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TEACHER SELF-EFFICACY FOR STEM TALENT DEVELOPMENT
Kelly Margot
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2017
APPROVED: Todd Kettler, Major Professor Anne Rinn, Committee Member Robin Henson, Committee Member Smita Mehta, Committee Member Abbas Tashakkori, Chair of the Department of
Educational Psychology Randy Bomer, Dean of the College of Education Victor Prybutok, Dean of the Toulouse
Graduate School
Margot, Kelly. Teacher Self-Efficacy for STEM Talent Development. Doctor of
Philosophy (Educational Psychology), August 2017, 77 pp., 10 tables, 1 figure, references, 95
titles.
In order to implement more science, technology, engineering, and mathematics (STEM)
into K12 classrooms, it is important to find out whether teachers are comfortable with this
pedagogy. To determine teachers’ current self-efficacy of STEM pedagogy, teachers in a
southern state in the United States were asked to enlighten researchers into this phenomenon.
Participants were K12 teachers (n = 119) from a public school district undertaking a district-wide
STEM initiative. A measure of STEM teacher self-efficacy and a demographic questionnaire
were administered online to participants. STEM teacher self-efficacy data were analyzed, along
with demographic data, using descriptive discriminant analysis (DDA) and canonical correlation
analysis (CCA). Results suggest some demographic variables are more predictive of STEM self-
efficacy (gender, grade level taught, feelings of administrative support, and professional
development sessions attended) than others (whether or not gifted courses are taught, age, and
length of teaching experience. This data should be used by school administrators that seek to
begin or improve STEM pedagogy in their schools.
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Copyright 2017
By
Kelly C. Margot
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ACKNOWLEDGEMENTS
Thank you to the Department of Educational Psychology and all of the wonderful
professors who help students realize their dreams. Many thanks to my amazing committee for
your guidance and wisdom. Dr. Todd Kettler, I am grateful for your mentorship. You push me
towards excellence in all scholarly work and I would not have gotten here without your help. Dr.
Anne Rinn, you are an inspiring role model. I sincerely hope I can someday pay forward all the
love and support you have shown me throughout this process. Dr. Robin Henson, you are a
gifted teacher. Thank you for helping me believe in myself. I am grateful for your support and
constant requirement that we conceptually understand statistics. This conceptual understanding is
the foundation of education research, and I am thankful to have it. Dr. Smita Mehta, I appreciate
the knowledge and experience you brought to my dissertation. Your feedback has been
invaluable and strengthened my project.
I would also like to thank Kaylee Seddio, my fellow doctoral student. I am sincerely
thankful for your unswerving friendship during this process. You are the best kind of friend.
Most of all I want to thank my husband (Matt Margot) and kids (Ethan, Gus, and Lucy).
You guys have been so supportive! I love you all more than I can ever express.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ............................................................................................................ ii LIST OF TABLES AND FIGURES............................................................................................... v TEACHER PERCEPTIONS OF STEM TALENT DEVELOPMENT: A SYSTEMATIC LITERATURE REVIEW ............................................................................................................... 1
STEM Education and Talent Development ........................................................................ 2
Purpose and Research Questions ........................................................................................ 6
Eligibility Criteria ................................................................................................... 7
Data Sources ........................................................................................................... 7
Search ...................................................................................................................... 8
Study Selection ....................................................................................................... 8
Data Collection Process ........................................................................................ 10
Data Analysis ........................................................................................................ 10
Results ............................................................................................................................... 11
Teacher Variables ................................................................................................. 11
Application Activities ........................................................................................... 13
Cross-Curricular Integration ................................................................................. 13
Student Enjoyment ................................................................................................ 14
Student Struggles .................................................................................................. 14
Value of STEM ..................................................................................................... 15
Pedagogical Challenges ........................................................................................ 17
Curriculum Challenges ......................................................................................... 18
Structural Challenges ............................................................................................ 18
Student Concerns .................................................................................................. 19
Assessment Concerns............................................................................................ 20
Teacher Supports .................................................................................................. 20
Collaboration......................................................................................................... 21
Curriculum ............................................................................................................ 22
District Support ..................................................................................................... 22
Prior Experiences .................................................................................................. 23
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Professional Development .................................................................................... 24
Discussion ......................................................................................................................... 24
Recommendations for Practice ............................................................................. 24
Recommendations for Future Research ................................................................ 26
References ......................................................................................................................... 27 TEACHER SELF-EFFICACY FOR STEM TALENT DEVELOPMENT.................................. 31
STEM Pedagogy in the K12 Classroom ........................................................................... 32
Role of Teacher Self-Efficacy in Talent Development ..................................................... 34
Social Cognitive Theory ....................................................................................... 34
Talent Development Model .................................................................................. 35
Teacher Self-Efficacy ........................................................................................... 36
STEM and Gifted Education ................................................................................. 41
Research Question and Hypotheses ...................................................................... 41
Method .............................................................................................................................. 42
Participants ............................................................................................................ 42
Measures ............................................................................................................... 43
Analysis................................................................................................................. 45
Results ............................................................................................................................... 46
Hypothesis 1.......................................................................................................... 49
Hypothesis 2.......................................................................................................... 51
Hypothesis 3.......................................................................................................... 53
Hypothesis 4.......................................................................................................... 54
Hypothesis 5.......................................................................................................... 54
Hypothesis 6.......................................................................................................... 54
Hypothesis 7.......................................................................................................... 55
Hypothesis 8.......................................................................................................... 56
Discussion ......................................................................................................................... 56
References ......................................................................................................................... 60 APPENDIX DEMOGRAPHIC SURVEY ................................................................................... 67 COMPREHENSIVE REFERENCE LIST .................................................................................... 70
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LIST OF TABLES AND FIGURES
Page
Tables
Table 1. Results of Initial Search .................................................................................................... 7
Table 2. Quality Assessment Rubric ............................................................................................... 9
Table 3. Descriptive Statistics of TESS-M ................................................................................... 46
Table 4. Factor Correlation Matrix ............................................................................................... 47
Table 5. Factor Pattern (Structure) Matrix Rotated to the Promax Criterion ............................... 47
Table 6. Descriptive Statistics from DDA Analysis ..................................................................... 50
Table 7. Wilks’ Lambda Table from DDA Results ...................................................................... 50
Table 8. Standardized Discriminant Function and Structure Coefficients for Interpreted DDA Results ........................................................................................................................................... 50
Table 9. Group Centroids for Interpreted DDA Results ............................................................... 51
Table 10. Canonical Solution for Administrative Support and Professional Development Predicting Teacher STEM Self-Efficacy ...................................................................................... 55
Figures
Figure 1. Diagram of the screening process. The diagram shows how the studies were culled down from the initial database search to the final retained studies. ............................................... 8
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TEACHER PERCEPTIONS OF STEM TALENT DEVELOPMENT: A SYSTEMATIC
LITERATURE REVIEW
In order to address the need for more science, technology, engineering, and mathematics
(STEM) literate workers, both elementary and secondary classrooms are integrating STEM
curriculum and pedagogy into their school day. For every person that is unemployed with a
STEM degree, there are 2.4 million unfilled jobs (National Science Board, 2012). It is important
to our economy that schools be successful at producing students capable of talented contributions
in STEM fields. In order to capitalize fully on the STEM potential of our students, schools must
streamline STEM education and refine their instructional pedagogy. Gomez and Albrecht (2013)
advocate for grounding this education and instruction in STEM pedagogy through an
interdisciplinary approach. This approach allows students to make real world connections and
prepare for STEM pathways and careers. Reform initiatives have begun with the goal of better
integrating engineering and technology into traditional math and science classrooms (National
Science Board, 2007). Teaching through the engineering design process is one approach to
integrating the subjects using a project-based approach that requires students to apply content
knowledge to solve problems. This is the basis for STEM pedagogy. The students learn by doing
and are encouraged to develop new understandings while refining their ideas (Mooney &
Laubach, 2002). Providing in-depth problem solving through STEM education with authentic
experiences requires that teachers are skilled with this unique student-directed pedagogy.
Educators have to understand the value and power of the engineering design process to enable
students to fail and persevere. These teachers have to know not just their subject matter, but the
content of the other disciplines. Also, they must feel capable of creating an educational
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environment that allows students to solve ill-defined problems while deepening their content
knowledge.
STEM Education and Talent Development
Gagné’s (2011) differentiated model of giftedness and talent explains how a person’s
natural abilities, or gifts, can be developed through learning and practice into talents. Part of this
model is the presence of catalysts that can either inhibit or facilitate the talent development
process. These catalysts can be intrapersonal, like perfectionism or confidence, environmental,
like programs or persons, or chance, things like genetic make-up and family. Teachers are an
example of persons that play the role of catalyst in the talent development process (Gagné,
2007). In this role, they can either help or hinder a student’s development of STEM talent.
STEM programs are an example of an environmental catalyst. The availability of a quality
STEM program in a student’s education would facilitate their talent development in science,
technology, engineering, and mathematics (MacFarlane, 2016). The teacher plays an important
role in this environment and therefore the person and environment work together to develop
STEM talent in this model. Within Gagné’s (2011) model, catalysts are not part of the initial gift
or the end talent, they are just part of the developmental pathway between the two. During the
learning and practice required to develop STEM talent, teachers and STEM programs provide the
opportunities, support, and experiences students need to reach their potential (MacFarlane,
2016).
STEM education is not a well-defined experience, but it does involve similar hallmarks
within the design and implementation (Honey, Pearson, & Schweingruber, 2014). Moore et al.
(2014) conducted an extensive review of published literature, analyzed documents of state
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content standards, and consulted with experts in STEM fields in order to determine the ways
teachers utilize STEM education in their classrooms. After this exhaustive search, these
researchers designated a framework that includes six major tenets for quality K12 STEM
education: 1) the inclusion of math and science content, 2) student-centered pedagogy, 3) lessons
are situated in engaging and motivating context, 4) inclusion of engineering design or redesign
challenge, 5) students learn from making mistakes, and 6) teamwork is emphasized. STEM in
education is both a curriculum and pedagogy. The curriculum includes cross-curricular real-
world challenges for students to solve. Judith Ramely, who was the director of the National
Science Foundation's education and human resources division, decided on the acronym STEM.
She explained that, math and science are used as the bookends for engineering and technology
(Christenson, 2011). According to Honey et al. (2014), the integration of knowledge must be
explicit both within the disciplines and across the disciplines. Students must have intentional
instruction into the connectedness of science, technology, engineering, and mathematics. STEM
education also includes the use of the engineering design process. There are various forms of this
process, but they all include a cyclical process of student’s evaluating their solutions and then
working to improve upon them. This revision step is an important part of STEM because it
requires perseverance and the acknowledgment that solutions can always be improved upon.
There is more than one answer to STEM challenges. STEM pedagogy explains the teacher’s role
within STEM instruction. The teacher guides students to examine problems from all angles by
questioning. This pedagogy involves the philosophy that students are capable of guiding their
own learning. Teachers are just there to facilitate this student-led process. Students use hands-on,
practical applications of content in order to solve their challenges. Students are also introduced to
STEM professions, which some researchers believe may increase the number of
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underrepresented populations in STEM jobs (Bagiati & Evangelou, 2015). The student goals of
STEM education according to Honey et al. (2014) include: STEM literacy, 21st century
competencies, STEM workforce readiness, ability to make connections among STEM
disciplines, and interest and engagement. While addressing the standards in each subject area,
students engage in the engineering design process to make connections to the real-world. An
example is studying simple machines to discover how a car works.
STEM education includes student use of math and science concepts they have learned in
an applied setting through the use of engineering design and technology. Instead of being taught
in a vacuum, math and science are brought to life through their need to be used in order to solve
a real problem (Chamberlin & Pereira, 2017).
