Early childhood teachers' beliefs about readiness for

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research-article2016

ECR0010.1177/1476718X15614040Journal of Early Childhood ResearchPark et al.

Article

Early childhood teachers’ beliefs about readiness for teaching science, technology, engineering, and mathematics

Journal of Early Childhood Research 2017, Vol. 15(3) 275–291 © The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav https://doi.org/10.1177/1476718X15614040 DOI: 10.1177/1476718X15614040 journals.sagepub.com/home/ecr

Mi-Hwa Park

Murray State University, USA

Dimiter M Dimitrov George Mason University, USA

Lynn G Patterson Murray State University, USA

Do-Yong Park

Illinois State University, USA

Abstract The purpose of this study was to examine beliefs of early childhood teachers about their readiness for teaching science, technology, engineering, and mathematics, with a focus on testing for heterogeneity of such beliefs and differential effects of teacher-related factors. The results from latent class analysis of survey data revealed two latent classes of teachers, not known a priori, with significant differences in levels of teachers’ beliefs about readiness to teach science, technology, engineering, and mathematics. The teachers’ teaching experience and their awareness of the importance of science, technology, engineering, and mathematics and potential challenges in teaching science, technology, engineering, and mathematics played a differential role in the classification of teachers into latent classes. In addition, the analysis of two open-ended survey questions revealed several themes in the early childhood teachers’ opinions about early childhood science, technology, engineering, and mathematics education. Study findings support the necessity for professional development practices that will enhance teachers’ understanding of the importance of early childhood science, technology, engineering, and mathematics education, as well as their knowledge of science, technology, engineering, and mathematics disciplines and potential challenges of teaching science, technology, engineering, and mathematics.

Keywords early childhood education, latent class analysis, STEM education, teacher beliefs Corresponding author: Mi-Hwa Park, Department of Early Childhood and Elementary Education, Murray State University, 3240 Alexander Hall, Murray, KY 42071, USA. Email: [email protected]

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Introduction Science, technology, engineering, and mathematics (STEM) education is an integrated approach that teaches technology and engineering based on science and mathematics in kindergarten through 12th grades (Bybee, 2010). Although the context of STEM education is commonly described as ranging from kindergarten to 12th grade, research on STEM education has generally put emphasis on upper elementary and secondary education settings (e.g. Merrill and Daugherty, 2010; Moorehead and Grillo, 2013). As a result, limited attention has been paid to teaching STEM in early childhood education settings. However, the National Research Council (2011) has clearly emphasized the need to include kindergarten to third grade in advancing STEM education and specifically suggested several goals for successful K–12 STEM education. The first goal is to produce advanced students pursuing STEM field careers and to increase the number of women and minority students who are involved in STEM areas. One driver of this goal in the United States is the imbalance of academic achievement among low socioeconomic status and minority students, including Hispanics and African Americans, which creates a political burden (Wyner et al., 2008). The increase in STEM workforces within these groups would assist in constructing a stronger society. The second goal of STEM education is to produce more STEM experts. Modern society requires more STEM-trained workers who can help resolve complex problems in everyday life, and a nation’s STEM workforce is an indication of its global strength and competitiveness. Currently, the demand for such workers surpasses the supply in the United States. The third goal is to increase STEM literacy of students at all grade levels. STEM literacy is an important goal for all students, regardless of their majors, because STEM literacy is needed for individual decision-making, cultural advancement, and economic productivity. It applies to all students at all levels. Therefore, educational institutions must strive to produce STEM-literate citizens who can think critically and creatively to solve STEM-related complex problems. A growing body of research has indicated that early STEM experiences (defined as preschool to third grade) play an important role in enhancing children’s knowledge, skills, and dispositions needed for the jobs of the future and preparing students for an economy that demands innovative solutions to complex problems (see Aronin and Floyd, 2013; Chesloff, 2013; DeJarnette, 2012; New, 1999). For example, Chesloff (2013) argued that STEM education should start in early childhood since “concepts at the heart of STEM—curiosity, creativity, collaboration, critical thinking— are in demand” (p. 27). Although the growing necessity to start STEM education in preschool has been justified in research and policies, the idea of teaching STEM to young children (aged 3–8 years) still sounds remote to teachers and administrators in schools, and early childhood education seems to be labeled as a marginalized sector in STEM education (Parette et al., 2010). This mismatch of STEM education in early childhood has caused early childhood teachers to avoid teaching STEM and thereby fail to develop their confidence to teach subjects related to STEM education in classrooms (Brown, 2005; Fenty and Anderson, 2014; Timur, 2012). Teachers’ beliefs about teaching have been studied from various viewpoints, including how teachers’ beliefs influence (a) their instructional decision-making and practices (Nathan et al., 2010; Sherin, 2002), (b) their interpretation and actual classroom practices regarding what they have learned from training and professional development (Breffni, 2011; Hughes, 2005; Polly et al., 2014), and (c) their efforts and resistance level toward new practices and reforms (Feldon, 2007; Richardson, 2003). Vartuli (2005: 82) stressed the importance of analyzing teachers’ beliefs, arguing that “beliefs are the heart of teaching” and teachers’ beliefs are not merely hypothetical understandings but also guide their behaviors and decisions in classrooms.

