MCasts
The most frequent recurring events in the MOSAIC community will be regularly scheduled 30minute seminars offered over the Internet: MCASTS.
One purpose of a MCAST is to enable an innovator to share an innovation and get feedback on it from interested members of the community.
The MCASTS are meant to be informal and easy:
just a chat among a small group. If
there's interest, an MCAST can be repeated on other occasions. Sometimes they may be the start
of a collaboration among interested members of the MOSAIC community, sometimes just a way to share an idea that others can try out in their own teaching.
The MCASTS are intended to require very low overhead so that a maven
can report on or propose an innovation without investing a lot of effort or expense. The idea is to test ideas and join forces to help an innovation develop, rather than waiting until it is ``finished'' or ``proven'' or ``peer reviewed.''
MCASTS are
designed to be easy to put together, easy to listen to, easy to repeat when requested,
and small enough to allow the participants to be active and to
investigate how they would adopt the innovation at their own
institution or in their own classes.
We encourage new people to join the MOSAIC community by participating in MCASTS and leading their own. To prime the pump,
we are planning MCASTS on a range of subjects:
 Relating to Modeling:
 The quadratic in two variables as a generalpurpose model.
 Teaching and exploiting dimensional analysis and units in
calculus.
 Exploring function parameters by fitting data.
 Analysis of circadian rhythm data using resampling based
inference in the introductory statistics class
 Analysis of multivariate audiology data to assess changes in
intracranial pressure
 Modeling the sleepwake cycle.
 Relating to Statistics:
 Languages for statistics.
 Resampling. Several topics draw on the ideas of resampling and
bootstrapping. Within the statistics education community, there is
strong interest developing in using this approach
, which is sometimes stymied by lack of appropriate computational preparation of students.
 The key role of resamplingbased inference as a foundation in the
introductory statistics course.
 Using simulation to develop the concept of a p value.
 Resampling and simulation in R.
 The key role of multipleregression as a foundation in the introductory
statistics course.
 The increasing sophistication of statistics in the medical literature
(and its implications for statistical education) .
 Becoming Warren Buffett: an inclass activity fostering understanding of
risk and reward.

Design of experiments in biology laboratories.
 Relating to Calculus
 Curve fitting as an example of optimization.
 Using partial derivatives for interpreting multivariate models.
 Accumulating local change to solve differential equations.
 Introducing constrained optimization to ``early'' students.
 What should calculus students learn about computation?
 Using R for Calculus.
 Least squares regression and vector projections.
 Propagation of error and significant digits.
 \item The Calculus Cycle: Discrete to Continuous to Discrete.
 The XMAC Lab: eXperiment, Modeling, Analysis, and
Computation.
 Relating to Computation
 Using Python to support the sciences. Experiences with a faculty
summer seminar on programming to introduce Python to a core of
science faculty so that they can use it in their courses and
research.
 Using NetLogo in a biology class.
 Physlets for physics instruction.

Places to make use of simple programming (scriping, etc.) in partner
disciplines.
 Assessment and the Modeling Concept Inventory
 How does one develop an assessment of ``knowledge''
and/or ``understanding?''
 What do the data on the Calculus Concept Inventory say about our
education system, and how are we to deal with this reality?
 The Survey of Attitudes toward Statistics.