An example of STEM education in action was a student raising the question about what
could be done to prevent a neighborhood raccoon from getting into his family’s trashcan. The
class analyzed the situation and the trashcan in question then proposed creating a clip for the lid
using a 3D printer. The students carefully measured and designed the clip. After printing, they
discovered the clip would not stay attached to the can. At this point, they evaluated what
modifications needed to be made in the design. Using 3D cad software, the students made the
changes and reprinted the trashcan clip. This time the clip worked to keep the lid attached to the
trashcan and prevented further raccoon scavenging in his family’s trash.
STEM and gifted. Some of the same tenets of gifted education are seen in STEM
pedagogy. Hockett (2009) examined all major curriculum recommendations for gifted learners
and found five principles of agreement: using a conceptual approach within a discipline, pursues
advanced levels of understanding, asks students to use processes and materials that approximate
those of a practicing professional in the domain, emphasizes problems, products, and
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performances that are true-to-life with transformational outcomes, and the curriculum needs to
be flexible enough to allow self-directed learning fueled by student interest. STEM pedagogy
contains all five principles by allowing students to work as professionals within the disciplines of
science, technology, engineering, and math would, while solving real-world problems in which
they are interested. STEM pedagogy also leads students to a deeper understanding of content
while solving ill-defined problems (Mann & Mann, 2017).
According to VanTassel-Baska and Little (2011) this type of pedagogy is not only good
practice with gifted students, but is also best practice with all students. All students benefit from
instruction rich in context and student-led investigations.
Though efforts are being made to implement STEM programming from the government
all the way down to local school district initiatives, teachers are the single most important factor
in the equation (McMullin & Reeve, 2014). Curriculum is simply a blueprint and STEM
education requires a pedagogical shift to student-centered learning. In addition, much of the
instruction is inquiry-based and experimental. It is paramount that administrators and policy-
makers discover the challenges and barriers teachers believe impede this effort to develop STEM
talent in classrooms. It is also salient to discover what supports teachers feel would bolster their
work as STEM practitioners. The role of the teacher is different with STEM, but just as
important. These teachers have to provide project-based lessons that encourage critical thinking
and innovation while building student understanding of content and concepts (Nadelson &
Seifert, 2013). Teachers must use questioning strategies to challenge students to think using
higher cognitive processes so they will think deeply about concepts and ideas in order to solve
STEM challenges (Bruce-Davis et al., 2014). This type of questioning is an essential skill for
STEM teachers’ instruction. How do teachers feel about using this type of questioning with
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students? Although primary and secondary teachers play an important role in the STEM talent
development of these students, few studies exist determining the prior held beliefs and
perceptions of these practitioners toward STEM curriculum and pedagogy. Policy makers,
administrators, and school administrators need to understand what challenges and barriers
teachers feel exist that prevent them from developing STEM talent in our schools. Johnson
(2006) reported that many teachers lack resources needed to effectively implement inquiry-based
learning experiences for their students. Understanding these challenges can help facilitate the
implementation and success of STEM programs. We also need to determine what supports
teachers feel would improve their ability to prepare students seeking STEM degrees and
pursuing STEM careers.
This review, by examining and critically analyzing multiple existing studies, will provide
a complete summary of what is known about teacher beliefs and perceptions regarding STEM
curriculum and pedagogy.
Purpose and Research Questions
The purpose of the study is to examine existing literature on teachers’ perceptions of
developing STEM talent. This study attempts to understand what is known about teacher beliefs
related to STEM talent development. To examine what exists in the literature the following
questions were used.
1) How does existing research characterize teachers’ perceptions of utilizing STEM pedagogy?
2) What do teachers identify as challenges and barriers to using STEM pedagogy in their classrooms?
3) What supports do teachers feel would improve their efforts to implement STEM pedagogy in their classrooms?
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Methods
This systematic literature review utilized the PRISMA guidelines and flow chart.
PRISMA guidelines include a 27-item checklist and a four phase flow diagram outlining the
items essential for transparency in conducting literature reviews (Liberati et al., 2009).
Eligibility Criteria
In order to be included in this review, studies needed to be peer-reviewed and published
in a scholarly journal (trade journals, magazines, and newspapers were excluded) between 2000
and 2016. Eligible studies also needed to be published in English with preK-12 teacher
participants and empirical in nature (editorials and monographs were excluded). The study also
needed to address at least one of the review’s research questions.
Data Sources
Databases searched were electronic and concerned the areas of education and social
science. The exact databases searched were Academic Search Complete, ERIC via Ebscohost,
and PsychINFO via Ebscohost. The last search was run on January 5, 2017.
Table 1
Results of Initial Search
Search Terms Database Search Limiters Hits
“teacher” AND “perception OR beliefs OR attitudes” AND Academic Search Complete Scholarly (Peer Reviewed) Journals
Published: 2000-2016 402
“STEM OR engineering” ERIC via Ebscohost Scholarly (Peer Reviewed) Journals Published: 2000-2016 310
PsycINFO via Ebscohost Scholarly (Peer Reviewed) Journals Published: 2000-2016 208
Total with duplications removed 418
Note. Searches were performed against article abstracts
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Search
The following search terms were used to search each database: “teacher” AND
“perception OR beliefs OR attitudes” AND “STEM OR engineering”. All searches were made
against article abstracts. Search limiters were used to align with the screening criteria. Results of
the initial search can be found on Table 1.
Study Selection
Screening
A diagram of the screening process can be seen in Figure 1. In order to select studies for
inclusion the following criteria were used sequentially:
Criteria 1: Study published between 2000-2016 in English
Criteria 2: Study published in scholarly journal
Criteria 3: Study participants included preK-12 teachers
Criteria 4: Study is empirical (qualitative, quantitative, mixed methods, or meta-analyses)
Criteria 5: Extracted data aligns with current study’s focus and research questions
A total of 21 articles were retained after screening.
Figure 1. Diagram of the screening process. The diagram shows how the studies were culled down from the initial database search to the final retained studies.
Initial studies resulting from database search (n = 418)
Studies excluded based on exclusion criteria (n = 397)
Potential studies appropriate for review (n = 21)
Studies retained for review (n = 19)
Studies excluded based on quality evaluation rubric
(n = 2)
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Table 2
Quality Assessment Rubric
Criterion 4 – Exceeds Standard 3 – Meets Standard 2 – Nearly Meets
Standard 1 – Does Not Meet
Standard I Objectives
and Purposes
Clearly articulated problem, objective, rationale, research
questions.
Adequately articulated. Poorly articulated. Incomplete.
II Review of Literature
Critically examines state of the field. Clearly
situates the topic within the broader field. Makes
compelling connections to past work. Discusses and resolves ambiguities in definitions. Synthesizes
and evaluates ideas; offers new perspectives.
Discusses what has and has not been done.
Situates topic within the broader field.
Makes connections to past work. Defines key
vocabulary. Synthesizes and evaluates ideas.
Minimally discusses what has and has not been done. Vaguely
discusses broader field. Makes few connections
to past work. Lacks synthesis across
literature. Minimal evaluation of ideas.
Fails to discuss what has and has not been done.
Topic not situated within broader literature. No
connections to past work.
III Theoretical or
Conceptual Frameworks
Clearly articulated and described in detail.
Frameworks align with study purposes.
Articulated; aligns with study purposes.
Implied or described in vague terms, or fails to
align with purposes.
Absent.
IV Participants Detailed, contextual description of population,
sample and sampling procedures.
Detailed description of population, sample and
procedures.
Basic description of sample and procedures.
Incomplete.
V Methods Instruments and their administration described in detail. Evidence for validity and reliability.
Documented best research practices. Potential bias
considered.
Instruments and their administration
described. Evidence for validity or
reliability. Some evidence of best
research practices. Potential bias considered.
Instruments described. Incomplete evidence of validity or reliability. Questionable research
practices.
Incomplete.
VI Results and Conclusions
Detailed results. Exceptional use of data
displays. Discussion clearly connects findings to past work. Proposes
future directions for research. Conclusions
clearly address the problem or questions.
Complete results. Sufficient use of data displays. Discussion connects findings to
past work. Conclusions address the problems
or questions.
Basic results. Insufficient use of data
displays. Discussion fails to connect
findings to past work. Conclusions
summarize findings.
Incomplete.
VII Significance Clearly and convincingly articulates scholarly and practical significance of
the study.
Articulates scholarly and practical
significance of the study.
Articulates scholarly or practical significance, but is neither clear nor
convincing.
Not articulated.
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Evaluation
In order to evaluate the quality of each article, a rubric that examined seven criterion
(objectives and purposes, review of the literature, theoretical frameworks, participants, methods,
results and conclusions, and significance) was used against the contents and each of the seven
parts were measured to see that they met the standards of quality reporting (Mullet, 2016) (Table
2). Each of the seven parts were scored on a 4-point scale where 1 = does not meet standard, 2 =
nearly meets standard, 3 = meets standard, and 4 = exceeds standard. After summing the seven
parts, the total possible score for each article was between 7 and 28. Articles that scored equal to
or less than 14 were excluded as not meeting the quality standard. After assessing the quality of
each article, two were excluded and 19 retained.
Data Collection Process
Data extracted included the research questions, participant information, methods, results,
discussions, and conclusions. All extracted data were stored in a database indexed by article.
Data Analysis
Thematic analysis (Braun and Clarke, 2006) was used as a method for identifying,
analyzing, and reporting themes (or patterns) within the data. Each theme is meant to capture
important information about the data. Braun and Clarke (2006) recommend six phases of
thematic analysis. The first phase involved becoming familiar with the data, the second phase
was where initial codes are generated, the third phase involved an initial search for themes by
collating the codes, the fourth phase required that each theme is checked or reviewed to ensure
the coded extracts work in relation, the fifth phase was when the themes are defined and named,
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and the sixth phase was producing the report from the themes by relating them back to the
research questions.
After becoming familiar with the data by reading and re-reading, initial codes were
created. Codes were generated by examining the results or findings, discussion, and conclusion
sections line by line looking for relevant data. Each time a piece of data addressed one of the
research questions it was extracted. Approximately 360 total pieces of data as text segments, or
codes, were extracted from the 19 articles. These codes were then sorted into themes, each theme
was then checked against the data and in relation to the research questions. The themes were then
defined and named by refining the specifics within theme. Finally the results were generated to
relate the findings back to the research questions.
Results
• How does existing research characterize teachers’ perceptions of utilizing STEM pedagogy?
Existing research gives us some insight into the beliefs teachers hold about STEM
education. There are six themes in the existing data to answer this question: teacher variables,
application activities, cross-curricular integration, student enjoyment, student struggles, and
value of STEM.
Teacher Variables
Differences have been found among various types of teachers in regard to STEM. For
example, a teacher’s years of experience seem to have some influence on their feelings. One
study showed more experienced teachers (>15 years) had a more positive view of the importance
of STEM education when compared with new teachers (between 1-5 years of experience) (Park,
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Byun, Sim, Han, & Baek, 2016), while another study showed a teacher’s years of experience was
not associated with their knowledge of or comfort with teaching STEM (Nadelson et al., 2013).
Another study found teachers with moderate experience (between 6-15 years) were actually least
familiar with engineering characteristics and likely to have a bias against female students’ ability
to learn STEM (Hsu, Purzer, & Cardella, 2011).
Age and gender of teachers may also play a role in their perceptions of STEM education;
Nadelson et al. (2013) found as a teacher’s age increased so did their positive attitude toward
engineering in the classroom. Female teachers have been shown to perceive technology as less
important within the STEM field than their male colleagues (Smith, Rayfield, & McKim, 2015)
and have also been found to have a more negative view in general of STEM education than male
teachers (Park et al., 2016).
A relationship (r = .21, p < .05) has also been found between the number of college level
math or science courses taken and a teacher’s comfort level for teaching STEM, such that the
more courses taken the more comfortable the teacher (Nadelson, Seifert, Moll, & Coats, 2012).
