Novice and Expert Characteristics in
Teacher Professional Development
with Astronomy Databases
Andria C. Schwortz1,2
Dr. Andrea C. Burrows1
1University
of Wyoming
2Quinsigamond Community College
Andria’s Background
• PhD Physics student (University of Wyoming)
• Quinsigamond Community College Associate
Professor (Worcester, MA)
• College teaching: calculus-based Physics,
algebra-based Physics, Astronomy 101
• Teacher Professional Development: UMass NSF
GK-12, UWyo Black Hole Days, LASSI
• Science research: a large database of quasars
2
Question
“What expert and novice traits do inservice STEM teachers display when
learning to work with databases in
astronomy?”
3
Theoretical framework
Social constructivism
4
Expert Characteristics
1.
2.
3.
4.
5.
Recognize meaningful patterns
Organized knowledge
Contextualized knowledge
Knowledge retrieval
Pedagogical content knowledge (PCK) and
peer instruction
6. Adaptability and metacognition
Adapted from Bransford, Brown, & Cocking (2000)
5
Mixed Methods
• Quantitative
– Pre-/Post-Test (8 MC questions)
• Qualitative:
– Pre-/Post-Test (3 free response)
– Responses on lab activity
– Video recording of activity
– Field notes
– One-on-one interviews
6
Astronomy Dataset Activity
7
Sample
• Participants self-selected to attend two
workshops on astronomy during summer 2014
• K-12 in-service teachers
• 6 workshop A only, 5 workshop B only, 3 both
• 14 unique individuals: 8 men, 6 women
9
Data
• Scores on all individual pre/post-test MC
questions
• Free response pre/post-test questions
• Free response to activity questions
• Transcripts of one-on-one interviews with
Workshop A participants
• Transcripts of recordings made during
Workshop B activity
• Field notes during both groups’ activities
10
Analysis
• Quantitative
– Pre/Post-test scores and p-values
– Normalized matched gains and p-values
– Individual question improvement p-values
• Qualitative
– Coded for themes, focusing on novice/expert
characteristics
11
Findings
12
Pre-/Post-Test Scores
Percent Score
100.0
90.0
Men
80.0
Women
70.0
87.5
60.0
83.3
50.0
40.0
72.5
75.0
73.4
30.0
20.0
10.0
0.0
Pretest
13
85.9
All
Significant (p<0.05)
• All’s pre to post
• Men’s pre to post
Posttest
Did they improve?
Normalized Matched Gains
Effect Size (Cohen’s d)
0.45
1.20
0.40
0.35
1.00
0.38
0.30
0.32
0.25
0.80
0.79
0.60
0.20
0.21
0.15
0.48
0.40
0.10
0.20
0.05
0.00
0.00
Men
14
1.00
Women
All
Men
Women
All
Individual questions
• Three questions had significant improvement
(p<0.05) from pre- to post-test for both men
and women.
16
Two Definition Questions
• Histogram (data analysis)
• Quasar (astronomy content)
• Familiarity with terms
17
One Interpretation Question
• Radio emission in quasars comes from jets
• Interpreting data
• Main goal of the activity
18
Analysis
• Quantitative
– Pre/Post-test scores and p-values
– Normalized matched gains and p-values
– Individual question improvement p-values
• Qualitative
– Coded for themes, focusing on novice/expert
characteristics
21
Expert Characteristics
1.
2.
3.
4.
5.
Recognize meaningful patterns
Organized knowledge
Big Picture!
Contextualized knowledge
Knowledge retrieval
Pedagogical content knowledge (PCK) and
peer instruction
6. Adaptability and metacognition
Adapted from Bransford, Brown, & Cocking (2000)
22
Evidence of Big Picture Thinking
23
Pre/Post-Test
• “Start with a question to answer.” … “Evaluate
to see patterns or if your question is answered
or not.”
• “I would look for trends, and then graph the
data based on what I want to analyze.”
24
One-on-One Interviews
• “The field of view grows
as you look further
away, causing the
telescope to see more
quasars further away.”
25
Activity Recordings
• “About the only thing you could maybe find
here is those that do have radio magnitudes,
how do they compare in distance?”
26
Conclusions
• Men had statistically significant improvement
from pre-test to post-test (p<0.05). Women
did not.
• Nine out of 14 teachers exhibited awareness
that a “Big Picture” exists and is important.
• The three “repeater” participants showed
improvement in their second iteration.
27
Implications
• In-service K-12 teachers can benefit from
professional development combining astronomy
content and dataset skills while working in groups.
• Repeated professional development can benefit
teachers.
• Not all teachers recognize the importance of
understanding the big picture (e.g., having a goal) in
data analysis, but most do.
• Teachers can recognize the importance of seeing
the big picture even if they don’t know what that
big picture is.
28
Limitations
• Small number statistics!
• No inter-rater reliability
29
Future Work
• More work needs to be done to determine if
assigning groups (e.g., based on gender,
experience level, subject or age taught) could
benefit women teachers more.
• Papers
– This study (mixed methods teachers)
– Quantitative Astro 101 students, K-12 teachers,
sci-fi authors (N=77)
30
Questions?
Andria C Schwortz
aschwortz@gmail.com
http://physics.uwyo.edu/~aschwortz