Modeling Across the Curriculum

1. 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

2. 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).

3. 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.

4. 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.

7. Screen shot from connected Chemistry~Pressure in a Rigid Box Settings, Operations Graphics screen Monitors Plots

8. Research: Level 1- Case Studies with students Case studies of students with software tools to assessconceptual 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).

9. 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).

10. 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).

11. Scaffolding Framework for Learning with Models

12. 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.

13. Model-Based Learning in situ Intrinsic Learner Factors Epistemology of models Attitudes & Self-efficacy Intrinsic Teacher Factors Epistemology of models Teaching experience Background Classroom Factors Implementation of MAC activity use (logged) Teacher practices (reported via Classroom Communique)

14. Research: Level 3- Longitudinal D.V.’s- Cumulative gains on students’ content knowledge, modeling skills, epistemological knowledge, and attitudes towards science.

15. 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 ofactivities able to use their understanding of models to provide reasoned explanations of new phenomena? • Attitudes. Do students who are exposed to a greater number ofactivities 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?

16. 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.

17. 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?

18. Sample: Levels of Partnerships

19. 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.

20. 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?

21. Cognition Effective Assessment Interpretation 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)

22. 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.

23. 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