Differences also seem to exist between elementary and secondary teachers. Secondary teachers
have been found to have a more negative view of the potential impact of STEM education on
student achievement when compared to elementary teachers (Park et al., 2016). In addition, a
group of middle school teachers identify the importance of professional development and
experience in working with engineering in the curriculum. Teachers with prior experiences with
engineering scored higher on their knowledge of instructional techniques associated with STEM
education and their ability to meet the goals of STEM education (Van Haneghan, Pruet, Neal-
Waltman, & Harlan, 2015). For all teachers knowledge of STEM content seems to matter.
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Teacher efficacy, confidence, and comfort for teaching STEM all appear to be positively
correlated to knowledge of STEM content (Nadelson et al., 2012; Nadelson et al., 2013).
Application Activities
Teachers emphasize the importance of student participation in application activities
within STEM education as a crucial indicator of their academic achievement. The hands-on,
application activities that are a fundamental part of STEM education are highly valued by
teachers as a necessary and beneficial tool for student learning outcomes. Teachers also note that
these engaging, kinesthetic activities motivate their students (Bruce-Davis et al., 2014; Dare,
Ellis, & Roehrig, 2014; Goodpaster, Adedokun, & Weaver, 2012; Van Haneghan et al., 2015).
Secondary teachers felt the engineering-based hands-on activities would be particularly useful as
students are mastering math concepts (Asghar, Ellington, Rice, Johnson, & Prime, 2012). Rural
teachers also noted the value of these activities as they allow students to link science to their
rural lives (Goodpaster et al., 2012).
Cross-Curricular Integration
Teachers seem to feel the cross-curricular nature of STEM education is valuable to
student learning. They believe including engineering with the other subjects adds a valuable
problem-solving and real-world aspect to instruction that will give students an advantage in
preparedness for their futures. The cross-curricular connections students make are seen as an
advantage to STEM education as they give students necessary skills to approach and solve
problems similar to those they will encounter in future careers (Asghar et al., 2012; Bruce-Davis
et al., 2014; Dare et al., 2014; McMullin & Reeve, 2014; Smith et al., 2015). Technology
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teachers were particularly interested in using this integrated cross-curricular approach in their
classrooms (Asghar et al., 2012).
Student Enjoyment
Although teachers believe STEM education is inherently motivating to students, they also
feel they need further scaffolding techniques to increase motivation once students begin feeling
frustrated with the challenging nature of the engineering design process. Teachers feel the
persistence and interest gained by students are very valuable as they work on STEM challenges,
and that students eventually begin to feel motivated and empowered by their ability to solve
complex problems. The complex, open-ended design of STEM challenges also lead to student
increases in academic achievement. Teachers commented that the addition of engineering to their
math and science curricula brings them to life. They also felt students are genuinely interested in
STEM problems. Teachers note an overwhelmingly positive response from students during
STEM education. Moreover, teachers felt this increase in student enjoyment and engagement
was the main reason for integrating STEM into their curriculum (Dare et al., 2014; Lesseig,
Slavit, Nelson, & Seidel, 2016; McMullin & Reeve, 2014; Van Haneghan et al., 2015).
Student Struggles
Failure is an inherent part of the engineering design process within STEM education.
Students are asked to improve upon their designs and solutions. They are encouraged to take
risks. Teachers feel this is beneficial to students, especially high-achieving students that typically
do not reach a point of frustration in their classrooms (Dare et al., 2014). Because failure is part
of the process it is expected and therefore accepted. This encourages student to do things they do
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not know how to do and challenge themselves to confront failure. Teachers feel this helps
encourage students to think for themselves and better understand the content in their classes by
thinking critically about it (Holstein & Keene, 2013). Teachers value these authentic learning
experiences, without one right answer (Bruce-Davis et al., 2014). Several teachers were happily
surprised that low-performing students were able to be successful in the less-structured and more
challenging STEM problems (Lesseig et al., 2016; Wang, Moore, Roehrig, & Park, 2011).
Teachers even reported that students were reluctant to begin with the STEM equipment because
they were afraid it would be damaged. Students had to learn that making mistakes was going to
happen and experimenting with the equipment would help them solve their STEM problem
(Dare et al., 2014).
Value of STEM
The value teachers place on STEM education seems to be important to their instruction.
Student learning is limited when teachers’ knowledge and understanding is deficient (McMullin
& Reeve, 2014). Teachers who have limited knowledge and comfort with STEM may feel they
are unable to contribute to classroom learning during STEM activities. On the other hand,
teachers that feel they have the knowledge and skill sets to implement STEM activities have a
high self-efficacy towards this type of learning. Teacher perceptions of the importance of STEM
influences their ability to learn and develop as STEM educators (Bell, 2016). This will affect
how they teach STEM curriculum. Teachers have reported challenges associated with learning
the content while teaching multiple courses together (Goodpaster et al., 2012). Teachers felt
developing new STEM problems while integrating different domains was difficult. Teachers also
reported trouble combining the STEM pedagogical approach with their content concepts (Asghar
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et al., 2012). These teachers seemed to be unable to see these things as anything but separate.
Even after professional development, some teachers are still uncomfortable using STEM
activities in their classroom (Asghar et al., 2012). Many professional development facilitators
have seen resistance by teachers to utilizing STEM (Dare et al., 2014; Holstein & Keene, 2013).
STEM teachers show a range of fidelity with implementation. Engineering appears to be the
content area teachers are least confident in teaching (Smith et al., 2015). This may be why there
is a lack of evidence that engineering decisions are explicitly being made by teachers and
students during STEM education (Lehman, Kim, & Harris, 2014; Van Haneghan et al., 2015).
While some teachers saw STEM as one more thing they needed to cover in their
classrooms, many teachers felt it was a valuable way for students to learn. Teachers felt strongly
that STEM should be integrated into students’ K-12 education (Hsu et al., 2011; McMullin &
Reeve, 2014; Smith et al., 2015). In other words, they felt STEM is important. Teachers believed
STEM leads to higher expectations for students after high school and felt increases in scientific
literacy were valuable as they challenge students to think critically about current issues and
future implications in their own lives (Bruce-Davis et al., 2014). These K12 elementary and
secondary teachers (n = 729) believed STEM education has a positive impact on student learning
and outcomes (Park et al., 2016). Teachers also experienced rewarding feelings of making a
difference with students and their communities when utilizing STEM education (Hsu et al.,
2011; Goodpaster et al., 2012; Wang et al., 2011). Teachers seemed to focus on how they could
improve their STEM lessons the next time they use them. They were working on future
improvements almost as soon as the lesson was over (Dare et al., 2014).
Teacher perceptions of STEM education influence how they design their STEM units and
their methods of delivery of instruction. A dynamic teacher, with a positive attitude toward
17
STEM seems to be the single most important factor to implementation fidelity and STEM
program success (McMullin & Reeve, 2014). These teachers believe STEM integration can
improve their students’ learning outcomes (Bagiati & Evangelou, 2015; Dare et al., 2014; Wang
et al., 2011).
• What do teachers identify as challenges and barriers to using STEM pedagogy in their classrooms?
The areas teachers identified as challenges and barriers related to STEM education can be
organized in six categories: pedagogical challenges, curricular challenges, structural challenges,
student concerns, assessment concerns, and teacher supports.
Pedagogical Challenges
Several pedagogical challenges were cited by teachers as inhibiting factors to STEM
implementation. For example, teachers mention STEM pedagogy requires a fundamental shift
away from teacher led instruction to student led instruction (Lesseig et al., 2016). Teachers have
to be able to step out of the director role and allow students to find their own way during the
lesson, which might involve unexpected directions (Lesseig et al., 2016). Another similar
concern is that teachers must have a view of instruction that aligns with the philosophy of the
STEM curriculum authors. There must be a match between the teachers’ pedagogy and the
curriculum pedagogy (Holstein & Keene, 2013). Teachers voiced a concern that they might
incorrectly or inadvertently misinterpret the STEM developer’s expectations (Bagiati &
Evangelou, 2015; Holstein & Keene, 2013). One last pedagogy concern is utilization of STEM
could actually hinder direct instruction of science content. Dare et al. (2014) found secondary
18
teachers noted that they were not teaching science concepts as well when utilizing STEM in their
classrooms.
Curriculum Challenges
The second category teachers felt could be a barrier to quality STEM programming was
curricular concerns. Teachers had apprehension about following someone else’s curriculum plan
(Bagiati & Evangelou, 2015). Teachers were also concerned about integrating STEM curriculum
into their existing curricula. District alignment and grade level standards can be inflexible, which
prevents a smooth integration of STEM. In addition, teachers noted they felt STEM curriculum
could be inflexible and the difficulty with combining two inflexible curricular plans (Bagiati &
Evangelou, 2015; Lesseig et al., 2016). Secondary teachers felt their domain specific courses
(biology II, geometry, etc.) did not integrate well with other STEM disciplines (Asghar et al.,
2012). Also, secondary teachers felt miscommunications between various STEM teachers’
perception of each other’s domain led to feelings of anger and subsequently caused
interdisciplinary curriculum to fail. For similar reasons, teachers had concerns about developing
their own STEM-based curriculum with teachers from other subject areas (Asghar et al., 2012;
Bell, 2016). Teachers were also concerned about STEM curriculum’s ability to impart
meaningful learning (Asghar et al., 2012). When implementing STEM curriculum, teachers were
observed treating the inclusion of specific content as more of an afterthought (Dare et al., 2014).
Structural Challenges
The third issue teachers felt was a barrier to STEM was structural challenges. This refers
to the structure of schools, schedules, and districts. Teachers felt the confines of class scheduling
19
prohibited the interdisciplinary nature of STEM lessons, and various teachers teaching their own
specific subjects was not conducive to interdisciplinary work either. This same scheduling
prevented teachers in different subjects from planning together (Asghar et al., 2012; Dare et al.,
2014; Lesseig et al., 2016). The structure of student schedules, and lack of flexibility in them,
was also cited as a barrier to STEM (Lesseig et al., 2016). At the district level, teachers felt
administrative and financial supports could be a challenge to STEM implementation (Asghar et
al., 2012; Clark & Andrews, 2010; Hsu et al., 2011; Park et al., 2016). Another concern was a
lack of technology resources available to students. Without student computers and other
technology tools available, it was difficult to integrate the technology piece into STEM lessons
(Wang et al., 2011). The last structural concern was the way education is organized and
evaluated at the state level (Asghar et al., 2012).
Student Concerns
Teachers believed that students are unable or unwilling to be successful with STEM
programming. Several studies reported teachers underestimate student abilities to solve STEM
problems (Asghar et al., 2012; Bagiati & Evangelou, 2015; Goodpaster et al., 2012; Van
Haneghan et al., 2015). Many of these teachers did not believe their students were competent
enough in content areas to apply these skills to self-directed STEM problems. They felt these
types of problems would be very difficult for their students and would cause their students to
become unmotivated to learn. Teachers reported a need for instructional tools they could use to
motivate students and get them interested in STEM subjects. In addition, rural teachers noted the
challenges associated with modifying the curriculum in order to meet the needs of
20
underperforming students (Goodpaster et al., 2012). Teacher beliefs about their students may be
related to their implementation fidelity of STEM curriculum (Holstein & Keene, 2013).
Assessment Concerns
In one study more than 40% of the teachers felt there was a lack of assessments for
STEM programs (Nadelson & Seifert, 2013). These teachers and many others felt there are not
enough standardized classroom assessments to use with STEM lessons. This makes assessing
student learning in a STEM curriculum very difficult. Teachers felt there are not enough
formative assessments to discover what concepts students understand from other disciplines
(Asghar et al., 2012; Dare et al., 2014). Formative assessments help teachers know when
reteaching or remediating is necessary or when students already know the material.
Teacher Supports
Teachers were concerned with the increased workload associated with STEM
programming. They have to find more time to plan with other subject areas and to prepare the
materials for students. Presenting the material and allowing for varying ability levels among
students also required more time. This makes a lack of time one of the primary concerns teachers
had when implementing STEM (Bagiati & Evangelou, 2015; Hsu et al., 2011; Goodpaster et al.,
2012; Park et al., 2016).
