AAPT_WM14program_final - page 43

January 4–7, 2014
2:50-3 p.m. Representing Processes of Energy
Transfer and Transformation**
Contributed – Amy D. Robertson, Seattle Pacific University, Seattle, WA
Rachel E. Scherr, Seattle Pacific University
Energy Tracking Representations
developed by Seattle Pacific
University Energy Project researchers, are designed to track energy as
it transfers and transforms in complex, real-world scenarios. Learn-
ers represent transfers and transformations by arrows that connect
symbols representing forms of energy (e.g., K --> T represents a
transformation of kinetic to thermal energy within an object, and K
--> K represents a transfer of kinetic energy between two objects).
Recent professional development efforts have supported teachers in
not only identifying different kinds of transfer and transformation
processes, but also in developing models for those processes. In this
talk, we offer examples of the models K-12 teachers negotiated for
specific transfers and transformations, and describe the effect of the
negotiation process on their understanding of energy.
1. R. E. Scherr, H. G. Close, S. B. McKagan, and S. Vokos, “Representing energy.
I. Representing a substance ontology for energy,” P
hys. Rev. - Spec. Topics: Phys.
Educ. Res
(2), 020114 1-11 (2012).
2. R. E. Scherr, H. G. Close, E. W. Close, and S. Vokos, “Representing energy. II.
Energy tracking representations,”
Phys. Rev. - Spec. Topics: Phys. Educ. Res
020115 1-11 (2012).
** This material is based upon work supported by the National Science Founda-
tion under Grant No. 0822342.
3-3:10 p.m. Describing Student Participation and
Performance in an Introductory Physics MOOC
Contributed – John M. Aiken, Georgia State University, Atlanta, GA
Shih-Yin Lin, Scott S. Douglas, Edwin F. Greco, Michael F. Schatz,
Georgia Institute of Technology
Brian D. Thoms, Georgia State University
Marcos D. Caballero, Michigan State University
We describe the results of an introductory physics Massively Open
Online Course (MOOC) offered through Coursera during summer
and fall 2013. This MOOC, modeled after an on-campus imple-
mentation of an introductory mechanic course, engages students in
activities involving interactive lectures, homework, exams, forum dis-
cussion, and laboratories. Student demographics, participation, and
performance on various assessment tools (e.g., the Force and Motion
Conceptual Evaluation) will be presented. Specific challenges in data
collection will also be discussed.
3:10-3:20 p.m. Peer Evaluations of Video Lab
Reports by Introductory Physics Students
Contributed – Shih-Yin Lin, Georgia Institute of Technology, Atlanta, GA
John M. Aiken, Scott Douglas, Michael F. Schatz, Georgia Institute of
Marcos D. Caballero, Michigan State University
Assessing student performance becomes challenging when course
enrollment becomes very large (~10^5 students). As part of an
introductory physics Massive Open Online Course (MOOC) offered
by Georgia Institute of Technology, students submit video reports on
force and motion labs. Peer evaluation of reports provides the primary
method for evaluating student laboratory work. This paper describes
the methods developed and used to guide students in evaluating each
others’ video lab reports when the course is offered in summer 2013
and fall 2013. Results of how students’ peer evaluation compares to
experts’ evaluation will be presented.
3:20-3:30 p.m. Implementing PER-based Materials
in the Introductory Algebra-based Lecture-supported
Contributed – Jarrad W.T. Pond,* University of Central Florida, Orlando,
FL 32816;
Jacquelyn J. Chini, Talat S. Rahman, University of Central Florida
We present the impact of incorporating physics education research-
based (PER) materials into our lecture-supported mini-studios for in-
troductory algebra-based physics. These courses are being redesigned
to provide improved integration of traditional lecture, recitation, and
laboratory components for a large number of introductory students
who cannot be served by our limited number of full-studio courses.
Previously, worksheet materials for the three-hour lab portion of the
mini-studio were mostly in-house designed. We have updated these
worksheets with exercises from the Maryland Open Source Tutorials
and the Minnesota Context-Rich Problem archive. Our previous re-
sults have shown lecture-supported mini-studios to perform similarly
to or better than studio-based courses on standard conceptual and
attitudinal assessments. We will investigate the sustainability of this
trend with our redesigned worksheets, and document our struggles to
identify existing PER-based materials for some topics.
*Sponsored by Jacquelyn Chini
3:30-3:40 p.m. Project-based and Team-based
Contributed – Carolann Koleci, Harvard University, Cambridge, MA
Eric Mazur, Kelly Miller, Laura Tucker, Harvard University
Have you ever journeyed to a learning environment in which students
take ownership of their learning, one in which students are encour-
aged to take risks, a learning community in which life skills-sets
are sharpened with real-world problem solving? Suppose in such a
learning environment, all within the course of one year, introductory
applied physics students: plan a manned or unmanned mission to
Mars; design and build electromagnetic safe locking mechanisms; ad-
dress the energy crisis; clean up the environment; design and build a
musical instrument; and, create an intricate Rube Goldberg Machine.
We invite you to Applied Physics 50*, a team-based and project-based
learning community whereby students own their learning.
*The AP50 Experience:
3:40-3:50 p.m. Spontaneous Formation of Learning
Communities and its Reflection on Learning
Contributed – Binod Nainabasti, Florida International University, Miami,
FL 33199;
David T. Brookes, Florida International University
This study seeks to understand the patterns of formation of spontane-
ous learning communities outside the classroom from the students of
a calculus-based introductory college physics class that is a studio-
format course implementing the Investigative Science Learning
Environment (ISLE). We build up a network pattern among students
from the self-reported data about who works with whom every week
during the whole semester. Our study also analyzes the relationship
between students’ network position or status as they work together in
groups outside the classroom, their interactions in the classroom, and
their performance on homework and exams.
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