Baseline
Evidence:
Reflective
self-evaluation
for video-taped baseline lesson on equilibrium provided with MCE
application
JUNE 2007 |
|
From reflective
self-reflection:
Excerpt 1:
Re:
Disposition toward inquiry and the reiterative process of improving
teaching. |
| "The
reason for my instructional choice of having a teacher-centric,
lecture and note-based class, at this point in time, is because that is the
method that I am most comfortable with.
I have not witnessed or been intensively mentored by another Chemistry
teacher (having done alternate route certification and never having
taken any education courses), and so I rely on a teaching method that
allows me to use my strengths." |
Excerpt 2:
Re: Having a sense of direction in
terms of what I can change in my teaching/ questions that I can try to
answer through research: |
"I believe that both teachers and
students tend to teach and learn in default, traditional patterns
unless challenged to teach and learn in another way. The default,
traditional patterns often do not give credence to the fact that there
are multiple learning styles and multiple teaching personalities.
But, I have a strong conviction that teaching should encourage each
student to individually engage with the information or content matter
of the subject in a way that is best for that student.
Unfortunately, there is very little evidence of that philosophy or
belief in my classroom right now, except for the fact that I acknowledge
that a student's inability to grasp a concept may be more indicative of
my lack of skill in teaching it rather than his lack of
potential. I'm here to learn how to align my teaching with my
philosophy! Help!!!!"
|
Excerpt 3:
Re: Gauging student understanding/ efficacy of
my teaching: |
| "In
this lesson,
I gauged student understanding by putting certain students on the spot
and asking them for answers to questions, in addition to asking the
rest of the class for answers. There is evidence that the
students did understand this unit because several would call out
answers enthusastically. Also, the types of questions certain
students
asked reflect their grasp of the concepts. I did not have the
time in
this unit to test or quiz my students' understanding with a written
assessment, which is typically my method for checking for
understanding, because I did not feel I had the time to do it.
I am certain that there are better ways of checking for understanding,
and I have a feeling that my students' understanding of the concepts
might still be fairly tenuous from what I witness in the lesson." |
Before entrance into
the MCE program, I was asked to tape a lesson and reflectively
self-evaluate it. This first excerpt from the self-evaluation
shows that before the program, I was still hesitant and
uncomfortable with changing my instruction and engaging in inquiry that
would reform my teaching. The second excerpt shows that
while I wanted the change, I did not know where or
how to begin--there is a clear lack of focus. The third
excerpt shows that assessment of student understanding (and the
efficacy of my own teaching) was still not very important to me; I did not understand its
value, nor have much experience with the variety (quantitative and
qualitative) research methods I could use to get a more comprehensive
perspective of what was happening in my classroom.
|
Later
Evidence:
|
My first piece of later evidence shows
how I became
more aware of what I wanted to research in my classroom by simultaneous
reflection and reading of science education literature. My second
piece of evidence summarizes my project and the outcomes of my
research, in which I set out to see the effect of what I had read in
the research and applied in my
classroom. The following is a brief summary of the relevant
research articles:
Brief
overview of
literature (full
citations at bottom of page):
Through my participation in the MCE program (particularly through my
coursework in Edu536 and Edu636), I became interested
in researching and improving students' grasp of the particulate
nature of matter as a way to improve conceptual understanding of a
range of topics. I describe how and when these articles informed
my research more fully in the "Why did I select this topic?" section
immediately following.
Williamson & Rowe (2002) was the
first research paper I really
read. It made me aware of the efficacy and impact of peer groups
on students' engagement and success. It made a profound impact on
me in showing that improving one's classroom practice often requires
detailed and deliberate changes that do not often happen naturally or
intuitively.
Nakhleh (1993) was
the paper that challenged my view of student understanding.
Nakhleh showed that students can perform quite differently on
conceptual and algorithmic (i.e. math-based, often reliant on
memorization of patterns and strategies) problems. This made me
aware of my need to more fully commit toward nourishing conceptual
understandings in my students (I felt I was already quite good at
fostering algorithmic understandings).
Nakhleh (1992) and Johnstone (1993) both made me aware of the
three levels of chemical understanding--the macroscopic, the
microscopic, and symbolic. Johnstone approached this triad from a
constructivist standpoint, arguing for the need to foster the
connections between these disparate aspects in improving conceptual
understanding. Nakleh indicated that reason why students had
difficulty comprehending many fundamental concepts was based in their
inability to correctly conceptualize the submicroscopic level of
chemistry (particularly the particulate nature of matter) and connect
it to macroscopically observed phenomena and the symbolic
representations in chemical problems.
Ercikan & Roth (2006) made me
aware of the need for both qualitative and quantitative methods in
answering a research question, and made me actually want to do the
research that I had been preparing for in Edu536. The authors
focused upon the necessity of asking good
questions (questions that were meaningful in informing and improving
teaching and learning) rather than asking questions that could be
evaluated easily (but have little importance beyond publication).