Teachers also felt they had a lack of subject matter knowledge concerning STEM content.
Pre-service and in-service training was seen as inadequate in preparing teachers to implement
STEM. Teachers felt they needed clarity about how the program was supposed to be
implemented into existing programs (Nadelson & Seifert, 2013). They did not feel fully prepared
21
to integrate STEM subjects (Hsu, et al., 2011). Although teachers felt STEM education was
important and valuable, they were not comfortable with meeting the high teacher expectations
they felt were associated with STEM. Feeling unsure about one’s ability to teach STEM could
lead teachers to a reduced confidence in their teaching efficacy (Bagiati & Evangelou, 2015;
Clark & Andrews, 2010; Holstein & Keene, 2013).
• What do teachers feel supports their efforts to implement STEM?
Some studies captured ways that teachers might need additional support. Five main areas
were found in the research that addressed this need for support. They were in the areas of:
collaboration, curriculum, district support, prior experiences, and professional development.
Collaboration
Several studies found that a culture of collaboration would increase the viability of
STEM programs (Asghar et al., 2012; Bruce-Davis et al., 2014; Lehman et al., 2014; Wang et al.,
2011). Teachers explained the importance of collaborating with other STEM teachers and
university professionals in order to not only create an atmosphere that enhances preparation for
STEM lessons, but also to model a team approach to students. STEM pedagogy required students
to collaborate to solve challenges, so a teacher modeling the strength of a group approach is
beneficial. One teacher noted that teachers had been siloed in the past and an integrated team
approach was necessary for STEM planning and implementation (Asghar et al., 2012). Other
teachers attributed much of their success with STEM to partnerships with university faculty and
accessing the expertise in their community (Lehman et al., 2014). These supports helped the
22
teachers feel comfortable taking risks and delving deeper into STEM concepts outside their
comfort area.
Curriculum
Quality curriculum was another often-cited factor for STEM success (Asghar et al., 2012;
Lehman et al., 2014; McMullin & Reeve, 2014; Van Haneghan et al., 2015; Wang et al., 2011).
Teachers discussed the importance of a flexible curriculum that is engineering-based. In order to
be effective, the curriculum must be flexible enough to be used with various ability levels and
educational environments while still being focused on the engineering design process (Lehman et
al., 2014). Teachers believed this type of curriculum increased their belief in themselves, or self-
efficacy, to teach STEM (Lehman et al., 2014; Van Haneghan et al., 2015). The STEM
curriculum or modules must also be explicitly and tightly connected to the standards. In addition,
these modules need to be developmentally appropriate (Asghar et al., 2012). Teachers expressed
a desire for specific, ready-made STEM problems they could use in their classrooms
immediately. These problems must be grounded in the STEM disciplines and driven by the
standards (Asghar et al., 2012, Wang et al., 2011). Also, there must be fidelity in the
implementation of the curriculum such that the teachers utilize the expectations and goals
intended by the curriculum designers (McMullin & Reeve, 2014).
District Support
This was cited as the number one most important factor to STEM success in two of the
studies (Bruce-Davis et al., 2014; McMullin & Reeve, 2014). It was also mentioned by other
studies as an important factor (Holstein & Keene, 2013; Park et al., 2016). A supportive
23
administrator or administrative team is important when teachers are implementing STEM
pedagogy. Teachers believed guidance by administrators is needed in order to successfully
utilize STEM programs (Holstein & Keene, 2013; McMullin & Reeve, 2014). Teachers believed
it was necessary for their school districts to allow flexibility for them to expand the curricula and
instruction beyond national and state standards, so they were able to offer problems that meet
student interests, talents, and academic needs (Bruce-Davis et al., 2014). In addition, the K-12
curricular framework or scope and sequence should be restructured to allow for STEM
programing (Park et al., 2016).
Teachers also felt that districts need to help parents and students understand course
offerings and what STEM courses will teach them. Secondary teachers believed another
important consideration is how high school credit will be given in these courses. More math or
science credit might have increased student enrollment rather than only giving elective credits
for taking STEM courses (McMullin & Reeve, 2014).
Prior Experiences
Prior experiences by teachers were seen as facilitators to STEM success. Teachers who
had more science or math courses in college (Park et al., 2016) or had utilized similar
instructional methods (i.e. Problem-Based Learning, inquiry-based learning, questioning
techniques, guided independent research studies) felt these experiences allowed them to promote
the inductive and deductive reasoning across disciplines necessary for STEM (Bagiati &
Evangelou, 2015; Bruce-Davis et al., 2014). Confidence with STEM pedagogy increased
because of these prior experiences.
24
Professional Development
The most often mentioned support that would increase the effectiveness of STEM
education was learning opportunities for teachers to increase their ability to effectively integrate
STEM content into their curriculum. Teachers at multiple stages in their careers reported
significant increases in their confidence, knowledge, and efficacy to teach STEM after attending
professional development programs (Lesseig et al., 2016; Nadelson et al., 2012; Nadelson et al.,
2013; Nadelson & Seifert, 2013; Van Haneghan et al., 2015). Effective professional development
or continuing education needs to provide time and structure for teachers to explore how STEM
can be integrated within their curriculum while focusing on increasing teacher’s content
knowledge and experiences with STEM. Research indicates these factors will directly influence
teacher practice and student learning (Nadelson et al., 2013; Van Haneghan et al., 2015).
Teachers wanted more strategies to improve student performance in engineering design
challenges (Bruce-Davis et al., 2014; Lesseig et al., 2016; Van Haneghan et al., 2015). Teachers
need support when incorporating engineering into their math and science instruction (Lehman et
al., 2014). Teachers felt support for planning and implementation should be ongoing and include
pedagogical tools they can use to increase student academic success (Lesseig et al., 2016).
Discussion
Recommendations for Practice
In order to support teachers and STEM programs as they seek to develop STEM talent,
necessary provisions must be provided so they can act as a facilitating catalyst in the student’s
development. It seems very promising that the results from multiple studies were so similar in
their findings concerning challenges and supports. Teachers need quality curriculum that aligns
25
with district and state guidelines and includes formative assessment techniques teachers can use
to assess their students’ conceptual understandings. Professional development that is attended by
the team of teachers that will be utilizing the curriculum (Nadelson et al., 2012) and allows
teachers to gain experience with STEM concepts and the pedagogy in a meaningful way is also
necessary. The pedagogical strategies associated with STEM must be explicitly taught to
teachers and modeled in order to improve fidelity of programming. Teachers have to become
comfortable allowing their students to “take the wheel” and drive instruction. They have to learn
how to play the role of facilitator of knowledge and how to encourage students to take academic
risks. All of this can be practiced and reinforced in professional development before
implementation in classrooms. The National Research Council (2013) recommends that districts
develop a mechanism for focused professional development to be coordinated that aligns with
instructional reforms and provides high-quality learning opportunities for teachers. The content
knowledge and affective needs teachers have regarding STEM instruction must be attended to
during in-service learning.
District administrators must be aware of the need for increased time to collaborate with
the other STEM teachers. Building time into the school year for teachers to plan lessons and
prepare lessons with colleagues would facilitate STEM integration. As teachers move from
teaching single subjects to teaching cross-curricular units they will need time to work together.
Teachers need to be encouraged to work together to create innovative ways to successfully
integrate this multidisciplinary way of thinking and learning. An open and transparent means of
communicating is necessary within the school and district concerning teacher needs and supports
regarding STEM education.
26
Teachers appear to value STEM education and believe it enhances student-learning
outcomes while preparing students for their future. With increased confidence teachers would
likely be more effective at integrating STEM activities. The research seems clear that increased
confidence leads to better performance during instruction and this will lead to gains in student
learning (Nadelson et al., 2012; Nadelson et al., 2013).
Recommendations for Future Research
More studies that document the success of STEM programs with low ability and diverse
student populations would be beneficial encouragement to teachers. Teachers need to believe all
students can benefit from STEM instruction. As they begin to experience student success in their
classrooms, they will be encouraged to continue implementing STEM activities.
A study examining differences in STEM perception among gifted and non-gifted
educators would be interesting in light of the pedagogical similarities between STEM and gifted
education (Mann & Mann, 2017). An examination of the confidence levels of these two groups
of teachers as they develop STEM talent within their classrooms would yield useful data for
future professional development.
Further study of effective implementation of the core tenets Moore et al. (2014)
suggested should be conducted in classrooms in various school settings (urban, rural, and
suburban). There may be specific pedagogical needs within different settings that can be
addressed with teacher in-service instruction.
Research into effective formative assessment strategies during STEM education needs to
be conducted. Teachers felt this was a missing component of STEM programs (Asghar et al.,
2012; Dare et al., 2014). Effective ways to gauge student understanding would yield more
27
efficient STEM talent development. Teachers could better differentiate for various ability levels
within their classrooms if they better understand each student’s progress throughout the unit
(Tomlinson & Moon, 2013).
More work needs to be done in order to understand how best to support teachers as they
attempt to integrate STEM education into their classrooms (Dare et al., 2014).
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*Lesseig, K., Slavit, D., Nelson, T. H., & Seidel, R. A. (2016). Supporting middle school teachers’ implementation of STEM design challenges. School Science and Mathematics, 116(4), 177-188. doi: 10.1111/ssm.12172
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*Nadelson, L. S., Callahan, J., Pyke, P., Hay, A., Dance, M., & Pfiester, J. (2013). Teacher STEM perception and preparation: Inquiry-based STEM professional development for elementary teachers. The Journal of Educational Research, 106(2), 157- 168. doi: 10.1080/00220671.2012.667014
*Nadelson, L. S., & Seifert, A. (2013). Perceptions, engagement, and practices of teachers seeking professional development in place-based integrated STEM. Teacher Education and Practice, 26(2), 242-265. Retrieved from https://journals.rowman.com
*Nadelson, L. S., Seifert, A., Moll, A. J., & Coats, B. (2012). i-stem summer institute: An integrated approach to teacher professional development in stem. Journal of STEM Education: Innovations and Research, 13(2), 69-83. Retrieved from www.jstem.org
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*Park, H., Byun, S., Sim, J., Han, H., & Baek, Y. S. (2016). Teachers’ perceptions and practices of STEAM education in South Korea. Eurasia Journal of Mathematics, Science, & Technology Education, 12(7), 1739-1753. doi: 10.12973/Eurasia.2016.1531a
*Smith, K. L., Rayfield, J., & McKim, B. R. (2015). Effective practices in STEM integration: Describing teacher perceptions and instructional method use. Journal of Agricultural Education, 56(4), 182-201. doi: 10.5032/jae.2015.04183
Tomlinson, C. A., & Moon, T. R. (2013). Assessment and student success in a differentiated classroom. Alexandria, VA: ASCD.
*Van Haneghan, J. P., Pruet, S. A., Neal-Waltman, R., & Harlan, J. M. (2015). Teacher beliefs about motivating and teaching students to carry out engineering design challenges: Some initial data. Journal of Pre-College Engineering Education Research, 5(2), 1-9. doi: 10.7771/2157-9288.1097
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*Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research, 1(2), 1-13. doi: 10.5703/1288284314636
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TEACHER SELF-EFFICACY FOR STEM TALENT DEVELOPMENT
Many schools in the United States are trying to implement more science, technology,
engineering, and mathematics (STEM) integrated pedagogy into their curriculum and instruction
in response to the national call to improve the math and science scores through innovative
teaching (Fleischman, Hopstock, Pelczar, & Shelley, 2010; The White House, n.d.). According
to the U.S. Department of Commerce, STEM jobs are expected to grow in greater numbers than
non-STEM jobs through 2018 (Langdon, McKittrick, Khan, & Doms, 2011). Therefore, it is
important our students are exposed to quality STEM experiences and pedagogy so they can find
out if this is an area of interest.
