Students experience different emotions in educational environments, which compels individuals to examine the processes behind emotion generation. The control-value theory of achievement emotions focuses on the emotions resulting from achievement outcomes and those experienced in learning environments (Pekrun, 2006, 2018, 2024). This theory brought a broad perspective in explaining how students’ academic feelings relate to several cognitive, affective, motivational, and behavioral factors. Control-value theory subsumes a dynamic relationship between potential antecedents and consequences of students’ academic emotions (Pekrun, 2006, 2018, 2024). Accordingly, environmental, and cognitive appraisals induce emotions, and the resulting emotions directly or indirectly shape students’ cognitive resources, motivation to learn, learning strategies, self-regulation of learning, and academic performances.

Among the antecedents, the social environment, particularly the cognitive and motivational quality of teaching, significantly influences control and value appraisals, thereby enhancing the arousal of emotions (Pekrun, 2006, 2018, 2024). The structure, clarity, and difficulty level of instruction, along with teacher enthusiasm and attentiveness to students’ interests and feelings, the use of positive verbal and non-verbal language, as well as genuine sincerity are essential factors that significantly impact students’ perceived control and the value they attribute to the subject matter. For example, as a control appraisal, self-efficacy would inevitably be influenced by those teaching quality elements. If the cognitive and motivational quality of the instruction mismatch with students’ competencies, student self-efficacy might diminish correspondingly. Therefore, fluctuations in students’ control appraisals resulting from problems in teaching quality would negatively influence their feelings in the intended subject domain (e.g., Goetz et al., 2013, 2020). Besides corresponding to the dynamic nature of the theory, the research has also supported the direct impact of teaching quality on students’ achievement emotions (e.g., Becker et al., 2014; Bieg et al., 2017; Goetz et al., 2013, 2020; Heckel & Ringeisen, 2019; Lazarides & Buchholzb, 2019; Liu et al., 2018; Luo et al., 2016; Sakiz, 2017).

That interwoven structure becomes apparent when considering sources of self-efficacy. Among the four primary sources of self-efficacy (Bandura, 1997), students’ physiological or affective arousal in stress, distress, and anxiety would reduce their capability judgments in accomplishing the designated tasks (Usher & Pajares, 2008; Pajares, 2006). The reduced self-efficacy might also result in negative emotions, indicating a vicious cycle of students’ academic performances and well-being. Therefore, inquiring about the structure of the antecedent-appraisal-emotion linkage would be meaningful in figuring out the emotion formation process through a domain-specific lens, as emotions and self-efficacy are domain-specific constructs (Bandura, 1997; Pekrun, 2006). This study addresses the possible association between teaching quality as combining two environmental antecedents (cognitive and motivational quality of instruction), self-efficacy as a control appraisal, and achievement emotions of middle school students in mathematics. Accordingly, the following research question guided this study.

• How do middle school students’ mathematics self-efficacy and teaching quality perceptions relate to their mathematics achievement emotions? 

This study employed associational research to uncover the relationships between students’ mathematics self-efficacy, perceived teaching quality, perceived teacher affective support, and mathematics achievement emotions. The sample constituted 5475 students (52.6% female students) through cluster sampling from 53 public middle schools in a metropolitan city in Turkey. Achievement Emotions Questionnaire-Mathematics (AEQ-M; Pekrun et al., 2005) measures students’ achievement emotions in mathematics. Accordingly, the Turkish adaptation of AEQ-M (Author, 2019) was administered by selecting enjoyment, anxiety, and anger dimensions regarding the prevalent nature of emotions in learning environments. Confirmatory factor analysis (CFA) revealed an acceptable model fit to data (Comparative Fit Index (CFI)=.96, Non-Normed Fit Index (NNFI) =.93, Root Mean Square Error of Approximation (RMSEA)=.12, and Standard Root Mean Square Residual (SRMR)=.029). Cronbach’s alpha coefficients were all above .80 for three emotion dimensions. The Self-efficacy Scale for Self-regulated Learning (Usher, 2007) assesses students’ capability judgments regarding the use of self-regulated learning strategies in mathematics. In this study, the researcher-adapted scale (Author, 2014), including 11 items, was used to gauge students’ mathematics self-efficacy. CFA confirmed a one-dimensional structure with appropriate fit indices (CFI = .96, NNFI = .96, RMSEA = .047, and SRMR = .028). Besides, Cronbach’s alpha coefficient was .89. Perceived Teaching Quality (PTQ; Goetz et al., 2013) and Perceived Teacher Affective Support (PTAS; Sakiz, 2017) scales were used to measure teaching quality in mathematics. The researcher-adapted PTQ scale (8 items) was employed to measure students’ perceptions of teaching quality in mathematics. CFA revealed a two-dimensional structure with the following fit indices (CFI = .96, NNFI = .94, RMSEA =.059, and SRMR = .043). In addition, internal consistency estimates were .79 for the supportive presentation style and .67 for the excessive lesson demands dimension. Furthermore, PTAS (12 items) was employed to explain how students perceive their mathematics teachers’ affective characteristics. CFA findings validated one-dimensional structure with appropriate fit indices (CFI = .96, NNFI = .95, RMSEA = .062, and SRMR = .03). Besides, Cronbach’s alpha was .93. Structural equation modelling (SEM) was performed to examine the relationships between latent variables of mathematics achievement emotions, self-efficacy, and teaching quality dimensions. Descriptive and reliability analyses and assumption checks were carried out through IBM SPSS 22. In interpreting the model fit, the recommended goodness of fit statistics for RMSEA, CFI, NNFI, and SRMR were used (Hu & Bentler, 1999; Browne & Cudeck, 1993) in Mplus 8.3 (Muthen & Muthen, 2019).

