Modeling Across the Curriculum

Paul Horwitz, Principal InvestigatorCo-PI’s: Janice Gobert, Research Director

Bob Tinker & Uri Wilensky, Northwestern

Other senior personnel: Barbara Buckley, The Concord Consortium

Chris Dede & John Willett, Harvard University

For more on The Concord Consortium visit www.concord.orgFunded by the the National Science Foundation and the U.S. Dept. of Education

under a grant awarded to the Concord Consortium (IERI #0115699). Any opinions, findings, and conclusions expressed are those of the presenters

and do not necessarily reflect the views of the funding agencies.

http://ccl.northwestern.edu

INE/IKIT themes addressed by Modeling Across the Curriculum (MAC)

Building on intuitive understandings--MAC’s representations leverage from students’ physical intuitions.

Focus on idea improvement--MAC focus on progressive model-building.

Comprehending difficult text as a task for collaborative problem-solving--Scaffolding difficult learning tasks (MAC).

Controlling time demands of on-line teaching and knowledge-building—Scaffolding knowledge integration (model-building) and transfer (MAC).

Project Summary

• Context: IERI program emphasizes scalability, “evidence-based” research, and emphasis on diverse populations- No Child Left Behind (NCLB).

• Four levels of studies- • Level 1- focus on improving the scaffolding design through

individual interviews of students and teachers. • Level 2, classroom-based studies to evaluate the impact of amount

of scaffolding. • Level 3 is a longitudinal study of a 3-year implementation of

materials in the Partner Schools. • Level 4- we address how this technology can be scaled to include

many more schools.

Project Summary (cont’d)

Doing this work in three areas of high school science: Genetics (BioLogica), Newtonian Mechanics (Dynamics), and Gas Laws (Connected Chemistry)

Our models in each of these domains are hypermodels- models that incorporate core science content that students learn through exploration and scaffolded inquiry. More about this later.

We apply Pedagogica a powerful engine that ~drives all three software tools, provides embedded guidance and assessment,controls all aspects of the learners’ interactions with the software tools by changing the nature of the scaffolding and the assessments.

Pedagogica can automatically report student progress through these lessons via the Internet, providing real-time, fine-grained data on student learning.

Screen shot from connected Chemistry~Pressure in a Rigid Box

QuickTime™ and aTIFF (LZW) decompressorare needed to see this picture.

Graphics screen

Monitors

Settings, Operations

Plots

Research: Level 1- Case Studies with students

Case studies of students with software tools to assess conceptual progression of concepts (progressive model-building), development of scaffolding framework (more later), and HCI issues. Tools:

BioLogica (formerly GenScope, teaches Genetics) Dynamica (teaches Newtonian /Mechanics)Connected Chemistry (teaches Gas Laws)

Student Data collection with surveys for case studies:• Science learning survey (mix of items from Schommer, and items constructed by us).• Students’ Epistemology of Models (Gobert & Discenna, 1997).

Level 1 (cont’d): Teachers

Teacher Data collected with surveys ~ science teaching style, epistemological understanding, science “comfort” level, pedagogy with modeling.

Surveys ~– Teachers’ epistemologies of models (adapted from Gobert &

Discenna, 1997)– Teachers’ science teaching survey (adapted from Fishman, 1999)

and teachers’ background questionnaire (The CC Modeling Team).

Research: Level 2-Classroom

Years 1-2

• 1) Classroom studies of scaffolding with software tools.• 2) Testing out reliability and validity of Science Learning Survey• 3) Attempt at developing a quantitative form of the epistemology of

models survey.

Decided to use instead:

• VASS-views of science survey (cognitive and scientific dimensions; Halloun and Hestenes, 1998); one form for each biology, physics, and chemistry.

• Students’ epistemologies of models (SUMS, Treagust et al, 2002, adapted from Grosslight et al, 1991).

Scaffolding Framework for Learning with ModelsType of Scaffold Description of Pedagogical Elements

RepresentationalAssistance

to guide students' understanding of therepresentations or domain-specificconventions within models.

Integration of pieces ofmodel

can take the form of reflective questions, tasks,explanations (either provided of student-generated) intended to help students' inintegrating components of the model to cometo a deeper understanding of the aspects of themodel (e.g., spatial, causal, dynamic,temporal).

Model-based reasoningsupports

refers to tasks, etc., that support students inreasoning with their models and revising theirmodels.

Reconstruct, Reify, &Reflect

refers to supporting students to refer back towhat they have learned, reinforce it, and thenreflect to move to a deeper level ofunderstanding.

Effects of epistemology

MBTL and cognitive affordances focus primarily on factors dealing with student’s cognitive processing but...

Also important are students’ epistemological understanding of the nature of models and the nature of science, both of which have been found to affect their success in building models of phenomena (Gobert & Discenna, 1997) and their knowledge integration (Songer & Linn, 1991).

Specifically, learners who have a sophisticated view of the nature of models generally outperform students who have less sophisticated views (Gobert & Discenna, 1997) and can use their epistemological understanding to drive deeper content understanding (Gobert, in preparation).

With the survey data and data from log files we hope to be able to detect differences in students’ use of MAC activities depending on their epistemologies of models, e.g., those with more sophisticated epistemologies may use different knowledge acquisition strategies and model-building strategies (log files can provide an index of this). Example haphazard versus systematic experimentation in BioLogica.