With the current focus on integrating STEM pedagogy in our schools, district
administrators need to understand what professional development and support systems teachers
need. It has been recommended by the National Science Board (NSB, 2010) that STEM
pedagogy begin in elementary school. It is the responsibility of the NSB to establish policies for
the National Science Foundation and to serve as advisor to both Congress and the President of
the United States. NSB ultimately influences what content teachers use in their science
classrooms. Therefore, many teachers K-12 will likely be called upon to implement STEM
pedagogy.
Although this seems a noble goal, there is an important variable to consider: Are the K12
teachers in our schools prepared to teach using an integrated STEM pedagogy? Since existing
research suggests that the teacher can be the most powerful predictor of student success in the
classroom, teacher preparedness is a salient topic (Goe & Stickler, 2008; Klem & Connell,
2004). The current study examined the self-efficacy of elementary and secondary teachers
regarding the implementation of innovative STEM pedagogies.
32
STEM Pedagogy in the K12 Classroom
Clarification of what is meant by STEM pedagogy is necessary. Evidence indicates that
teachers themselves may be confused about how to conceptualize this topic (Brown, Brown,
Reardon, & Merrill, 2011). STEM is a way to teach science, technology, engineering, and math
with an interdisciplinary and applied approach. Students are generally asked to solve complex
problems by applying the skills they have learned. Many schools refer to a very similar learner-
centered approach to instruction as Project or Problem Based Learning (PBL) (Dailey &
Cotabish, 2016). The students apply the scientific method and problem solving skills in
assignments related to everyday life. Students, usually in groups, engage in the engineering
design process to solve the task they have been given. This process is cyclical and allows them to
repeat the steps making improvements as many times as they feel are necessary to develop a
solution. For example, students might try to both understand how much water is being wasted as
a result of a leaky water fountain in their school and how to fix the fountain. Working together, a
group of students would apply their science and math knowledge to determine a solution to this
problem. After building a prototype, students would use the engineering design process to
address any revisions that could be made to improve upon their solution.
Technology is used as a tool during this process both for research, measurement, and
presentation purposes. Additionally, STEM pedagogy focuses on student awareness of STEM
occupations. Careers are highlighted and explained so students will understand their options in
the future. STEM investigations help students develop autonomy, investigative thinking,
reasoning, and teamwork skills they can use in all aspects of their lives. Teachers can use both
formal and informal learning environments to foster the basic STEM principles (Denson, Hailey,
Stallworth, & Householder, 2015). An example of informal STEM learning environments would
33
be students’ curiosity about why the swings on the playground would never allow students to
swing all the way around the bar at the top. The teacher guides students through developing a
model they could use to test and determine an answer to their question. Using the design process,
students could improve upon their solutions. A goal of STEM pedagogy is to foster deeper
connections among the STEM disciplines and real-world problems (Kelley & Knowles, 2016).
Teachers are required to teach STEM content in intentional ways so students will
understand how the content can be applied to complex problems (Kelley & Knowles, 2016). It is
important that connecting ideas across disciplines be explicitly taught because students cannot
always naturally make these connections and apply content knowledge in integrated settings.
Teachers have to provide support and modeling so students can connect their knowledge and
ideas to effectively solve these real-world problems (National Research Council, 2012). STEM is
more than just content; it is a pedagogy—an intentional method of teaching—which includes an
interdisciplinary curriculum as a blueprint for instruction. In order to foster student growth,
teachers must use a strategic approach when implementing STEM pedagogy. A teacher’s belief
about his or her ability to implement this type of pedagogy is critical. In fact, when teachers are
uncomfortable with STEM content, students have shown decreased learning and negative
perceptions of the lesson (Beilock, Gunderson, Ramirez, & Levine, 2010).
STEM pedagogy requires teachers to use an instructional approach (inquiry-based
problem solving process) that may be new to them. Often, participating in STEM programming
requires extensive training for teachers which is provided by the school district. The way a
teacher perceives this type of pedagogy in his/her classroom may affect its implementation
(Wang, Moore, Roehrig, & Park, 2011). Existing research indicates that a teacher’s perceptions
about his or her ability to teach, a teacher’s self-efficacy, is linked to student success in the
34
classroom (Tschannen-Moran & Woolfolk Hoy, 2001). Therefore, it is important to examine
teacher self-efficacy as it relates to developing STEM talent among students at all levels.
Role of Teacher Self-Efficacy in Talent Development
In order to determine the relationship between teacher self-efficacy and STEM talent
development, multiple theoretical frameworks were integrated within this study. Both
developmental and learning theories were applied in order to understand the complex interaction
between teachers’ behavioral responses and their students’ talent development.
Social Cognitive Theory
Social cognitive theory views learning as an integration of cognitive processes and social
context (Baer & Bandura, 1963; Bandura, 1978). Social cognitive theory is considered both a
cognitive and behavioral theory. This theory posits that learning occurs through observations in
an individual’s social context (Baer & Bandura, 1963). Within this theory, self-efficacy is a
mechanism of agency through which a person constructs and selects his or her environment.
Self-efficacy is the belief a person holds about his or her capabilities to carry out a given task or
course of action successfully (Bandura, 1978). Research conducted on self-efficacy supports the
value of this construct and its influence on one’s achievement and behavior (Bandura, 1993).
Schools provide teachers with many challenges such as minimal control over school
environment, shared responsibility for student achievement, and accountability to the public.
According to Bandura (1997), this makes developing high levels of teacher self-efficacy difficult
but not impossible. Teacher self-efficacy is an important factor related to student success and
motivation (Bandura, 1997; Goddard, Hoy, & Woolfolk Hoy, 2000; Tschannen-Moran &
35
Woolfolk Hoy, 2001), and teacher self-efficacy is positively correlated with student
achievement. A teacher’s self-efficacy may vary toward specific content areas and teaching
environments as self-efficacy is a complex phenomenon dependent on many variables. A
teacher’s performance in the classroom is affected by his or her self-assessment of ability to
teach (Bandura, 1997). The teacher’s level of persistence in instruction, personal goals, and
amount of effort are all affected by self-efficacy (Tschannen-Moran & Hoy, 2001). The
relationship to student achievement is a strong reason to investigate teachers’ self-efficacy
related to specific content areas and teaching environments, especially when the content and
environment represent new and innovative approaches to teaching. The three factors necessary to
develop STEM talent according to Oakes (1990) include both the opportunity to study math and
science and achievement in these subjects, but the third factor is the student’s decision to pursue
these areas. Therefore, teachers are expected to cultivate student interest in STEM subjects.
Talent Development Model
In order to support talent development in STEM, teachers need to understand the
developmental trajectory associated with their students’ competencies in this area. Gagné’s
(2011) differentiated model of giftedness and talent (DMGT) suggests a clear path through
learning and training (developmental process) to develop one’s natural abilities into talents.
Gagné’s model also includes catalysts that can either facilitate or inhibit the talent development
process. There are two categories of catalysts in in the model. Intrapersonal catalysts include
factors such as personality, passions, effort, and health. Environmental catalysts include external
provisions such as school programs (e.g., STEM programs) and individuals (e.g., teachers and
mentors). Having a STEM program available to students reasonably improves their chances of
36
developing STEM talent; however, the teacher plays an important role in the talent development
process as an individual of influence who can foster or hinder STEM talent development (Gagné,
2007).
Subotnik, Olszewski-Kubilius, and Worrell (2011) also proposed a model for the
development of talent that includes a goal of elite student performance. The talent domain
trajectory for math and science suggested by this model begins in early childhood and continues
into adulthood. This makes the elementary and secondary school years an important place for
individuals to progress through higher stages of talent development in the STEM domains.
Elementary school has a critical role in the development of STEM talent as the early years are
where interests and dispositions toward science and math are beginning. Within this model, early
exposure to STEM activities is critical as it allows teachers to cultivate student interest and
potential through engaging problem-solving approaches to learning.
Teacher Self-Efficacy
Teacher self-efficacy, defined as a teacher’s belief about his or her capability to promote
student learning, has been shown to influence both teaching behaviors and student achievement
(Klassen & Chiu, 2010). Low self-efficacy in teachers has been linked to high levels of job stress
and more difficulty with instruction (Betoret, 2006). Self-efficacy plays a central role within
Bandura’s (2006) social cognitive theory. Because of its prominent role in learning, it has long
been an important construct for education researchers.
Lack of knowledge on the subject matter being taught is a predictor of low self-efficacy
(Menon & Sadler, 2016). This makes self-efficacy particularly salient to STEM pedagogy
research as data has indicated that many teachers—one half in one study—are not even able to
37
adequately define STEM (Brown et al., 2011). Conversely, research shows that teachers with a
greater sense of self-efficacy are better able to implement STEM pedagogy in their classrooms
and are more comfortable with teaching STEM (Nadelson, Seifert, Moll, & Coats, 2012).
Teachers’ self-efficacy is influenced by their pedagogical skills (or lack of) in STEM areas and
how comfortable they feel grading student work or projects in this type of learning environment
(Menon & Sadler, 2016).
Demographic Influences
Several demographic variables (gender, years of teaching experience, number of college
level math and science courses taken, grade level taught, age, feelings of administrative support,
and relevant professional development experiences) have been found to affect levels of teacher
self-efficacy. For example, Klassen and Chiu (2010) found that female teachers had lower self-
efficacy for classroom management. On the other hand, Atta, Ahmad, Ahmed, and Ali (2012)
found females had a higher overall teacher self-efficacy and that more teaching experience
correlated with a higher self-efficacy. Park, Byun, Sim, Han, and Baek (2016) found female
teachers had a more negative view of STEM pedagogy when compared to male teachers. Kumar
and Morris (2005) found teacher gender was statistically significant (R2 = .60) in predicting
attitude towards math and science.
Results on the relationship between years of teaching experience and teacher self-efficacy
are varied. Tschannen-Moran and Woolfolk-Hoy (2006) found teachers who have more than
three years of teaching experience have higher self-efficacy than teachers with three or fewer
years. Other researchers have found the relationship between years of teaching experience and
self-efficacy to be non-linear. For instance, some studies indicate an increase in self-efficacy
38
throughout the early years of teaching and then an eventual decline midcareer (Klassen & Chiu,
2010; Woolfolk, Hoy, & Burke Spero, 2005). Further, Ghaith and Yaghi (1997) found a negative
correlation between teaching experience and teacher self-efficacy. It makes sense that some
experience in the field would make teachers feel more confident in their use of instructional
strategies and classroom management techniques.
Regarding teaching experience and STEM pedagogy, more experienced teachers (greater
than or equal to 15 years) felt more positively about the potential impact of STEM pedagogy
than teachers will less experience (one to five years) (Park et al., 2016). However, teachers with
moderate experience (six to fifteen years) are least familiar with engineering and have shown a
bias against females’ ability to learn STEM concepts (Hsu, Purzer, & Cardella, 2011).
The grade levels taught also seem to affect a teacher’s self-efficacy. Elementary teachers
(grades K-6) report higher self-efficacy scores than secondary (grades 7-12) teachers (Klassen &
Chiu, 2010; Wolters & Daugherty, 2007). In looking for differences of STEM pedagogy beliefs
in the grade levels taught by teachers, Nadelson et al. (2013) did not find any notable effects
between grade levels. However, Park et al. (2016) found secondary teachers had a more negative
perception of STEM pedagogy’s potential impact on student learning compared to elementary
teachers.
Another variable of interest is the number of college level math and science courses a
teacher has taken, as this has been shown to influence STEM teacher self-efficacy. A teacher’s
STEM efficacy increases as the number of college level math and science course taken increases
(Nadelson et al., 2012). Kumar and Morris (2005) examined the predictive power of college-
level math and science courses on attitudes toward of math and science. Males were found to be
more influenced by college science experiences than females. College science classes did add
39
significant predictive accuracy to the prediction of teacher’s attitude toward math and science.
