Using The Theory Of Planned Behavior To Explore Teachers’ Intentions To Engage In Continuing Professional Development

Author(s):

Ryan Dunn(presenting / submitting)

Conference:

ECER 2015

Network:

01. Continuing Professional Development: Learning for Individuals, Leaders, and Organisations

Format:

Paper

Session Information

01 SES 02 B, Behaviour and Perceptions in Professional Development

Paper Session

Time:

2015-09-08

15:15-16:45

Room:

204.Oktatóterem [C]

Chair:

Jannet Doppenberg

Contribution

Topic/Objectives: With schools across England required to implement a new National Curriculum, it is timely explore how systems have approached this large-scale undertaking. The United States have been implementing the Common Core State Standards (CCSS) over the past 4 years, which has led to schools, districts and systems across the United States to intensify their focus the professional learning of teachers to support teachers to implement the CCSS with fidelity. As a consequence the focus has also turned to the effectiveness of professional learning for teachers.

The purpose of this study was to examine the factors influencing teachers’ intentions to engage in professional learning on the Common Core State Standards (CCSS) in mathematics. The aim was to identify specific factors teachers encounter during professional learning that may facilitate implementation. Ajzen’s (1991) theory of planned behavior was used to examine the underlying antecedent beliefs (factors) and the relative strengths of the three direct determinants (attitude, subjective norm and perceived behavioral control) of teachers’ intentions to utilize the professional learning (based on the Math CCSS) to inform classroom instruction. Data for this quantitative study was collected from 151 teachers in an urban school district in California via an online self-report survey. The teachers’ participated in a mathematics professional learning course that required participants’ to attend four full-day workshops, complimented by in classroom math coaching, throughout the 2013-2014 academic year. The questionnaire was completed at the conclusion of the second workshop.

The study addressed the following research questions:

1. Which of the three direct determinants of intention are statistically significant predictors of teachers’ intentions to use the professional learning their classrooms?

2. What are the potential enablers and barriers to the subsequent use of the professional learning?

3. Is the theory of planned behavior an appropriate conceptual framework for evaluating the participants’ intents to use the professional learning?

Theoretical Framework: The theory of planned behavior was used as a means to predict the intentions of teachers to actively engage in ongoing professional learning and modify teaching practices as a result of this process. Behavioral intention is the measure of likelihood that an individual will engage in a given behavior. That is, how hard will they try, and how much of an effort are they planning to exert in order to perform the behavior (Ajzen, 1991). As a general rule, the more favorable the attitude and subjective norm with respect to behavior, and the greater the perceived behavioral control, the stronger should be an individual’s intention to perform the behavior under consideration (Ajzen 1991).

The theory of planned behavior is a framework designed to predict and explain human behavior in specific contexts (Cheng, Lam, and Hsu 2005; Conner, Povey, Sparks, James & Shepherd, 2003; Shin 2004). According to the theory of planned behavior, the “main antecedent of an individual’s behavior is their intention toward the behavior; and in turn an individual’s behavioral intention is determined by three constructs” (Greaves, Zibarras & Stride, 2013, p.110). As Demir (2010) outlined, the theory of planned behavior has been broadly supported in empirical literature in different fields (Chiou 2000; King & Dennis 2003; Cheng, Lam, & Hsu 2005; Fusilier & Durlabhji 2005; Francis et al. 2004). Meta-analyses also indicate that the theory of planned behavior provides an explanation for a wide range of behaviors (Ajzen 1991; Armitage & Conner 2001; Cooke & Sheeran 2004; Godin & Kok 1996; Sheeran & Taylor 1999). Applying the theory of planned behavior to this research context suggests that a teacher’s behavioral intention (modifying practice as a result of professional learning) is jointly determined by his/her; attitude, subjective norm, and perceived behavioral control.

Method

The theory of planned behavior survey was used to ascertain the participants’ intention to use the teaching practices and ideas explored during the mathematics professional learning. The data was analysed according to the method described by Ajzen and Fishbein (1980) and subsequently used by other educational researchers (Crawley, 1990; Demir, 2010; Haney, Czerniak, & Lumpe, 1996; Smith, 1993). The questionnaire was developed based on Demir (2010), Patterson (2001), Venkatesh et al. (2003) and Francis et al. (2004) frameworks. Demir (2010) used the theory of planned behavior to predict teachers internet use for the professional development of teachers and reported an average reliability for all constructs at 0.87 using Cronbach’s Alpha. The questionnaire is comprised of 12 items based on four constructs; attitude toward behavior (AB), subjective norm (SN), perceived behavioral control (PBC), and behavioral intention (BI). Each construct will measured using a 5 point Likert scale. Based on Demir’s (2010) methodology a Confirmatory Factor Analysis (CFA) was conducted to determine whether the designed item-factor structure (i.e. the proposed grouping of items to measure 12 underlying factors: attitudes, subjective norms, perceived behavioral control and behavioral intentions) did indeed comprise an adequate measurement model for the items (Greaves, Zibarras & Stride, 2013). The model fit indices indicated a reasonably good fit of item responses to the designed measurement model: specifically X^2(48) = 113.71. As the chi-square statistic is affected by the sample size, the relative fit of a proposed model can be assessed by using the ratio of chi-square to degrees of freedom (Demir, 2010). The model was assessed using other common fit indexes: normed fit index (NFI), root mean square error of approximation (RMSEA), and the goodness of fit index (GFI). For measurement models to have sufficiently good model fit, the X^2 value normalized by degrees of freedom (X^2/df) should not exceed 3 (Kline 1998), GFI should exceed .8 (Doll, Xia, and Torkzadeh 1994), NFI should exceed .9 (Hu and Bentler 1999), and RMSEA should not exceed .08 (Browne and Cudeck 1993). The fit indices for the measurement model indicated a reasonably good fit between the model and the data (X^2/df = 2.37; NFI = .90; RMSEA = .09; GFI = .90;). To establish reliability of the four factors Cronbach’s alpha was used. Based on the data collected, all constructs exhibited an α-value greater than .7, a common threshold for exploratory research (Nunnally & Bernstein 1994).