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Although many researchers claim that teachers’ beliefs are difficult to change, they also argue that teachers’ beliefs are associated with teaching experience (Kagan, 1992; Pajares, 1997; Pendergast et al., 2011). This connection suggests the need for administrative efforts to improve teaching practices, support, and experiences in order to enhance teachers’ beliefs about their teaching (Kim et al., 2013). Specifically, teachers’ beliefs toward the subject matter being taught, subject matter knowledge, and teaching practices can be changed through supportive training or professional development (Breffni, 2011; Hughes, 2005; Maier et al., 2013; Polly et al., 2014). Teachers’ readiness is also viewed as “a significant predictor of change in practice” (Lang, 1992: 301). Teacher readiness for teaching has specific elements, including knowledge, attitudes, and interests that are critical components that directly contribute to the effectiveness of creating and implementing teaching methods (Jusoh, 2012; Lang, 1992). Lang (1992: 301) conducted a study on teacher readiness—which he defined as “teachers’ awareness of curricular intentions and their reactions indicated by interest, motivation, willingness, and attitudes and activated knowledge in a school context”—and found that teachers with an affirmative view of their knowledge, attitude, and interest toward using computers showed a high level of computer readiness (Lang, 1992).

Purpose of the study This study was motivated by (a) the shortage (if not total lack) of studies on early childhood teachers’ beliefs about their readiness for teaching STEM, and (b) the fact that all previous studies on teachers’ beliefs about teaching STEM assume that the study population of teachers is homogeneous in response profiles across items of the respective questionnaire. This, however, may not be the case because the targeted population of teachers may break into latent classes with different response profiles. Furthermore, relationships between teachers’ beliefs in their readiness for teaching STEM and other variables of interest may have differential effects across latent classes of respondents (if such classes exist). For example, given the strong arguments about the appropriateness of early childhood STEM education (e.g. Aronin and Floyd, 2013; Chesloff, 2013), the awareness of early childhood teachers about the importance of early childhood STEM education and the challenges they may encounter in teaching STEM can play a differential role in their beliefs regarding their readiness to teach STEM and its relationship with the teaching experience. Therefore, this study sought to examine the beliefs of early childhood teachers in terms of their readiness for teaching STEM, paying particular attention to (a) possible heterogeneity among teachers regarding their beliefs about readiness for teaching STEM; (b) the relationship between such beliefs and teacher experience, with possible differential effects attributed to teachers’ awareness of the importance of early childhood STEM education and challenges they may encounter in teaching STEM; and (c) the perceptions of early childhood teachers regarding issues and problems related to their beliefs about readiness for teaching STEM. To achieve this purpose, the following three research questions (RQs) were addressed: 1. Does the targeted teacher population break down into different latent classes, not known a priori, based on the response profiles across items in a survey on early childhood teachers’ beliefs about readiness for teaching STEM? 2. What is the relationship between early childhood teachers’ beliefs about readiness for teaching STEM and their teaching experience, with possible differential effects due to their awareness of the importance of early childhood STEM education and the difficulties they may encounter in teaching STEM?

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3. What issues and problems emerge from the opinions of early childhood teachers about the importance of early childhood STEM education and the difficulties that they may encounter in teaching STEM?