Nadelson et al. (2013) examined the relationship between teachers’ age and their perceptions of
STEM, and found age to be related to teacher attitudes toward STEM. As a teacher’s age
increased, so did his/her positive attitude.
Feelings of administrative support are associated with teacher self-efficacy, especially as
it relates to STEM pedagogy (Holstein & Keene, 2013; Park et al., 2016). Two studies found that
school district support was the most important factor to STEM success in classrooms (Bruce-
Davis et al., 2014; McMullin & Reeve, 2014). Teachers felt guidance by administration was vital
to their comfort with STEM pedagogy (Holstein & Keene, 2013). McMullin and Reeve (2014)
found that teachers believe school administrators send a message to students and parents by their
support that is foundational to STEM success.
The professional development sessions teachers have attended may influence their levels
of confidence, particularly with STEM pedagogy (Lesseig et al., 2016; Nadelson et al., 2012).
Teachers with various years of experience reported increased efficacy and confidence with
STEM pedagogy after attending professional development sessions (Lesseig et al., 2016;
Nadelson et al., 2012; Nadelson & Seifert, 2013). Research also indicated that increased efficacy
and confidence improved teacher practice and student achievement (Nadelson et al., 2013).
Self-Efficacy and STEM
Because teacher self-efficacy is very context specific, it should be examined within the
context and expectations of STEM pedagogy. Teachers appear to have the strongest feelings and
beliefs about engineering content and the pedagogy that is associated with it. Teachers believe
that engineering is the subject that has the ability to bring math and science to life (Dare, Ellis, &
40
Roehrig, 2014), but teachers have reported engineering to be the subject in which they are least
comfortable teaching (Smith, Rayfield, & McKim, 2015).
There are specific factors within teacher self-efficacy of STEM that are particularly
essential: content knowledge, classroom management, engagement, and outcome expectancies
for students. These are areas normally examined with teacher self-efficacy regardless of contexts.
Teachers report on a rating scale of each subject area that engineering is the lowest area of
content knowledge within the STEM disciplines (Smith et al., 2015). However, teacher comfort
with STEM education and efficacy for teaching STEM has been found to be positively correlated
with knowledge of STEM content (Nadelson et al., 2013). Classroom management is relevant as
the pedagogical strategies borrowed from engineering and used in STEM require teachers to
allow students to drive the instruction using investigative approaches on ill-structured problems.
This type of student-led classroom may be uncomfortable to many teachers. If a teacher is not
comfortable maintaining order in his or her class, or feels he or she lacks the disciplinary skills,
in this type of student-centered instruction with ill-defined problems, student learning may suffer
(Dailey, 2016; Johnson, 2006).
A teacher’s beliefs in his or her ability to promote student engagement in STEM lessons
through the techniques and tools of STEM pedagogy are particularly relevant as these
opportunities for students to solve real-world problems have been shown to increase student
engagement. Moreover teachers’ beliefs influence student attitudes and behaviors (Bagiati &
Evangelou, 2015). Teacher self-efficacy regarding student outcomes and their expectations for
students are crucial to STEM success. Student outcomes from STEM pedagogy include
developing an appreciation for and capacity to engage in complex challenges through deep
knowledge and skill mastery, creativity, and problem solving (National Research Council, 2012).
41
STEM and Gifted Education
Whether or not a teacher has experience teaching gifted students is also a variable of
interest. Inquiry-based instruction and problem-based learning (PBL) have been widely used
with high-ability students (Gallagher, 2005; VanTassel-Baska, 2011). These models are
pedagogically similar to STEM in that students are using authentic questions, defining problems,
designing and conducting investigations, and then using evidence to design a solution all while
working in student groups (Dailey, 2016). In many cases, teachers of gifted students are already
integrating STEM pedagogy into their curriculum. The National Association of Gifted Children
(NAGC) programming standards for PK-12 emphasize inquiry, collaborative learning
environments, and problem solving as key components of gifted education (NAGC, 2010). These
objectives of gifted education are similar to those of STEM pedagogy. Teachers of gifted
students, as an innovative force, may have a unique perspective on teaching with STEM
pedagogy because of their prior experiences with collaborative problem-based learning.
Research Question and Hypotheses
Teacher self-efficacy can be measured regarding their abilities to utilize STEM pedagogy
in their classrooms. Knowledge of teacher confidence toward STEM pedagogy will inform
school administrators and the developers of in-service training (professional development) on
how to improve STEM program implementation. The hope is that this knowledge will ultimately
lead to enhanced student learning and STEM talent development by helping teachers feel more
confident with STEM pedagogy. As school systems seek to increase STEM pedagogy, they will
better understand what teachers need to feel confident with this type of instruction.
42
For this study, teacher self-efficacy of STEM is defined as the teachers’ personal belief
about their ability to positively influence student learning of STEM concepts (Yoon, Evans, &
Strobel, 2012). The following research question was used for the study and based on existing
research, eight specific hypotheses were tested: To what extent can the factors of a measure of
STEM self-efficacy be predicted by gender, grade level taught, number of college-level math and
science courses taken, gifted-teacher status, age, and length of teaching experience in K-12
teachers?
• Hypothesis 1: Male teachers will have a higher scores on the STEM content knowledge self-efficacy subscale than female teachers.
• Hypothesis 2: Elementary teachers will report higher subscale scores on disciplinary and engagement self-efficacy than secondary teachers.
• Hypothesis 3: Teachers of gifted students will have higher scores on all factors of STEM self-efficacy than teachers who do not teach in the gifted education program.
• Hypothesis 4: Age will be positively correlated with all factors of STEM self-efficacy.
• Hypothesis 5: As the length of teaching experience increases, so will STEM self-efficacy on the disciplinary subscale.
• Hypothesis 6: The number of college-level math and science courses taken will be positively correlated to the teacher’s STEM self-efficacy (on all factors).
• Hypothesis 7: Teacher STEM self-efficacy is related to perceptions of administrative support.
• Hypothesis 8: STEM teacher self-efficacy is related to the number of STEM professional development sessions the teacher has attended.
Method
Participants
Participants were 119 teachers from a school district in a southern state in the United
States. Twenty-six of the respondents were male, and ninety-one were female, two participants
43
did not indicate their gender. The participants ages ranged from 23 to 69 (Mean = 41.9; SD =
9.35).
The sample was drawn from a population of K12 teachers whose district agreed to
participate in the study. At the time of the study, the participating school district was
implementing a district-wide STEM initiative. STEM pedagogy was encouraged in every
classroom across the district. Teachers were asked to implement STEM pedagogy whenever
possible in their classrooms. The study was conducted during the first year of this new initiative,
and at the time of the study, the district was planning to implement training to support the
initiative. There were 436 teachers (K12) in the district. Teachers were recruited through an
email that explained the study. Every K12 teacher in the participating district was invited to
participate, and 119 (27% response rate) teachers responded to the invitation and completed the
survey instruments.
Measures
Teaching Engineering Self-Efficacy Scale Modified
STEM self-efficacy was assessed with the Teaching Engineering Self-Efficacy Scale -
Modified (TESS-M). With exploratory and confirmatory factor analyses using structural
equation modeling, Yoon, Evans, and Strobel (2014) refined development of the TESS which
consists of 23 items in a four factor structure: (a) STEM content knowledge, (b) engagement, (c)
disciplinary (classroom management), and (d) outcome expectancy. Previous scores on the TESS
yielded Cronbach’s α between 0.89 and 0.96. Engineering pedagogical content knowledge
scores, which the authors define as the teacher’s belief in their pedagogical engineering
knowledge that is useful in teaching, had the highest internal consistency (α = 0.96). Engineering
44
engagement scores, or the teacher’s belief in their ability to engage students during engineering
instruction, had a Cronbach’s α = 0.93. The disciplinary subscale scores, which is related to a
teacher’s belief in his or her classroom management skills during lessons that include
engineering, had a Cronbach’s α = 0.92. The final subscale scores measured outcome
expectancy, or the beliefs a teacher holds about his or her ability to impact student learning of
engineering, and had a Cronbach’s α = 0.89. These subscales were created by Yoon, Evans, and
Strobel after examining the factors on five commonly used teacher self-efficacy scales. They also
found the scores on the instrument had a high overall reliability coefficient of 0.98. This
validation of the instrument was conducted with data from 434 teachers in 19 states. For this
study TESS was modified by replacing the word engineering with STEM. For example, “I can
discuss how engineering is connected to daily life” was changed to “I can discuss how STEM is
connected to daily life.” The instrument was validated with this substitution using exploratory
factor analysis. Like the original TESS, the TESS-M used a 6-point Likert-type response scale
(1= strongly disagree; 6 = strongly agree).
Demographic Questionnaire
Demographic survey items (see appendix) were used to collect data about each teacher
participating in the study. Teachers provided information about themselves and their teaching
experiences such as their gender, years of teaching experience, age, grade level taught
(elementary or secondary), subject taught, whether or not they teach gifted courses/students,
ethnicity, number of college-level science and math courses taken, and number of professional
development sessions attended over STEM topics. Participants also provided answers to
questions regarding perceptions of administrative support.
45
Analysis
Before running the factor analysis, descriptive statistics of the TESS-M items were
examined and are presented in Table 3. Although two items had a kurtosis a little higher than
three, this was within reasonable limits (Curran, West, and Finch, 1996). Exploratory factor
analysis was used to determine the number and nature of factors in the measured variables on the
TESS-M. Confirmatory factor analysis was not appropriate based on the sample size (n = 119)
(Curran, West, & Finch, 1996). In order to determine the number of factors to retain, parallel
analysis (using 95 %tile eigenvalues) and minimum average partial analysis were used (Henson
& Roberts, 2006; Zwick & Velicer, 1986) and both indicated a four factor solution. The scree
plot was also examined but, the results were less clear with a possible factor structure anywhere
from one to four. The researcher examined solutions with one, two, three, and four factors. In the
four factor solution, the factors are not so highly correlated that they are subsuming each other
(see Table 4). Ultimately, the four factor solution was retained because it was most interpretable
and mirrored the instrument’s theoretical underpinnings (Yoon et al., 2012).
Principal axis factoring (PAF) was chosen as it assumes variables are measured with
error, and the TESS-M is not perfectly reliable (Simpson, Rholes, & Nelligan, 1992). By
including only the variability of items that can be reliably measured and excluding variability
due to error, PAF provides more reliable results than methods such as principal component
analysis (Curran, West, & Finch, 1996). Also, PAF provides results that are more replicable
when less than 35 variables are analyzed (Velicer & Jackson, 1990), as is the case with the
TESS-M.
46
Table 3
Descriptive Statistics of TESS-M
Item Number Mean Standard Deviation
Skew Kurtosis
1 4.96 1.17 -1.64 3.03 2 4.90 1.15 -1.39 2.20 3 3.93 1.39 -.30 -.57 4 4.22 1.35 -.64 -.05 5 4.13 1.28 -.75 .28 6 4.25 1.26 -.72 .40 7 4.04 1.25 -.75 .24 8 4.46 1.23 -.92 .86 9 4.47 1.27 -1.05 .89 10 5.08 1.17 -1.69 3.16 11 4.96 1.10 -1.47 2.81 12 5.08 1.13 -1.63 2.98 13 4.94 1.11 -1.45 2.65 14 4.83 1.17 -1.23 1.58 15 4.65 1.24 -1.20 1.36 16 4.84 1.17 -1.36 2.16 17 4.90 1.12 -1.41 2.31 18 4.94 1.19 -1.52 2.56 19 4.55 1.18 -.94 1.03 20 4.36 1.13 -.65 .43 21 4.55 1.17 -.99 1.19 22 4.45 1.23 -1.10 1.15 23 4.68 1.19 -1.09 1.35
Results
Using SPSS Version 23, the correlation matrix was submitted to a principal axis factor
analysis using promax (Kappa = 4), an oblique rotation, because the factors were highly
correlated (see Table 4). An oblique rotation was also utilized by the original instrument
developers (Yoon et al., 2012). Although cross-loading is expected with items that are highly
correlated, two of the items (1 and 2) were closely weighted on two different factors. The
decision was made to retain these items, based on previous studies conducted with the
instrument. The items were retained on the first factor in congruence with the original
47
instrument. The other 21 items were clearly related to only one factor (see Table 5). These results
do not grossly violate the underlying theoretical structure that the original instrument developers
proposed (Yoon et al., 2012). The extracted factors explained 77.1% of the variance in the
original items. Henson and Roberts (2006) examined 60 articles that utilized EFAs and found
that extracted factors explained, on average, 52.03% of the total variance in the original
variables. According to MacCallum, Widaman, Zhang, and Hong (1999), the quality of the factor
analysis solution improves as communalities increase. These researchers suggest communalities
that are considered high are in the .6, .7, and .8 range. Present study results show all but one item
(22) were in this range of high communalities, suggesting a strong factor analysis solution.
