STEM Reasoning and STEM Learning
Scientists and engineers work in teams, using complex semi-formal processes to make breakthrough discoveries and develop innovative products. While we know a lot about how people become fixated on past solutions and how the discovery and design processes can each be better structured, much remains to be understood the process of insight and innovation, particularly regarding the cognition and social interaction that underlies it. We bring the theories and methods of cognitive and social psychology to unpack the complex events of discovery and design that occur in the real teams working on real problems.
I have studied many aspects of STEM teaching and learning. Most unique to my lab, we apply deep integrations of science, technology, engineering, and mathematics (STEM) to reform K-12 instruction. We explore different strategies for integrating different parts of STEM in ways that better match real science and engineering practice to produce stronger learning outcomes, but in ways that today’s teachers can implement. Most commonly, we have explored integrating: 1) engineering to teach science, 2) mathematics to teach science, and 3) robotics to teach mathematics and computer science.
- Sam AbramovichSUNY Buffalo
- Jacquey BarberUC Berkely
- Debra BersteinTERC
- Laura BetancurUniv. of Pittsburgh
- Joel ChanCMU
- Jon CaganCMU
- Brian DraytonTERC
- Paul EganUTH Zürich
- Joe GrabowskiUniv. of Pittsburgh
- Miray Tekkumru KisaFlorida State Univ.
- Susannah PaletzCASL
- Kathy MaloneOSU
- Susan McKenneyUniv. of Twente
- Mhusin MenekseArizona State Univ.
- Tim Nokes-MalachUniv. of Pittsburgh
- Ben RottmanUniv. of Pittsburgh
- Anita SchuchardtUniv. of Minnesota
- Chandralekha SinghUniv. of Pittsburgh
- Robin ShoopCMU
- Mary Kay SteinUniv. of Pittsburgh
- Elizabeth Votruba-DrzalUniv. of Pittsburgh
Engineering is now taking on the design of objects building upon emerging nanoscience. This kind of design poses new challenges for designers, as they must manipulate objects at the nanometer-level with outcomes at the centimeter or larger level (e.g., changing myosin properties to influence heart muscle properties). We build simulations, study the cognitive processes of designers, and develop new design environments that combine the simulations with artificial intelligence.
Understanding cross-level linkages improves designer performance.
Effective agent-based simulations can be evolved from basic strategies taken from studying human performance.
Why do some educational curricula successfully impact teachers and learners across many locations while others do not? We are studying critical designer processes related to dimensions of time, goals, context, and scale. The work involves retrospective studies of four successful cases, participant/observation of ongoing large scale curriculum design cases, and interview studies across larger numbers of science curricula design projects.
Systematic documentation of impacts on teachers or scaling outcomes is quite rare, even in grant funded curriculum design projects
Designers are critical pathway for having an impact from educational research but currently educational research does not address many of the critical questions that designers have.
Together with the CMU Robotics Institute, we create and study robotics curricula designed to strengthen mathematics (especially algebra) and computational thinking (especially programming) in middle and high school students, while also attending to issues of motivation and career interest, particularly for girls and underrepresented minorities.
Students are better able to learn key algebra concepts and skills when thinking about mechanisms underlying mathematical patterns.
Virtual robots can greatly speed learning of programming skills relative to physical robots.