Transcript No Slide Title

Modeling Across the Curriculum
Paul Horwitz, Principal Investigator
Co-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.org
Funded 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
Settings,
Operations
Graphics screen
QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture.
Monitors
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 Models
Type of Scaffold
Description of Pedagog ical Elements
Representational
Assistance
to guide students' understanding of the
representations or domain-specific
conven tions within model s.
Integration of pieces of
model
can take the form of r eflective qu estions, tasks,
explanations (either provid ed of studentgenerated) int ended to help students' in
integrating component s of the model to come
to a deeper understanding of the aspects of the
model (e.g., spatial, causal, dynamic,
temporal).
Model -based reasoning
supports
refers to tasks, etc., that support students in
reasoning with their model s and revising their
models.
Reconstruct, Reify, &
Reflect
refers to supporting students to refer back to
what they have learned, reinforce it, and then
reflect to mov e to a deeper level of
understanding.
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 models
Attitudes & Self-efficacy
Intrinsic Teacher Factors
prior knowledge
new information
model formation
model use
Interacting with
understanding
reasoning
generating
Learner's
Mental
Models
model evaluation
Epistemology of models
Teaching experience
Background
Hypermodels*
simulations
diagrams
explanations
instructions
data tables
graphs
+ Metacognitive
model reinforcement
model revision
model rejection
Selecting
Directing
Monitoring
Phenomena
experiences
experiments
Classroom Factors
Implementation of MAC activity use (logged)
Teacher practices (reported via Classroom
Communique)
Research: Level 3- Longitudinal
D.V.’sCumulative 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 Partnerships
Modeling Across the Curriculum Partcipating Schools
Name
Denali Borough SD
Bibb County HS
Bromf ei ld HS
Amarillo HS (ASU)
West Chester East
Sprayberry HS
Location
Healy, AK
Centreville, AL
Harvard, MA
Amarillo, TX
West Chester, PA
Marietta, GA
Falmouth HS
Falmouth, ME
Spearfish HS
N. Forsyth HS
Nathan Hale HS (ASU)
Fitchburg HS
Lincoln HS (ASU)
Preston HS
Lowell HS
Peekskill HS
Josiah Quincy US
Spearfish, SD
Winston-Salem, NC
Seattle, WA
Fitchburg, MA
Lincoln, NE
Bronx, NY
Lowell, MA
Peekskill, NY
Boston, MA
# Science
# Students Teachers Density
120
4 Rural
600
4 Rural
400
4 Suburban
2035
15 Suburban
1577
16 Suburban
2000
17 Suburban
600
758
1477
1100
1415
1940
528
3626
725
*53
18,901
10 Suburban/Rural
5
10
8
10
10
6
11
8
*2
Suburban/Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
% F/A
Lunch Participation
25
Member
60
Member
1
Lab
6
Member
6
Member
10
Member
5
Member
12
18
20
26
37
41
42
50
*78
24
Member
Member
Member
Partner
Member
Member
Partner
Member
Partner
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
Cognition
Observations (level 1 only)
Explicit assessment items-post
Implicit assessment items-logs
Effective
Assessment
Interpretation
–
–
–
–
Manipulable models
Time & tries to success
What steps they take
What info or help they seek
Graphic Adapted from Knowing What Students Know (2001)
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 Pedagogica
Pedagogica~ 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