Methodology Participants The participants in this study were 830 early childhood teachers in preschool–third grade representing public elementary schools in rural areas located in Western Kentucky. The data consisted of the responses of the study participants on a survey of teachers’ beliefs regarding readiness for teaching STEM developed specifically for this study (see Appendix 1). The participants’ teaching grades included preschool (12.7%), kindergarten (29.3%), first grade (20.4%), second grade (18.8%), and third grade (18.8%). The participants’ gender breakdown was male (3.2%) and female (96.8%), while their teaching experience included 0–5 years (18%), 6–10 years (24.9%), 11–16 years (22.9%), and more than 17 years (34.2%). In addition, their reported educational attainment was as follows: less than 2 years of college (0.5%), associate’s degree (0.6%), bachelor’s degree (17%), and postgraduate degree (81.9%). In terms of the subject areas that participants were interested in and/or passionate about teaching, responses included science (21.1%), technology (9.2%), mathematics (53.3%), and none of these (16.4%). Although engineering is an integral part of STEM education, it was not included as a subject in this study because it has not yet been added to the curriculum in early education. Finally, the participants’ reported race breakdown was White (89.8%), Black African American (8.6%), Asian or Pacific Islander (0.5%), American Indian or Alaska Native (0.1%), and Latino or Hispanic (1.1%).

Procedures The data were collected through the online SurveyMonkey® system (see http://www.surveymonkey.com) from August to October 2013. The survey of teachers’ beliefs about readiness for teaching STEM was distributed to 3000 early childhood teachers who were teaching preschool to third grade. The emails of potential participants in the study were obtained through websites of public elementary schools in Western Kentucky. The participants and their responses were kept completely anonymous. Neither their email addresses nor their school mailing addresses were connected with the survey. The participants received an incentive to participate, as previous studies (e.g. Dillman et al., 1998) have indicated that incentives (such as gift cards) increase response rates significantly. The return rate of 27.67 percent (830 out of 3000) was relatively low, but the study sample was considered adequate for the exploratory purpose of this study. As described earlier, the study sample (N = 830) consisted predominantly of White (89.8%) and females (96.8%) in terms of race and gender, respectively. However, issues of potential return bias in the survey related to race and gender were ruled out, given that the demographic makeup of early childhood teachers in Kentucky is 96 percent White and 78 percent female (Kentucky Department of Education, 2013).

Measures The construct of interest in this study was beliefs in readiness for teaching STEM for the study population of early childhood teachers (preschool–third grade). The measures of this construct consisted of the responses of the participants on seven survey items that were designed to serve as

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indicators of the teachers’ beliefs about their readiness for teaching STEM. The survey was based on a review of empirical studies on teacher beliefs about readiness to teach in a specific context (e.g. Jusoh, 2012; Lee, 2005; Maier et al., 2013; Nathan et al., 2010). The survey items were scored on a 4-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = agree, and 4 = strongly agree). The “neutral” (middle) category of this scale was omitted to avoid measurement problems stemming from a central tendency bias, which occurs when respondents avoid using extreme categories (e.g. see Dimitrov, 2012: 10). The Cronbach’s coefficient alpha of internal consistency reliability of the scores was 0.894, which is an adequate level of measurement accuracy for the purpose of this study. In addition to the seven survey items on early education teachers’ beliefs about readiness for teaching STEM, three other variables were used in testing for differential effects: (a) level of teaching experience, scaled as 1 = from 0 to 5 years, 2 = from 6 to 10 years, 3 = from 11 to 16 years, and 4 = 17 or more years; (b) an open-ended question on awareness of the importance of early childhood STEM education, with the responses coded as 0 and 1 (0 = lack of awareness, 1 = awareness); and (c) an open-ended question on awareness of difficulties that the respondents may encounter in teaching STEM, with the responses coded as 0 and 1 (0 = lack of awareness, 1 = awareness).

