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Dissertation Proposal Presentation

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Amanda Gonczi

on 22 February 2015

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Transcript of Dissertation Proposal Presentation

PROFESSIONAL DEVELOPMENT AND TEACHERS’ PEDAGOGICAL CONTENT KNOWLEDGE: FACTORS INFLUENCING SCIENCE EDUCATION COMPUTER SIMULATION USE

Computer Simulation PD: Program Elements That Make a Difference
Study 1: Situating Computer Simulation Use: Does It Make a Difference?
Science Education Goals (NRC, 2012)
Students:

understand key scientific concepts
are exposed to scientifically relevant technologies
engage in scientific practices fundamental to scientific inquiry
have accurate nature of science (NOS) conceptions

Computer Simulations
Dynamic visualizations to model complex scientific phenomena (Smetana & Bell, 2011).
Scientifically relevant technology (Medina & Mauk, 2000).
Help students understand certain scientific concepts and processes (Smetana & Bell, 2011).
Can be used to cultivate scientific practices and NOS understanding (Mäeots, Pedaste, & Sarapuu, 2008).
Situated Learning Theory (Lave & Wenger, 1991)

Explains transmission of knowledge, skills, and traditions unique to particular communities.

1. Modeling
2. Practice
3. Context (authentic, facilitative)

Two PD Programs
1. Technical computer simulation PD.
Cohort 1: 53 elementary, 11 secondary teachers.
Focused on technical aspects and gave participants time to browse web-based simulations and set up EL accounts.

2. Situated computer simulation PD.
Cohort 2: 98 elementary, 43 secondary teachers.
Modeled inquiry instruction,
Modeled instructional support
Provided content-relevant lesson planning time
Participants
Two cohorts of VISTA participants

Cohort 1: technical PD (52 elementary, 11 secondary)

Cohort 2: situated PD (98 elementary and 42 secondary)
Data Sources and Analysis
PD observations
Classroom observations
Classroom observation reports
Participant interviews
Perceptions surveys

Nonparametric statistical procedures and systematic data analysis
Research Questions
1. How and to what extent did participants in each computer simulation professional development program implement simulations into their science instruction?

2. What contextual factors influenced the extent of simulation implementation?

Results: Extent (Classroom Observation Reports)
Purpose in Observations
TPD N=12, SPD N= 29

Instructional Support (Mann Whitney
U
-test)
Extent: instructional context prevents transfer and practice

Purpose: Model not content specific, worksheets may promote inquiry

Instructional support: PD modeling insufficient. Reflection and greater content knowledge or experience may influence instructional support
Barriers
(Perceptions Surveys)
Insufficient computer access (38.5%)
Computer-based standardized tests

Time limitations (7.8%)

Software problems (7.3%)

Simulations not age/content appropriate (5.4%)
Implications
“They showed us a good example of how to do that [inquiry], but it was a great example for Physics… I teach Chemistry…. I would have liked to see something at least Chemistry-related” (Geri, Interview).
Situated Learning Theory (Lave & Wenger, 1991)
Explains transmission of knowledge, skills, and traditions unique to particular communities.

1. Modeling
2. Practice
3. Context (authentic, facilitative)
Research Questions
1. To what extent did participants adopt simulations and use them for inquiry instruction?

2. What variables fostered simulation adoption?

3. What variables hindered simulation adoption?

Pedagogical Content Knowledge
(Shulman, 1986)


Science content

Processes to help students learn science (content, NOS, inquiry)

Know about and integrate different instructional activities/representations uniquely

Students
Elementary Science Teachers Science Education Computer Simulation Use: How can PD promote Adoption?
Elementary Teachers:
Limited science content knowledge (Appleton, 2007)
Limited scientific inquiry experience
(Ireland, Watters, Brownlee, & Lupton, 2012)
Simulations may be especially valuable for this teacher population
Methods
Extent/Purpose of Use
PrePD: 11 (17%)
PostPd: 34 (52%)
t(66) = 4.897, p=.001