Model-Based Learning in situ

Intrinsic Learner Factors Epistemology of modelsAttitudes & Self-efficacy

Intrinsic Teacher Factors Epistemology of models

Teaching experienceBackground

Classroom FactorsImplementation of MAC activity use (logged)

Teacher practices (reported via Classroom Communique)

Hypermodels*simulationsdiagrams

explanationsinstructionsdata tables

graphs

model reinforcementmodel revision

model rejection

Learner'sMentalModels

model evaluation

prior knowledge new information

model formationInteracting with

understandingreasoninggenerating

Phenomenaexperiencesexperiments

model use

+ MetacognitiveSelectingDirecting Monitoring

Research: Level 3- Longitudinal

D.V.’s-Cumulative gains on students’ content knowledge,

modeling skills, epistemological knowledge, and attitudes towards science.

Research: Level 3- Longitudinal

• In September 2003, we began a longitudinal study of three-year implementations of project materials. The longitudinal study is designed to answer five research questions:

• Content learning. Do students who are exposed to greater numbers of activities in the three areas using our modeling tools achieve a deeper understanding of content?

• Epistemological understanding. Do students who are exposed to greater numbers of activities in the achieve a deeper understanding of models?

• Modeling skills and transfer. Do students who are exposed to a greater number of activities able to use their understanding of models to provide reasoned explanations of new phenomena?

• Attitudes. Do students who are exposed to a greater number of activities have increased motivation for science?

• Learning sequence effects. Are there differences in any of the measures depending on the sequence of courses?

• School effects. How do the varying levels of assistance to the schools influence learning outcomes?

Level 3- Longitudinal (cont’d)

Research with log files.

Pedagogica generates logs for every student interaction, including all assessments for all students over three years.

Data be used as input include: which activities were used, for what length of time, the pattern of use (consecutive or intermittent days), and pre and post-test dates.

We can also generate a profile for the class in terms of their understanding at pivotal points in the curriculum. These data will be used to derive teacher reports, important for formative and summative evaluations.

Research: Level 4- Scalability

What kinds of technology infrastructure and data logging capacities are necessary to provide high level, conceptually-based feedback to teachers about their students?

What kinds of additional support (professional development, on-line support, etc) is necessary for teachers to succeed?

How can we scale up from 3 partner schools to many schools across the U.S. where we deliver software and collect data from schools with modest support?

Sample: Levels of PartnershipsModeling Across the Curriculum Partcipating Schools

Name Location # Students# ScienceTeachers Density

% F/ALunch Participation

Denali Borough SD Healy, AK 120 4 Rural 25 MemberBibb County HS Centreville, AL 600 4 Rural 60 MemberBromfield HS Harvard, MA 400 4 Suburban 1 LabAmarillo HS (ASU) Amarillo, TX 2035 15 Suburban 6 MemberWest Chester East West Chester, PA 1577 16 Suburban 6 MemberSprayberry HS Marietta, GA 2000 17 Suburban 10 MemberFalmouth HS Falmouth, ME 600 10 Suburban/Rural 5 MemberSpearfish HS Spearfish, SD 758 5 Suburban/Rural 12 MemberN. Forsyth HS Winston-Salem, NC 1477 10 Urban 18 MemberNathan Hale HS (ASU) Seattle, WA 1100 8 Urban 20 MemberFitchburg HS Fitchburg, MA 1415 10 Urban 26 PartnerLincoln HS (ASU) Lincoln, NE 1940 10 Urban 37 MemberPreston HS Bronx, NY 528 6 Urban 41 MemberLowell HS Lowell, MA 3626 11 Urban 42 PartnerPeekskill HS Peekskill, NY 725 8 Urban 50 MemberJosiah Quincy US Boston, MA *53 *2 Urban *78 Partner

18,901 24

Fine-Grained Data

• Time-series data collected online as students work through activities– Log files stored locally (thin client on schools’ servers

send data to Concord– Data includes pre- and post-tests

• Capacity to treatments (levels of scaffolding) fully randomized within classrooms~part of original design.

School Details

• First 2 years of project (3 partner schools) ~1000 students, 22 teachers

• large urban, suburban, small urban, very mixed SES

• Growth curve:– Adding 10 schools this year– Any number of schools can join in subsequent

years; is this viable?

Technology Enhanced Formative Assessment for Teachers’ and Students’ Use

Observations (level 1 only)Explicit assessment items-post

Implicit assessment items-logs– Manipulable models – Time & tries to success– What steps they take– What info or help they seek

Graphic Adapted from Knowing What Students Know (2001)

Cognition

Interpretation

EffectiveAssessment

Other Research Issues- Hypermodels

In MAC we are also leveraging the affordances of technology to:

• further develop our hypermodel technology.

• characterize model-based learning with technology.

• provide individualized, technology-scaffolded learning that can be faded as student needs less scaffolding.

• assess conceptual understanding with technology and provide formative feedback to teachers.

In summary, how are we leveraging technology…

Technology allows for dynamic simulations ~ not possible with static representations

Broadly accessible using our client and PedagogicaPedagogica~ can make changes to all activities in all

schools, etc when it is initiated, ~ creates teacher reports for formative assessment~ sends back all data for researchers.With the scaffolding and the on-line support, should

be “infinitely” scalable