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Transcript of My Research
Is this the end of the story?
My research interests:
1. the process of EEE adoption
2. the process of EEE expertise development
Elementary Engineering Education (EEE) as an educational innovation
The Impact of Student Teaching Experiences
on Readiness for Technology Integration
What's already there in the literature?
Qualitative Data Analysis
The impact of student teaching experiences on pre-service teachers' readiness for technology integration
Quantitative Data Analysis
-- Inductive qualitative analysis
Integrating emerging technologies into teaching and learning
P-12 teachers’ knowledge and skills for technology integrated
Models and frameworks guiding and assessing P-12 technology integration practices
STEM Education (Engineering)
Elementary engineering PCK (pedagogical content knowledge) development
Elementary engineering education (EEE) adoption and expertise development
Interdisciplinary connections between engineering and other disciplines
Impacts of engineering learning
Quantitative Evaluation, Measurement, & Statical Analysis
(growth curve model and clustered observation model)
Sun, Y. (2012). The EMPIRe model as a thinking tool to prepare
teachers for technology integration. Journal of Educational Technology
Development and Exchange, 5(2), 95-110.
Sun, Y. & Strobel, J. (under review) Evaluation of Technology Integration:
Problems and New Directions. American Journal of Evaluation.
Sun, Y. & Strobel, J. (under review). A typological investigation of the use
of instructional tools in elementary engineering teaching. Teaching
and Teacher Education.
(interviews; qualitative and quantitative
Strobel, J. & Sun, Y. (under review). From knowing-about to knowing-to:
Development of engineering PCK by elementary teachers through
perceived learning and implementing difficulties. American Journal of
Yu, J. H., Luo, Y., Sun, Y. & Strobel, J. (2012). A Conceptual K-6 Teacher
Competency Model for Teaching Engineering. Procedia-Social and
Behavioral Sciences Journal, 56, 243-252.
(surveys; Delphi method)
Elementary Engineering Education (EEE) as an Educational Innovation
The impact of student teaching on readiness for technology integration
-- From words to numbers
Are people using the innovation ?
How well are people able to use the innovation ?
- Not all people start using an
innovation at the same time
- Not common for people to be an expert
of the innovation since day one
Innovation research literature
- Adoption of an innovation is a process
* Rogers's innovation diffusion model (Rogers, 2003)
* the Concerns-Based Adoption Model (CBAM) (Hall and Hord, 1987; 2005)
- The development of Expertise in using an
innovation is a process
* Dreyfus and Dreyfus’s skill acquisition model (Dreyfus, 2004; Dreyfus & Dreyfus, 1980)
Engineering is not a discipline traditionally taught at the elementary level.
Elementary teachers are not prepared for EEE.
Generally not interested in and intimidated by teaching engineering in
Unfamiliar with DET (design, engineering, and technology)
Lacking engineering knowledge and engineering teaching experience
Theoretical and Methodological Framework
73 elementary teachers from 13 elementary schools in a school district in Texas
Constructing evidence-based models capturing and depicting:
* the EEE adoption process
* the EEE expertise development process
EEE Adoption Model
EEE Expertise Development Model
EEE Adoption Model
EEE Expertise Development Model
Then what's next?
* Use the two models as a rubric
* Classify teachers into specific EEE adoption or EEE Expertise development
stages and dimensions (using interview data at a give time)
* Use "1" to denote progress made over a certain period of time and "0"
for no progress (dichotomous variables: 0=No, 1=Yes)
* Logistic regression
Advantages: Make the most out of collected data
* Time consuming;
*inter rater-reliability (Cohen's Kappa )
*Only two time points each time.
*Development a survey instrument measuring teachers' EEE adoption
and EEE expertise development based on the two models
* Validate this instrument (conducting EFA )
* Administer this instrument to elementary teachers
* track and assess progress in EEE adoption and EEE expertise
development (growth curve mode or clustered observation mode )
Advantages: efficient (timewise and costwise); multiple time points
Disadvantages: self-report; low response rate
Readiness for technology integration
* a multidimensional construct (technology skills, TPACK,
self-efficacy beliefs about technology uses)
* instruments for measuring readiness for technology
Student teaching experience
* in general: developing situated knowledge about teaching, bridging theory with practice, forming professional identity
* In the context of technology integration: self-efficacy theory based perspective, a sociocultural perspective (mentor-novice relationship), contextual factor based perspective
A dearth of research quantifying the impact of student teaching experience
Lack of research connecting student teaching experience with pre-service teachers' readiness for technology integration
Phase II: Individual F2F Interviews--Lived student teaching experiences
Planning Mixed Methods Procedures
2-phase explanatory sequential mixed methods research design
Pre-service teachers (n=300) in the College of Education at Purdue university doing their student teaching in Spring 2013
Survey of Preservice Teachers’Knowledge of Teaching and Technology (Schmidt et al., 2009)
Computer Technology Integration Survey (CTIS) (Wang et al, 2004)
-- 32 survey questions (5-point likert-like scale)
-- 10 demographic questions
Phase I: online surveys measuring readiness for technology integration
Online using Qualtrics
Repeated measures: three times-- at the beginning,
in the middle, and at the end of the student teaching
A sample of 15 pre-service teachers
Interview protocol based on survey data analysis results
About an hour individual, f2f interviews
Timing (sequential or concurrent)
Weighting (weight or priority given to quan or qual)
Mixing (mixing of quan and qual)
Multilevel modeling (MLM) or Hierarchical Linear Modeling (HLM)
Growth curve model
Why not repeated-measures ANOVA?
Interested in individual differences rather than group or repeated measures trends.
MLM allows for or is tolerant of missing data
2-level Growth Curve Model
Level-1 model (without predictors):
Level-2 model (with predictors):
WHat predictor(s) account for the variance of the intercepts?
What predictor(s) account for the variance of the the slopes?
Random effects (between-person)
Interview questions based quantitative data analysis results
Bringing a deeper understanding about the quantitative findings
Support the interpretation of quantitative findings
From words to numbers
Quantitizing (mixed methods research): numerical translation, transformation, or conversion of qualitative data
Constructing and validating instrument that make future quantitative research possible
-- Words along with numbers
From Words to Numbers
Words along with Numbers
-- Research is a matter of preference and positioning
Luo, Y., Sun, Y., & Strobel, J. (2013). The effect of collectivism-individualism
on learners’ cooperative learning of motor skills. Journal of International
Students, 3(1), 41-51.
(Pre- and Post- tests and questionnaires;
Independent and paired t-tests)
Validating instrument for evaluating teachers' engineering teaching self-efficacy
(surveys; Exploratory factor analysis)
Effects of engineering education on math and science learning
(pre- and post- tests; ANCOVA)
The effects of MEA (Model Eliciting Activity) curricula on math and science learning
(achievement tests; clustered observations model)
Sun, Y. (under review). Collective Decision Making and Design Solution Finding in
Elementary Engineering Design Projects: A Case Study. International Journal of
Technology and Design Education.
(observations and learning artifacts; multiple
regression, ANOVA, and ANCOVA)
(a question of adoption)
(a question of expertise development)
Sun, Y. & Strobel, J. (in press). Elementary engineering education
(EEE) adoption and expertise development models: An inductive
and deductive study. Journal of Pre-College Engineering Education
Models for models' sake?
Guiding and improving teaching practice?
Instrument development and validation
(Intercept and slope as outcomes model)
* The EEE adoption and expertise development research is funded by the National Science Foundation under grant # 0822261.
* Thank you for my advisor, Dr. Strobel, my colleagues at INSPIRE, and my classmates in LDT for their support and assistance to my research.