This article pushed me into seeing the ultimate usefulness of research
in informing my classroom practice and thus, into using whatever melange of methods
necessary to appropriately answer my questions.
|
EVIDENCE
#1:
Research
question in research proposal drafts
JANUARY 2008-APRIL 2008
|
Jan 12, 2008:
|
Why did I select this
topic?
The
initial impetus
for my research was my frustration with student performance on word
problems. I had the sense that students were not really "getting"
the
material in the deep, conceptual way that I desired.
The
literature I had started reading in Edu536 provided me with the
background to eventually narrow and focus my
research to be meaningful in my classroom. I first encountered
the
ideas of impacting classroom engagement and performance by reading Williamson & Rowe's
(2002) paper on the effect of peer groups. As you can see,
the impact of this literature is readily apparant in the research
question.
Unfortunately, I was asking a question that, for all intensive
purposes, was already answered by Williamson & Rowe.
|
Feb
18, 2008:
 |
Upon further reading Nakhleh (1993), I became interested
in the dichotomy between algorithmic and conceptual undrstanding. I was encouraged to do
exploratory research of the algorithmic-conceptual disconnect Nakhleh
had observed in college classes in my own highschool
classroom.
I asked the questions because I was somewhat interested, but the
questions I was asking seemed mildly pedantic to me--not a driving
force in improving my own classroom.
|
March
8, 2008:
 |
I
narrowed the question down into something I could more easily measure,
but this question was even more pedantic and far removed from my
classroom experience. Sometimes you
have to take a couple steps back to move forward....
|
April
24, 2008:
 |
Through my reading of
Nakhleh (1992) and
Johnstone (1993), I
realized I wanted to do something very specific in my own classroom to
improve conceptual understanding. In particular, I wanted to
attack the problem of student's inability to understand the particulate
kinetic nature of matter (PKNM). Eventually, in my classroom, I
not only integrated the use
of manipulatives, but also a broad range of tools targeting student's
understanding of PKNM, from animations and applets representing
the submicroscopic aspect of nature to questions asking students to
draw representations. Finally, I had arrived at
a question that was of practical value to me in my classroom, of
importance to a larger community, and focused enough to be meaningfully
researched.
|
|
EVIDENCE
#2:
Statistical analysis of students' answers
to
paired
algorithmic & conceptual MC
questions (from Nakhleh,
1993)
on their final (comparison
of
2008 vs. 2009)
JUNE
2009
|
TABLE 1. Comparison of student performance
on matched MC questions on 2008 (n=37) and 2009 (n=45) final
|
QUESTIONS
|
Gases
|
Equations
|
Limiting
Reactant
|
Empirical
Formula
|
Algorithmic
|
Conceptual
|
Algorithmic |
Conceptual |
Algorithmic |
Conceptual |
Algorithmic |
Conceptual |
YEAR
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
AVG SCORE
(0=incorrect; 1=correct)
*# x 100 =
% who got answer correct
|
0.46
|
0.89
|
0.39
|
0.18
|
0.65
|
0.56
|
0.62
|
0.60
|
0.49
|
0.38
|
0.38
|
0.29
|
0.43
|
0.470
|
0.62
|
0.73
|
T-TEST STATISTIC testing for significant
difference in performance between years on the same question (1-tail,
unpaired)
|
5.6 x 10-6
|
0.021
|
0.20
|
0.42
|
0.16
|
0.20
|
0.38
|
0.14
|
T-TEST STATISTIC
testing for significant difference in performance between algorithmic
and conceptual questions
|
2008:
0.243
2009: 1.66 x 10-15
(algorithmic understanding > conceptual understanding)
|
2008:
0.406
2009: 0.337
|
2008:
0.177
2009: 0.188
|
2008:
0.053
(approaching
significance, where conceptual understanding > algorithmic
understanding)
2009: 0.00472
(conceptual
understanding > algorithmic understanding
|
highlighted numbers
are significant at 95% (p<0.05)
TABLE 2. Percentage breakdown of students
in four groups based on performance on matched questions:
|
Categories
|
Gases |
Equations |
Limiting
Reagent
|
Empirical
formula
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
2008
|
2009
|
| A0C0 |
27.0
|
11.1
|
16.2
|
20.0
|
37.8
|
46.7
|
24.3
|
13.3
|
| A1C0 |
35.1
|
71.1
|
21.6
|
20.0
|
24.3
|
24.4
|
13.5
|
13.3
|
| A0C1 |
27.0
|
0
|
18.9
|
24.4
|
13.5
|
15.6
|
32.4
|
40.0
|
| A1C1 |
10.8
|
17.8
|
43.2
|
35.6
|
24.3
|
13.3
|
24.3
|
33.3
|
*Percentages
sometimes do
not add up to 100 because of rounding
Click here
for full Excel 2007 file coding students answers.