Data analysis The first RQ (RQ1) was addressed through the use of a statistical method referred to as latent class analysis (LCA; e.g. Agresti, 2002; Dayton and Macready, 2002; Hagenaars and McCutcheon, 2002; Lazarsfeld and Henry, 1968; McCutcheon, 1987; Muthén, 2001). Using LCA allowed for the following information to be obtained: (a) the number of latent classes in which respondents fell depending on their survey responses; (b) the probability with which any respondent belongs to each of the latent classes, with the highest probability used to assign the respondent to a given latent class; (c) the count of respondents in each latent class; and (d) the expected mean score on each survey item across different latent classes of respondents. The LCA procedures were conducted through the use of the computer program Mplus (Muthén and Muthén, 2010). The second RQ (RQ2), related to the relationship between teachers’ beliefs about readiness to teach STEM and three teacher-related variables—teaching experience, awareness of the importance of early childhood STEM education, and awareness of difficulties in teaching STEM—was addressed by including these three variables as covariates in the LCA model. This allowed for the examination of differential effects of the covariates on the classification of early childhood teachers into latent classes based on their beliefs about readiness to teach STEM. The third RQ (RQ3), which focused on participants’ responses to the two open-ended survey questions, including (a) their awareness of the importance of STEM in early childhood education (357 teachers responded) and (b) the difficulties they may encounter in teaching STEM (319 teachers responded), was addressed through data analysis grounded in the constant comparative method (Strauss and Corbin, 1998). Three researchers read and reread the participants’ responses independently until a unit of data was identified. A unit of data refers to “any meaningful (or potentially meaningful) segment of data” (Merriam, 1998: 179). A unit of data in this study was defined as meaningful words and phrases that were related to the questions from the survey and recursively emerged from the data (e.g. categories or themes). Specifically, the responses of the first participant were carefully examined and then revisited by comparing them with the next participant’s responses; these comparisons were conducted until the last participant’s responses were examined. Following an approach suggested by Fowler (2014), the three researchers independently coded responses by classifying each response to only one category and that was cross-checked among the researchers. While examining the teachers’ responses, each researcher independently took notes

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and created a preliminary unit of data (e.g. categories or themes as they emerged from the responses). Through this process, the identified themes evolved with the accumulation of new responses (Merriam, 1998). Each researcher’s notes were then compared and contrasted to ensure whether they coded in the same way, and the researchers finalized the unit of data. When unclear coding rules were identified, the researchers discussed and clarified each other until they come to an agreement to secure “internal consistency” (Fowler, 2014: 132). These comparisons were conducted until the last participant’s responses were analyzed. After emergent themes were identified, the researchers reviewed the entire body of analysis to refine and confirm or refute their preliminary identification of themes. This was done with the purpose of enhancing the trustworthiness of the outcomes from the analyses on identification of themes (e.g. Fowler, 2014). Then, the researchers highlighted themes with different colors, wrote coding numbers next to the subthemes within participants’ responses, and developed a list to visually display overarching themes by combining similar categories that emerged from the analyzed responses.

Findings Results related to RQ1 A key step in LCA is to decide how many latent classes of respondents to retain (if such classes exist). In this study, the decision was based on three main statistics used in testing for the proper number of latent classes: the Akaike information criterion (AIC), the Bayesian information criterion adjusted for sample size (aBIC), and the Lo–Mendell–Rubin adjusted likelihood ratio test (aLRT). The testing for the number of latent classes starts with a single class model and gradually increases the number of latent classes until a decision about the proper number of classes is reached. Under the AIC and aBIC criteria, smaller values indicate better data fit. Under the aLRT, the decision about the proper number of latent classes is based on the p values associated with the test in the comparison of a model with (K − 1) classes versus a model with K classes. If the p value indicates significance for a model with (K − 1) classes (p 0.05), then the decision is to retain (K − 1) latent classes. In such a case, the difference in data fit between the models with (K − 1) and K classes is negligible, so the more parsimonious model with (K − 1) classes is preferred. The results of testing for the number of latent classes are summarized in Table 1. Based on the aforementioned criteria, the decision was to retain two latent classes. The LCA profiles of the mean scores across the survey items for the two latent classes are depicted in Figure 1. Latent class 1, with the lower-level beliefs about readiness for teaching STEM, contained 69.4 percent of the participants, whereas latent class 2, with the higher-level beliefs about readiness to teach STEM, contained 30.6 percent of the participants. The means and standard deviations of the scores on the survey items for the two latent classes are given in Table 2. The results from t-tests for the comparison of the two latent classes across the seven survey items showed that all item mean differences were statistically significant (p