What makes science a science?
The top two answers are “data”, and “reason”.
A scientist believes that with the
right amount of data and with the right approach to analyze the data science can
explain everything.
Scientists generally believe that a
scientific approach can be applied to any natural phenomenon, to any social practice.
Well, except one.
Science cannot be used to provide a scientific
description of how scientists teach science.
When I say “cannot” I do not mean “impossible”,
I mean “restricted”.
A scientific approach to analyzing
how scientists teach science would require collection, accumulation, and analysis
of a large amount of data; a scientific approach would require collecting as
much data as possible because in science, no amount of data is ever enough –
one or two new facts may force the paradigm change (it happened!).
But take any college or a university
and check what data does administration collect on the teaching practices of
the faculty? All you find is the set of standard questions in the end-of-a-course
student evaluations. And that’s that.
Those evaluations serve two goals:
(1) to display that the college or university cares about the quality of
teaching (“See, we listen to our students!”); (2) to ensure that a faculty does
not stimulate in students a large amount of hatred toward the college (“Not
great at teaching, but OK”).
That is why all college and
university administrations limit the set of the questions in evaluations to the
bare minimum. Why bother if the answers do not make any difference, anyway?
Data coming from end-of-a-course evaluations are vague and limited. But they are data! Even they could have been used for some analysis. But they aren't. No comparison of any sort; a year to a year, faculty to faculty, a department to a department, a college to a college. Hence, there is no system in professional development of faculty. Of course, there is an official, or a faculty, or even a structural entity which purpose, supposedly, is helping faculty becoming better teachers. It regularly issues advises, links, or invites to talks. But no one assesses the impact of those actions in any way, and no one has a specific data-based strategy for professional development, so, in the end, it is just a facade ("See, we work on professional development of our faculty").
If no one analyses even that limited data which comes from limited and vague evaluations, no one spends any energy on designing more informative assessing techniques.
If no one analyses even that limited data which comes from limited and vague evaluations, no one spends any energy on designing more informative assessing techniques.
Could it be done?
Of course!
Why it is not happening?
Because no one wants it – not administration,
not faculty, not students, not parents, not accreditation agencies, not the government,
not politicians.
There are many possible reasons for
WHY no one wants to conduct actual scientific study of how faculty teach (e.g. read
“A Continent Lie” or “What
Research University Faculty Tell Themselves About Their Teaching”).
Reason #2, of course, is that there are
only few faculty who are actually good at teaching and making this fact
official could shatter the system (reason #1 is that no one really cares about
the quality of teaching at a college or a university level if that is a
research college or a university; teaching practices at public or for-profit colleges
or universities are heavily guarded by local politicians or the owners).
But there are faculty who are good
at teaching (I am one of them).
Hence, there are faculty who are not
afraid of sharing their evaluations (here are mine).
Plus, there are faculty who could
share their evaluations anonymously (for the sake of science!).
If some college or a university would
start collecting and analyzing the data coming from those evaluations, it already
could lead to invaluable insights on the teaching practices at a college and a
university level. That could, in turn, lead to the improvement of the teaching
practices of more faculty; which would lead to more data, etc. A positive
feedback loop could eventually break the current restrictions self-imposed by
administrations on designing, collecting, analyzing, and sharing data on teaching
practices of faculty.
Who knows, maybe even teaching
practices of scientists teaching science could eventually become a science,
too?
The links to all six my applications to the NSF 2026 Big Idea Machine (from August 31, 2018 to October 26, 2018):
1. Entry125253: High Frequency Data Streams in Education
2. Entry124656: objective measures of physics knowledge
3. Entry125317: National database teacher PD
4. Entry124655: role of NSF in funding education
5. Entry125719: The new type of a science course for science teachers.
6. Entry126205: The development of the uniform standard for measuring content knowledge in physics.
1. Entry125253: High Frequency Data Streams in Education
2. Entry124656: objective measures of physics knowledge
3. Entry125317: National database teacher PD
4. Entry124655: role of NSF in funding education
5. Entry125719: The new type of a science course for science teachers.
6. Entry126205: The development of the uniform standard for measuring content knowledge in physics.
To
learn more about my professional experience:
No comments:
Post a Comment