Students’ perceived teacher affective support (γ = -.08, p < .01), their teachers’ supportive presentation style (γ = -.13, p <.001), and excessive lesson demands (γ = .29, p < .001; γ = -.18, p < .001), as well as self-efficacy for self-regulated learning (β = -.38, p < .001; β = .56, p < .001), significantly predicted anger in mathematics. Similarly, students’ perceptions of their teachers’ supportive presentation style (γ = - .07, p < .05; γ = .16, p < .001) and excessive lesson demands (γ = .41, p < .001; γ = -.18, p < .001), along with self-efficacy for self-regulated learning (β = -.38, p < .001; β = .56, p < .001), significantly contributed to explaining anxiety and enjoyment, respectively. However, perceived teacher affective support did not significantly explain anxiety (γ = .006, p >.05) or enjoyment (γ = .05, p > .05). Students’ perceptions of their teachers’ supportive presentation style (γ = .38, p < .001) and perceived teacher affective support positively (γ = .10, p < .01), while teachers’ excessive lesson demands negatively predicted self-efficacy for self-regulated learning (γ = -.32, p < .001). For the indirect effects, perceived teacher affective support had significant negative indirect effects on anxiety (-.039) and anger (-.039) through self-efficacy for self-regulated learning in mathematics. Similarly, students’ perceptions of their mathematics teachers’ supportive presentation style and excessive lesson demands had significant indirect effects on student anger (-.145; .121), anxiety (-.147; .122), and enjoyment (.213; -.178). Overall, the model explained 44% of the variance in self-efficacy for self-regulated learning and 65%, 53%, and 51% in enjoyment, anxiety, and anger in mathematics, respectively. Overall, this study highlights the importance of teacher behaviors—particularly affective support and how they present the material—on student emotional responses and self-efficacy in mathematics.

Selected References Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman. Bieg, M., Goetz, T., Sticca, F., Brunner, E., Becker, E., Morger, V., & Hubbard, K. (2017). Teaching methods and their impact on students’ emotions in mathematics: an experience-sampling approach. ZDM Mathematics Education, 49 (3), 411-422. https://doi.org/10.1007/s11858-017-0840-1 Goetz, T., Keller, M. M., Lüdtke, O., Nett, U. E., & Lipnevich, A. A. (2019). The dynamics of real-time classroom emotions: Appraisal mediate the relation between students’ perceptions of teaching and their emotions. Journal of Educational Psychology. https://psycnet.apa.org/doi/10.1037/edu0000415 Goetz, T., Lüdtke, O., Nett, U. E., Keller, M., & Lipnevich, A. A. (2013).Characteristics of teaching and students’ emotions in the classroom: Investigating differences across domains. Contemporary Educational Psychology, 38, 383-394. https://doi.org/10.1016/j.cedpsych.2013.08.001 Muthen, L. K., & Muthen, B. O. (2019). MPlus8 [Computer Software]. Los Angeles. CA Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341. https://doi.org/10.1007/s10648-006-9029-9 Pekrun, R. (2018). Control-value theory. A social-cognitive approach to achievement emotions. In G. A. D. Liem, D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 162-190). Information Age Publishing. Pekrun, R. (2024). Control-value theory: From achievement emotion to a general theory of human emotions. Educational Psychology Review, 36(3), 83. https://doi.org/10.1007/s10648-024-09909-7