Expected Outcomes

The results of the study indicated that there was a strong link between the antecedent beliefs and behavioral intention. Intention was predicted significantly by perceived behavioral control, subjective norm and attitude. Perceived behavioral control was the most powerful predictor. Demir (2010) suggested the increased importance perceived behavioral control may have with behaviors that require skills and resources. This is consistent with the finding of this study where perceived behavioral control was the most significant predictor of intention and the nature of the teacher professional learning was focused on the required instructional shifts (the skill of teaching) and operationalising the CCSS math curriculum (resources, materials, time to collaborate, etc.). The control factors suggest the importance of resources and time for teachers to collaborate when implementing the CCSS. As with Demir (2010), perceived expectations by principals, colleagues, and students were used to establish the subjective norm. As Taylor and Dodd (1995) highlighted, because innovation can create uncertainty, the relative influence of subjective norm on intentions is expected to be stronger for people with no prior experience as they rely on the reactions of others in forming their opinions. The results for subjective norm may highlight the importance of people who are important to teachers play in the initial stages of implementation and influencing teachers’ normative beliefs. Attitude toward the behavior, while significant, was least powerful predictor of the three direct measures. The descriptive statistics indicate that while teachers have a generally favorable evaluation of the behavior, their attitude is not the most powerful predictor in their decision to engage in the professional learning.

References

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes 50: 179–211. Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall, Inc. Cheng, S., T. Lam, and C.H.C. Hsu. (2005). Testing the sufficiency of the theory of planned behaviour: A case of customer dissatisfaction responses in restaurants. Hospitality Management, 24, 475–92. Chiou, J. (2000). Antecedents and moderators of behavioural intention: Differences between US and Taiwanese students. Genetic, Social, and General Psychology Monographs 126(1),105–24. Conner, M., Povey, R., Sparks, P., James, R., & Shepherd, R. (2003). Moderating role of attitude ambivalence within the TPB. British Journal of Social Psychology, 42(1), 75-94. Crawley, F. E. (1990). Intentions of science teachers to use investigative teaching methods: a test of the theory of planned behavior. Journal of Research in Science Teaching 27:685–697 Demir, K. (2010). Predictors of internet use for the professional development of teachers: an application of the theory of planned behaviour, Teacher Development, 14(1), 1–14. Francis, J., Eccles, M, P., Johnston, M., Walker, A., Grimshaw, J., Foy, R., Kaner, E., Smith, L., and Bonetti, D. (2004). Theory of Planned Behaviour Questionnaires: Manual for Researchers. Newcastle upon Tyne, UK: Centre for Health Services Research, Univeristy of Newcastle, 2004. Greaves, M., Zibarras, L. D. & Stride, C. (2013). Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. Journal of Environmental Psychology, 34, 109 - 120. Haney, J., Czerniak, C. M., & Lumpe, A. T. (1996). Teacher beliefs and intentions regarding the implementation of science education reform strands. Journal of Research in Science Teaching, 33(9), 971-993. Patterson, R. (2001). Using the Theory of Planned Behavior as a Framework for the Evaluation of a Professional Development Workshop. Microbiology Education, p.34-41. Shin, N. (2004). Strategies for generating e-business returns on investment. Hershey, PA: Idea Group Publishing. Smith, B. G. (1993). Teacher intentions to use and their actual use of microcomputer science laboratory interface materials in science instruction: an application of the theory of planned behavior. Ph.D. dissertation. The Pennsylvania State University, University Park. Taylor, S., and P. Todd. (1995). Assessing IT usage: The role of prior experience. Management Information Systems Quarterly 19( 4) 561–70. Venkatesh, V., M.G. Morris, G.B. Davis, and F.D. Davis. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–78.

Author Information

Ryan Dunn (presenting / submitting)

Melbourne University

Fitzroy North

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