Table 4
Factor Correlation Matrix
1 2 3 4 Pedagogical Content Knowledge (1) 1..00 0.67 0.67 0.67 Disciplinary (2) 0.67 1.00 0.58 0.57 Outcome Expectancy (3) 0.67 0.58 1.00 0.68 Engagement (4) 0.67 0.57 0.68 1.00
Table 5
Factor Pattern (Structure) Matrix Rotated to the Promax Criterion
Item Number Factor 1: Factor 2: Factor 3: Factor 4: h2
1 .465(.718) -.068(.449) -.154(.416) .565(.733) .65 2 .524(.795) -.091(.515) .029(.548) .459(.768) .73 3 .870(.806) .345(.577) -.023(.461) -.364(.424) .73 4 .870(.877) .095(.547) .092(.545) -.144(.555) .79 5 .906(.912) -.054(.489) .038(.517) .024(.617) .83 6 .786(.880) -.017(.519) -.004(.520) .159(.670) .79 7 .740(.817) -.086(.453) .049(.493) .148(.616) .68 8 .704(.848) -.078(.485) -.062(.486) .332(.712) .77 9 .599(.818) -.085(.509) .026(.540) .378(.739) .74 10 -.017(.608) .213(.696) .019(.628) .744(.882) .81 11 .067(.675) .203(.723) .024(.659) .723(.914) .87 12 .086(.676) .162(.690) -.018(.626) .764(.914) .86 13 .121(.676) .110(.677) .151(.684) .616(.864) .79 14 .029(.525) .889(.907) .000(.622) .004(.596) .82 15 -.033(.496) .889(.912) .049(.642) .013(.595) .83 16 -.019(.547) .801(.901) -.044(.628) .218(.694) .84
48
17 .028(.554) .912(.937) -.065(.623) .085(.649) .88 18 -.017(.572) .633(.864) .073(.682) .296(.739) .81 19 .091(.562) .146(.661) .601(.806) .087(.624) .69 20 -.017(.473) -.099(.555) .943(.889) .035(.561) .80 21 .035(.470) .114(.621) .892(.872) -.182(.482) .78 22 .038(.415) -.093(.449) .736(.715) .033(.467) .52 23 -.083(.519) .151(.687) .564(.817) .310(.710) .74
Total Eigenvalue 14.06 2.29 1.29 1.02 18.66 Trace 13.84 2.06 1.04 .81 17.75 % of variance 60.17 8.95 4.51 3.51 77.1 Note. Bolded score was retained for that factor in accordance with the factor to which Yoon et al, 2012 had them specified. Percentage variance and trace is postrotation. The eigenvalue for the fifth, unretained factor was .58. h2 = communality coefficient. Factor 1 is pedagogical content knowledge self-efficacy, factor 2 is STEM disciplinary self-efficacy, factor 3 is STEM outcome expectancy, and factor 4 is STEM engagement self-efficacy.
The four factors, or subscales, for the instrument were: STEM pedagogical content
knowledge self-efficacy (Factor 1; α = .95), STEM disciplinary self-efficacy (Factor 2; α = .96),
STEM outcome expectancy (Factor 3; α = .91), and STEM engagement self-efficacy (Factor 4; α
= .96). Cronbach’s alpha coefficients greater than .9 show excellent internal consistency on the
items in the instrument (George & Mallery, 2003). For each participant, the score for each of the
four subscales was calculated by summing the scores from the subscale items and then dividing
by the number of items on that subscale (mean score). These subscale scores were then used to
answer the research question and hypotheses. In order to check for multivariate normality, a
scattergram of the Mahalanobis distance and paired chi-square value was examined (Henson,
1999) and found tenable.
For each hypotheses, Thompson’s (1997) two-stage hierarchal decision process was used
to interpret the results. First, the researcher looks at the statistical significance tests, effect sizes,
and other replicability criteria to determine what, if anything, requires further interpretation.
Second, if the researcher decides there is indeed something worth interpreting, results are more
closely examined to determine where the effects originate within the data (using standardized
weights and structure coefficients).
49
Hypothesis 1
In order to examine group differences between males and females on the TESS-M
subscales, a descriptive discriminant analysis (DDA) was conducted. The independent variables
were the TESS-M subscales related to STEM self-efficacy: pedagogical content knowledge,
disciplinary, engagement, and outcome expectancy. DDA was utilized so that all predictor
variables could be entered in one step, thus reducing experimentwise (Type I) error and to
provide a more parsimonious explanation of the data. Descriptive statistics were examined (see
Table 6). Two participants did not give their gender, so the total participants for this analysis
were 117. Log determinants were also examined (male = -3.4; female = -1.9) and found tenable.
Box’s M test was conducted to test for homogeneity of variance across each group. This
assumption was met (Box’s M = .41). There was one discriminant function (k-1) created and it
was found statistically significant (p < .01). The Wilks’ Lambda, chi-square distribution and
effect size of the function can be found in Table 7. The eigenvalue (.133) and canonical
correlation (.342) for the function indicate a reasonable amount of variance explained by gender
(Rc2 = .117). Because the function was both statistically significant (p < .01) and had a moderate
effect size (11.7%), further interpretation was deemed necessary (Thompson, 1997). The null
hypothesis was rejected. Squared structure coefficients, standardized coefficients (see Table 8),
and group centroids (Table 9) were examined in order to better understand which variables are
contributing most to the variance explained by gender differences (Courville & Thompson, 2001;
Kraha, Turner, Nimon, Zientek, & Henson, 2012; Sherry, 2006).
50
Table 6
Descriptive Statistics from DDA Analysis
mean Standard deviation Subscale 1 2 3 4 1 2 3 4 n
Gender Male 4.55 4.52 4.16 4.84 1.05 1.06 .91 1.14 26 Female 4.33 4.93 4.62 5.07 1.10 1.10 1.03 1.05 91
Gifted Status
Teacher of Gifted
4.47 4.79 4.54 4.92 1.11 1.17 .91 1.11 42
Not Gifted Teacher
4.32 4.86 4.51 5.06 1.07 1.06 1.07 1.04 77
Group Taught
Elementary 4.42 5.11 4.81 5.20 .99 .78 .76 .74 55 Secondary 4.36 4.60 4.26 4.87 1.17 1.28 1.15 1.28 62
Note. Subscale 1 is pedagogical content knowledge self-efficacy, subscale 2 is STEM disciplinary self-efficacy, subscale 3 is STEM outcome expectancy self-efficacy, and subscale 4 is STEM engagement self-efficacy.
Table 7
Wilks’ Lambda Table from DDA Results
Function Wilks’ Lambda Χ2 df p Rc Rc2
1 (Gender) .883 14.099 4 .007 .342 .117 1 (Gifted/Not) .963 4.312 4 .365 .192 .037 1 (Elem/Sec) .879 14.537 4 .006 .347 .121
Table 8
Standardized Discriminant Function and Structure Coefficients for Interpreted DDA Results
Scale Coefficient rs rs2
Function – Gender PC -1.327 -.225 .05 D .416 .426 .18
OE .797 .525 .28 E .414 .256 .07
Function – Grade Level Taught
PC -.898 .072 .01 D .524 .639 .41
OE .912 .763 .58 E .081 .421 .18
Note. PC = Pedagogical Content Self-Efficacy related to STEM; D = Disciplinary Self-Efficacy related to STEM; OE = Outcome Expectancy Self-Efficacy related to STEM; E = Engagement Self-Efficacy related to STEM
51
Table 9
Group Centroids for Interpreted DDA Results
Group Function Gender Male -.676 Female .193 Grade Level Taught Elementary .390 Secondary -.346
Although females did have a higher group centroid (.193) than males (-.676), males
showed higher scores on the STEM pedagogical content knowledge self-efficacy subscale
(coefficient = -1.327; rs2 = -.225) than females. The negative signs on this variable’s results
indicated males were contributing more to the synthetic variable created in the equation, and had
higher scores on the pedagogical content subscale. Based on the low squared structure coefficient
and high coefficient, this variable also seemed to be acting as a suppressor to the other variables
(Pandey & Elliott, 2010). Suppressor variables remove irrelevant variance from one or more of
the other variables which increases the coefficient of the suppressed variable and improves the
overall model (Pandey & Elliott, 2010). Outcome expectancy STEM self-efficacy was also an
important variable in the model explaining the second highest amount of variance between
genders (coefficient = .797; rs2 = .28). The Cohen’s d effect size was calculated for the
pedagogical content knowledge subscale and found to be small (d = .20) (Cohen, 1992).
Hypothesis 2
A second DDA with the same independent variables (STEM self-efficacy subscales) was
conducted but the two groups were elementary (K-5) and secondary (6-12) teachers (n = 117).
Descriptive statistics can be found in Table 4 and the log determinants were -3.07 and -1.89.
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Box’s M test was significant (.001) indicating a lack of homogeneity of variance across the
groups. However, this test is very sensitive (Anderson, 2006) and because the log determinants
were not drastically different, the results of the DDA were still interpreted. The one discriminant
function created was found statistically significant (p < .01) and the effect size (1- Wilks’ λ) was
.121 meaning group identification (elementary or secondary) accounted for 12.1% of the
variance. Table 5 shows the results of Wilks’ λ, Χ2, canonical correlation, and effect size (Rc2).
Further interpretation was deemed appropriate to determine which variables were contributing to
the effect observed. The hypothesis was partially supported.
Standardized discriminant function coefficients and structure coefficients were examined
and can be found in Table 8. Elementary teachers had a higher group centroid (.39) than
secondary teachers (-.346). The STEM outcome expectancy self-efficacy subscale was making
the biggest contribution (coefficient = .912; rs2 = .58) and the disciplinary subscale was the
second biggest (coefficient = .524; rs2 = .41). Engagement was not given much credit in the
coefficient (.081), but could actually have more weight (rs2 = .18) than was being explained by
multicollinearity between it and one or more of the other subscales. The pedagogical content
knowledge subscale appeared to be acting as a suppressor (coefficient = -.898; rs2 = .01). The
hypothesis that elementary teachers would report higher scores on the disciplinary and
engagement subscales was partially supported in this study. The disciplinary subscale was
correct, but engagement did not make as much of a contribution to the effect. Outcome
expectancy however, contributed more than expected to the effect. The Cohen’s d effect size for
the engagement subscale was small (d = .17) (Cohen, 1992) and for the disciplinary subscale was
medium (d = .49) (Cohen, 1992).