Sample: two cohorts of elementary teachers (N=67)
Data collected for two years: one year pre and one year post pd
Data sources: Quarterly lesson reports, Classroom observations (4 pre and 4 post), Participant interviews (11%), PD observations, perceptions surveys


1. Instructional use for student centered teaching.

2. Appropriate instructional support.


Virginia Initiative for Science Teaching and Achievement (VISTA)

Encourages:
Hands on science
Explicit NOS instruction
Inquiry
Problem-based learning
Instructional technology integration
ExploreLearning accounts
Instructional Science Computer Simulation Use: Does Professional Development Make a Difference?
Extends the PCK framework

Focuses on emerging technologies

Valuable lens to analyze specific challenges emerging digital technology integration poses to teachers
Methods
Within subject experimental design 65 elementary teachers pre/post PD

Data sources: PD observations, Classroom observations, classroom observation reports, perceptions surveys, interviews

Data analysis: Descriptive statistics, systematic data analysis (Miles & Huberman, 1984) and analytic induction (Erickson, 1986)
Implications
Only study to examine inservice elementary teachers TPACK within the context of computer simulation use.

Within subject experimental design will allow more reliable conclusions regarding effectiveness of the PD.

Inform teacher preparation and PD programs.



Preservice teachers:
methods classes & student teaching
Inservice teachers: professional development


Knowledgeable and Technologically Literate Teachers
1. What were participants’ instructional computer simulation use patterns before and following professional development?

2. What factors influenced instructional computer simulation implementation?

Technological Pedagogical Content Knowledge (Koehler & Mishra, 2009)
Extent of Use
Cohort 1: 28 of 64 (43.8%)
Cohort 2: 56 of 141 (39.7%)
Pearson's Chi Square (1, N-205) =.296,
p
= .59
Inquiry: 50% vs. 48.3%
Pearson's Chi Square (2, N=41) =.44,
p
=.80

Explicit NOS: 25% vs. 10.3%
Pearson's Chi Square (2, N=41) =2.19,
p
=.34

PBL: 8.3% vs. 3.4%
Pearson's Chi Square (2, N=41) =.66, p =.72

TPD SPD z p

Sreenface intro 6 (54.5%) 13 (48.1%) 2.92 .23
Initial play 0 (0%) 0 (0%) NA NA
Worksheets 7 (63.6%) 18 (66.7%) 1.54 .46
Goal 9 (81.8%) 18 (66.7%) 2.66 .27
Pacing 2 (18.2%) 12 (44.4%) 2.34 .31
Collaboration 8 (72.2%) 16 (59.3%) .63 .73
Probing questions 6 (54.5%) 13 (48.1%) 2.92 .23
General support 9 (81.8%) 23 (85.2% ) .63 .73
closure 2 (18.2%) 12 (44.4%) 4.10 .13

Dynamic visualizations to model complex scientific phenomena (Smetana & Bell, 2011).
Scientifically relevant technology (Medina & Mauk, 2000).
Help students understand certain scientific concepts and processes (Smetana & Bell, 2011).
Can be used to cultivate scientific practices and NOS understanding (Mäeots, Pedaste, & Sarapuu, 2008).
Innovation Adoption Theory (Rogers, 1985)
1. Awareness of innovation
2. Benefits identified
3. Decision to try innovation
4. Initial implementation
5. Reflection and decision to adopt/abandon innovation
Data analysis: quantize binomial codes (hesse-Biber
Systematic Data analysis
Inferential statistics
Variables that promoted adoption
5 Pre observation : 0% inquiry
15 post observations (10 (67%) inquiry
1. Increased confidence
Pre: M= 2.4, Post: M = 3.7
t(61) = 10.465, p.<.000
2. Content instructional support
3. Authentic and simple curricular option
"being able to use computer model simulations
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