets in THREDDS thematic

catalogs (in NSDL/DLESE)• find related Concept Models in

DLESE/NSDL • modify or build a new interfaces for the

IDV engine (including importing concept models)

• use VGEE as a scaffold to build curriculum

Advantages of Compound Documents Dynamic Curriculum

• A student or teacher can• Use curriculum with chosen, static data

sets (as now)• Access thematic catalogs to look for the

most recent related data sets

• Catalogs can (in the future) • Contain pointers to configuration info

for the IDV (including customizing the interface.

Summary

• Cyber-infrastructure provides tools for reform of science education• Data access and Interoperability (e.g. NVODS)• Digital Libraries

• Educational research provides guidance in using these tools• Context, recommendations, and barriers

• Compound Documents apply educational guidance to cyber-infrastructure components• An Example: The VGEE

A Cyberinfrastructure for Geoscience Education: DLESE

• Easy access to quality teaching and learning resources on a full range of Earth Systems topics for a wide range of learners

• Services to help users effectively create, use and evaluate digital learning resources

• Interfaces and tools to allow student exploration of Earth data

• A community center that fosters interaction, collaboration and sharing

What can DLESE offer NVODS?

• Connections to educational community

• Services to add context to data and tools

• Mechanisms to align to digital resources to community needs

• Authentic assessment opportunities

Acknowledgements

• The VGEE curriculum and concept models were developed by: Dan Bramer, Colleen Contrisciane, Ryan Deardorff, Dean Elliott, Ken Hay, Katia Issa, Mary Marlino, Rajul Pandya, Mohan Ramamurthy, Caryssa Seider, Marianne Weingroff, Robert Wilhelmson, and and John Yoder

• VGEE Data were prepared by Don Middleton and Tim Scheitlin of the VETS group in SCD

• The IDV was developed by the Unidata IDV developers: Jeff McWhirter, Don Murray, and Stuart Wier

• THREDDS tools are designed and maintained by Unidata THREDDS developers: John Caron, Ethan Davis, Ben Domenico, Robb Kambic, and Stefano Nativi

• All work supported by the National Science Foundation.

Links• VGEE Curriculum

www.dpc.org/vgee

• IDV Visualization Environment: www.unidata.ucar.edu/projects/metapps/webstart

• SCD’s Visualization and Enabling Technologies Group:http://www.vets.ucar.edu/

• THREDDS: www.unidata.ucar.edu/projects/THREDDS

• DLESE:www.dlese.org

• NSDL:www.nsdl.org