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Hypothesis 3
A third DDA with the same independent variables (STEM self-efficacy subscales) was
conducted, but the two groups were teacher of the gifted and teachers not teaching gifted
students (total n = 119). Teachers of the gifted were defined as teachers that taught within the
gifted education program in any capacity. For example, in elementary school (Grades K-5) these
teachers might have been the gifted specialist on campus or a homeroom gifted cluster teacher
(identified gifted students were placed in groups of 4 to 7 in homeroom classes). For secondary
gifted teachers (Grades 6-12), they might have taught advanced placement, pre-advanced
placemnt or honors classes (students are asked to self-select into these advanced courses based
on prior academic performance). Descriptive statistics can be found in Table 6 and the log
determinants were -2.49 and -2.04. Box’s M test was not significant (.634) indicating
homogeneity of variance across the groups. The one discriminant function created was not
statistically significant (p < .365) and the effect size (1- Wilks’ λ) was .037 meaning group
identification (teacher of gifted or not) accounted for only 3.7% of the variance. Table 7 shows
the results of Wilks’ λ, Χ2, canonical correlation and effect size (Rc2). Further interpretation of
results was not conducted, as the difference between the two groups explains so little variance.
For Hypotheses 4 through 8, canonical correlation analyses (CCAs) were conducted
separately for each hypothesis. Each time the dependent (criterion) variables were the four
subscales from TESS-M (pedagogical content knowledge, disciplinary, engagement, and
outcome expectancy).
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Hypothesis 4
Hypothesis 4 stated that age would be positively correlated with all factors of STEM self-
efficacy. With age entered as the predictor of the TESS-M subscales, one function was created
with a squared canonical correlation (Rc2) of .016. The model was not statistically significant,
F(4, 112) = .456, p = .768. Using Thompson’s (1997) two stage decision making process, no
further interpretation was made due to lack of variance explained by age.
Hypothesis 5
Hypothesis 5 stated that as the length of teaching experience increased, so would STEM
self-efficacy on the disciplinary subscale. Years of teaching experience was entered as the
predictor of the TESS-M subscale variables, and one function was created with a squared
canonical correlation (Rc2) of .053. The model was not statistically significant, F(4, 112) = 1.554,
p = .192. With only 5.3% of the variance explained by years of teaching experience, the decision
was made to not interpret the results further.
Hypothesis 6
Hypothesis 6 stated that the number of college-level math and science courses taken
would be positively correlated to the teacher’s STEM self-efficacy (on all subscales). To test
this, the total number of college-level math and science courses taken was entered as the
predictor variable and yielded one function. The squared canonical correlation (Rc2) was .072.
The model was not statistically significant, F(4, 112) = 2.184, p = .075. Lack of statistical
significance and variance explained resulted in no further interpretation of results.
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Hypothesis 7
Hypothesis 7 stated that a teacher’s STEM self-efficacy would be related to his or her
perceptions of administrative support. In order to examine this phenomenon, each teacher’s
answers, on the same 6-point Likert scale as the TESS-M, to these three questions were summed:
• “The administration provides the support necessary for me to be successful in the STEM learning initiative.”
• “The administration provides the resources necessary for me to be successful in the STEM learning initiative.”
• “The administration provides the professional training opportunities necessary for me to be successful in the STEM learning initiative.”
The sum of each teacher’s answers was entered as the predictor variable and yielded one
function. The squared canonical correlation (Rc2) was .255. The model was found statistically
significant, F(4, 112) = 9.601, p < .01. Further interpretation of results was deemed necessary,
based on both statistical significance and a small effect size (25.5% of variance explained)
(Cohen, 1992). The null hypothesis was rejected.
Standardized canonical function coefficients, structure coefficients and squared structure
coefficients for the function can be found in Table 10. The most relevant criterion variable
appears to be pedagogical content knowledge (coef = .302; rs2 = .9) followed by engagement
(coef = .243; rs2 = .85).
Table 10 Canonical Solution for Administrative Support and Professional Development Predicting Teacher STEM Self-Efficacy
Variable Coefficient rs rs2 (%)
Function –Admin Support
PC .302 .95 90.25 D -.020 .72 51.84
OE .012 .70 49.0 E .243 .92 84.64
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Function – Prof. Development
PC .364 .61 37.21 D -.025 .15 2.25
OE -.487 -.25 6.25 E .226 .28 7.84
Note. PC = Pedagogical Content Self-Efficacy related to STEM; D = Disciplinary Self-Efficacy related to STEM; OE = Outcome Expectancy Self-Efficacy related to STEM; E = Engagement Self-Efficacy related to STEM
Hypothesis 8
Hypothesis 8 stated that teacher STEM self-efficacy would be related to the number of
STEM professional development sessions a teacher had attended. Only the teachers who had
attended at least one session were included in this analysis (n = 54). In testing this hypothesis the
total number of half-day sessions on STEM related topics a teacher had attended was entered
into the CCA as the predictor variable. The squared canonical correlation (Rc2) was .174. The
model was found statistically significant, F(4, 50) = 2.635, p = .045. Further interpretation of
results was deemed necessary, based on both statistical significance and a small effect size
(17.4% of variance explained) (Cohen, 1992). The null hypothesis was rejected.
After examining the standardized canonical function coefficients, structure coefficients
and squared structure coefficients (see Table 10) for the function, pedagogical content
knowledge appears to be the most relevant criterion variable (coef = .364; rs2 = .37) and
engagement was also pertinent (coef = .226; rs2 = .06).
Discussion
The purpose of this study was to examine teacher STEM self-efficacy. As school districts
work to develop STEM talent, administrators need to determine how best to support teacher
confidence in this unique learning environment.
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Data revealed that some teacher demographics were more predictive of STEM self-
efficacy than others. Gender seems to play an important role. The findings suggest that although
female teachers feel an overall higher confidence with STEM pedagogy, male teachers are more
comfortable with the pedagogical content knowledge. The results represent a multifaceted
understanding of the relationship between gender and self-efficacy toward STEM pedagogy.
This is consistent with the research conducted by Atta, Ahmad, Ahmed, and Ali (2012) where
women were found to have an overall higher teacher self-efficacy than men. Results indicated
that women had, on average, higher STEM self-efficacy scores on disciplinary, outcome
expectancy, and engagement. One reason men may feel more confident in the pedagogical
content is they tend to be more comfortable with the engineering design process (Smith,
Rayyfield, & McKim, 2015). Engineering has also historically been a male dominated field
(NSB, 2010). District support that increases teacher confidence in pedagogical content may
improve teachers’ ability to develop STEM talent.
Regarding the grade level taught, these results were similar to those in a previous study.
Kassen and Chiu’s (2010) study also found elementary teachers had higher levels of self-efficacy
in the areas of classroom management and student engagement. Perhaps this is due to the
relationship elementary teachers are able to build with their students since they generally have
them for longer periods of time. This subtle difference between elementary and secondary
teachers might inform school district implementation of STEM initiatives.
Teachers’ confidence with STEM pedagogy appears related to the number of STEM
professional development sessions they have attended and to their feelings of administrative
support. Additional research also supports the importance of administrative support (Bruce-
Davis et al., 2014; McMullin & Reeve, 2014) and opportunities for in-service instruction
58
(Lesseig, Slavit, Nelson, & Seidel, 2016; Nadelson & Seifert, 2013) on successful STEM
initiatives. With multiple studies similarly supporting the value of professional learning and
administrative support, preparing teachers in advanced as well as during implementation of a
STEM initiative ought to be a prominent recommendation.
The results associated with whether a teacher was assigned to the gifted education
program and number of college-level math/science courses taken were somewhat surprising and
may warrant further study. It is possible that by utilizing this unique pedagogy with all students
the differences usually found between gifted education teachers and general education teachers
were negated. It is also possible that it is less important whom teachers are teaching and more
about the training they have received. Professional learning while in-service may be more
important than college-level courses or at least level the playing field for those that did not take
many math and/or science courses.
These findings can be used to inform future school-wide STEM initiatives. Teachers with
more training in this study were more confident implementing STEM pedagogy. School districts
need to establish professional learning opportunities specific to STEM integration. Providing this
type of training might also help teachers feel supported by their administration. Professional
development could be offered in areas teachers feel least confident. Perhaps allowing teacher
choice in their selection of professional development would target the specific skills they feel are
weakest. Also, school administrators need to examine the support systems in place for their
teachers when beginning STEM initiatives. Even providing a forum for teachers to voice their
concerns and recommendations might be appropriate and improve their ability to develop STEM
talent (Bruce-Davis et al., 2014).
59
Providing teacher reinforcement such as professional development and administrative
support may help teachers feel more confident, which may also lead to higher student academic
achievement (Tschannen-Moran & Woolfolk Hoy, 2001). A student’s resources, necessary to
develop STEM talent, are likely enhanced by their teachers’ confidence in their ability to
implement STEM pedagogy. Subsequent studies out to further explore the relationship between
STEM self-efficacy and administrative support. The three items used to measure administrative
support could be expanded to better understand the nuances of this relationship.
Limitations and Future Research
A few of the limitations in the study may have implications on future studies. One
important limitation of the study was the recruited teachers were all from the same school
district. While this does give a good snapshot of the specific district investigated, results cannot
be generalized to the population without further studies. Future researchers can replicate the
model in this article with other groups of teachers. Replication is necessary to establish any type
of generalizability (Makel & Plucker, 2014).
Another limitation was the limit on responses created by the instrument (TESS-M).
Participants may have additional information they would like to share, but the instrument limited
these responses. Using a wider range of collection techniques or instruments would provide more
insight into teacher perspectives. The TESS-M might be a valuable quantitative measure in a
sequential mixed-method design to gather more detailed understanding of teachers’ perceptions
and confidence with innovative STEM pedagogies.
An additional limitation is that of possible selection bias. All of the mean scores on the
TESS-M were above the mean and every item had a negative skew (see Table 3) suggesting that
60
the group of respondents largely demonstrated high self-efficacy. It is possible those teachers
with low self-efficacy did not respond to the invitation to participate in the study.
Conducting longitudinal research of teachers’ STEM self-efficacy, using the TESS-M in
conjunction with open-ended questions, as they participate in ongoing professional development
would be beneficial to understanding the change such development invokes. Additionally,
further study is needed into the effectiveness of STEM professional development in increasing
teacher self-efficacy and differences in student achievement. In other words, we need to
determine which professional developments best improve a teacher’s ability to develop STEM
talent.
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Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological bulletin, 99, 432-455. Retrieved from http://files.eric.ed.gov/fulltext/ED251510.pdf
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APPENDIX
DEMOGRAPHIC SURVEY
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I. Personal Characteristics 1. Male Female
2. What is your age? ____________
3. What subject(s) do you teach? ______________________
4. What grade do you teach? K-5 6-12
5. If you are an elementary school teacher (K-5), what is the subject that you feel the most confident in teaching?
Reading/Language Arts Mathematics Science Social Studies Fine Arts Not elementary
6. If you are a secondary school teacher, in which subject area are you teaching?
Mathematics Science Technology Engineering Vocational Agriculture English/LA
Social Studies Fine Arts Business, Marketing, Information Technology Health Physical Ed.
World Languages Special Education Other
7. Are you teaching any of the following classes? Check all that apply.
Gifted Class Accelerated Course Honors Course Gifted Cluster in my Homeroom
None of the listed courses
8. For how many years have you been a teacher? ________
9. Please indicate your ethnic background by marking the answer that applies to you:
a. Black/African-American b. Latino/Other Hispanic-
American
c. White/Caucasian (&
Middle Eastern)
d. Other
10. How many college level math and science classes have you taken? _______________
11. Have you ever attended a STEM professional development or teacher workshop over STEM?
______No ______Yes
If you have, how many half day workshops have you attended? ___________
12. Have you ever attended a Project-Based Learning (PBL) professional development or a teacher workshop over
PBL?
______ No ______ Yes
Indicate the degree to which you agree with these statements.
1 2 3 4 5 6
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13. The administration provides the support necessary for me to be successful in the STEM learning initiative.
14. The administration provides the resources necessary for me to be successful in the STEM learning initiative.
15. The administration provides the professional training opportunities necessary for me to be successful in the STEM learning initiative.
1 = Strongly Disagree 2 = Moderately Disagree 3 = Disagree slightly more than agree 4 = Agree slightly more than disagree 5 = Moderately Agree 6 = Strongly Agree
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