SUMMARY OF PROJECT AND OUTCOMES:
The
second piece of later evidence shows my analysis of student performance
on paired
algorithmic and conceptual questions on the 2008 final (n=37)
and 2009 final (n=45). After having intensively integrated
submicroscopic represenations and applets and manipulatives
representing the particulate nature of matter during the 2008-2009
school year, I wanted to
identify whether students did significantly better or worse on
questions gauging their algorithmic (calculation-heavy) understanding
and conceptual understanding of gases, chemical equations, limiting
reagents, and empirical formulas.
It is important to note that
statistical
analysis alone can not determine the reason or causality for
significant increases or
decreases--however, it does highlight significant associations.
This aids in identifying areas of improvement or deterioration in
performance and provides a
starting point for determining possible causes and thinking about what
I could improve the next time I
teach my class.
It is
evident
from the data that, contrary to what I
expected, my students showed significantly less conceptual
understanding of gases,
even as they improved their algorithmic ability to solve problems
related to gas laws. There was no significant difference in any
of the other question area. Since NO ONE got just the conceptual
question correct, my conjecture, in looking over the actual answers
chosen, is that I may have taught my lesson in a way that encouraged
students in learning or retaining a misconception--they often thought
that when
gases are cooled, the gas not only slows down, but condenses, even at
temperatures above the gas boiling point. I will have to make a
point of stressing the importance of observing whether the temperature
is below or above the boiling point, and emphasizing that a gas only
condenses when cooled at a temperature below its boiling point (at
the given pressure). I think I may have
actually improved my teaching of gases in general, but made the
unfortunate mistake of not accounting for a common misconception that
could arise once students started visualizing and qualitatively
associating particulate motion, physical state, and the effect of
temperature.
There was
significantly better performance on the conceptual empirical formula
question in 2009. Also, the average for both the algorithmic and
conceptual question increased, indicating that this was an overall area
of improvement. Because I do not have qualitative data to draw
from, it's a bit hard to conjecture as to why--it may have been a
change in my teaching, or it may have been the sample of students that
took the class that year. I hope to gain a more thorough
assessment in the future by making use of qualitative surveys and
evaluations in concert with quantitative analysis.
|
|
|
DISPOSITION
TOWARD CONTINUING INQUIRY
As a result of my research, I have an increased
desire to re-address the conceptual-algorithmic disconnect that
students have, particularly in learning about the nature of
gases. I have made a mental note to be more careful about how I
may unintentionally perpetuate misconceptions. I
have also grown in my awareness of the need for evaluation,
particularly because my
research results showed an unexpected outcome that I
would not have been aware of if I had not taken the time to assess the
effectiveness of my pedagogical changes.
As for the following year, I plan on using and assessing (i.e.
researching) the integration of web-based tools from quia.com (another
great idea given to me by Mark Hayden) into my teaching. I hope
to assess their effect on student engagement with mid-year evaluations
(or perhaps even cogenerative diaglogues) asking for qualitative
feedback. My hypothesis is that the use of this web-based support
software (which is easily integrated into pre-existing materials to
make them interactive) will improve class participation,
accountability, and student enjoyment of my courses.
Of equal importance, through my experience in Edu636 (in particular, my
reading of an article by Ercikan & Roth (2006)), I have gotten more
comfortable with adjusting my methods and assessment to meaningfully
probe and address the question I am asking. I have become quite
comfortable with quantitative methods, but I also feel that qualitative
methods (e.g. interviews, surveys, evaluations) are equally legitimate
and have the ability to give more nuanced information that may be more
helpful in directing further efforts and iterations of a particular
lesson.
|
RELEVANT
ARTICLES:
Ercikan,
K., & Roth, W-M. (2006). What good is polarizing research
into qualitative and quantitative? Educational Researcher, 35(5),
14-23.
Johnstone, A.H.
(1993). The development of chemistry teaching: A
changing response to changing demand. Journal
of Chemical Education, 70(9), 701-705.
Nakhleh, M.B. (1993). Are
our students conceptual thinkers or algorithmic problem solvers? Journal of Chemical Education, 70(1),
52-55
Nakhleh, M.B. (1992).
Why some students don't learn chemistry:
Chemical misconceptions. Journal
of
Chemical Education, 69(3), 191-196.
Williamson, V. M. & Rowe, M. W. (2002). Group Problem-Solving
versus Lecture in College-Level Quantitative Analysis: The Good, the
Bad, and the Ugly. Journal of Chemical Education, 79(9), 1131-1134.
Updated
August 4, 2009.
|
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