Professional Competence of Prospective Teachers in Business and Economics Education: Evaluation of a Competence Model Using Structural Equation Modeling

Abstract

Teacher competence is crucial for quality of teaching and learner achievement. Competency models and competence measurement are prevalent in domains such as the natural sciences and lacking in others. We conducted our research in the field of business and economics education by focusing on the accounting domain because it is key to a deep understanding of the economic context and the development of economics competence. To teach well, teachers require professional knowledge, which is mainly composed of content knowledge (CK), pedagogical content knowledge (PCK), and pedagogical knowledge (PK). Our competence model comprises the cognitive component of professional knowledge and the noncognitive components of beliefs, self-efficacy, and self-regulation. To measure competence in competence of prospective teachers, we employed novel instruments to test for professional knowledge and beliefs as well as established ones to test for self-regulation and self-efficacy. The sample consists of 1,152 students at 24 German universities. The structure of the competence model was tested. Results suggest that professional competence in accounting has at least four distinct dimensions (CK, PCK, beliefs, and self-regulation aspects).

Notes

Besides these categories, Shulman (1987) also cites curriculum knowledge, knowledge of educational ends, knowledge of educational contexts, and knowledge of learners and their characteristics.

All 28 universities in Germany offering a degree program in business and economics education were contacted, with 24 agreeing to participate. Because participation for students was voluntary, we drew a convenience sample.

Concerning X², the criterion for acceptance varies ranging from less than 2 (Ullman, 2001) to less than 5 (Schumacker & Lomax, 2004). We decided to use a rather stringent cutoff criterion of less than 2.5. According to MacCallum, Browne, & Sugawara (1996), RMSEA ≤.05 indicate a good model fit. Following Hu and Bentler (1999), SRMR ≤.08 and CFI of ≤.95 show a good model fit.

Figure 2 Structural equation model with all correlations between the constructs (Model 2). Note. PCK1: knowledge of students’ cognition and typical student errors, PCK2: knowledge of multiple representations and explanations, and PCK3: knowledge of tasks as an instructional tool.