Chemistry learning poses persistent challenges in the 21st-century educational landscape due to its abstract nature and the inherent need to integrate symbolic, macroscopic, and microscopic representations. In such cognitively demanding domains, students’ ability to proactively manage their learning processes—rather than relying on passive reception—becomes a critical determinant of academic success. Grounded in the social-cognitive perspective of Self-Regulated Learning (SRL), this study investigates how the interplay between motivational beliefs and self-regulatory strategies influences chemistry achievement within highly competitive, high-stakes testing environments.

The theoretical framework is primarily anchored in Pintrich’s (2000) multidimensional model of SRL, which provides a comprehensive structural basis for understanding the cognitive and motivational pillars of learning. Pintrich’s model is unique in its integration of expectancy-value components with cognitive and metacognitive strategies, allowing for a nuanced exploration of why students choose to engage (motivation) and how they execute that engagement (strategies). To complement the structural depth of Pintrich’s framework, this study incorporates Zimmerman’s (2000) cyclical phase model. Zimmerman’s model elucidates the iterative process of learning through three distinct phases: forethought (goal setting and strategic planning), performance (self-control and self-observation), and self-reflection (self-judgment and causal attribution).

In the rigorous context of chemistry education, where the complexity of content often leads to cognitive overload, students' success depends not only on the quantity of study time but on the quality of their regulation (Schunk & Greene, 2018). This study focuses on the tension between surface-level processing and deep-level regulation. For instance, while high-stakes assessment environments often push students toward rehearsal strategies (rote memorization), Pintrich’s framework suggests that deeper engagement through elaboration and effective resource management (e.g., time and study environment management) is essential for conceptual mastery. By integrating these two dominant SRL models, the study explores whether autonomous, self-regulated behaviors can serve as a buffer against the detrimental effects of standardized, exam-oriented pressure.

From a European and international perspective, this research addresses a vital concern: the "rehearsal paradox" observed in many modern educational systems where high performance in standardized tests does not necessarily equate to scientific literacy or conceptual understanding (Zohar & Dori, 2003). As Europe moves toward fostering "competence-based" science education, understanding the self-regulatory profiles of students in competitive contexts like Turkey offers a valuable comparative lens for international researchers and policymakers aiming to develop resilient, 21st-century learners (Wigfield & Eccles, 2000).

Accordingly, this study addresses the following research questions:

1. To what extent do twelfth-grade students’ motivational beliefs predict chemistry achievement?
2. To what extent do their self-regulated learning strategies predict chemistry achievement?

This study employed a correlational research design to examine the predictive relationships between self-regulated learning components and chemistry achievement. The sample consisted of 161 twelfth-grade students (science track) from four different districts in Ankara, Turkey. The sample reflected a diverse range of socioeconomic backgrounds, providing a representative cross-section of students facing high-stakes university entrance examinations. Data were collected using two validated and reliable instruments. First, the Turkish adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ), originally developed by Pintrich et al. (1991) and adapted into Turkish by Sungur (2007), was utilized. The MSLQ is a self-report instrument consisting of two main sections: a motivation section and a learning strategies section. In this study, specific subscales corresponding to Pintrich’s SRL framework were selected, utilizing a 7-point Likert scale (1 = not at all true of me, 7 = very true of me). The internal consistency of the subscales was verified, ensuring the instrument's reliability for the local context. Second, chemistry achievement was assessed through a researcher-developed Chemistry Achievement Test (CAT), consisting of 29 multiple-choice items. The CAT was meticulously designed to measure conceptual understanding across the high school chemistry curriculum, moving beyond rote recall to higher-order thinking. The test underwent a rigorous validation process, including content validity checks by an expert panel of chemistry educators and experienced secondary school teachers. Following a pilot study with 100 students, the CAT demonstrated high internal consistency with a Cronbach’s alpha of .853, and item analysis was performed to ensure appropriate difficulty and discrimination indices. Data analysis was conducted using IBM SPSS software. Multiple Linear Regression (MLR) analyses were performed to determine the predictive power of the independent variables (motivational beliefs and learning strategies) on the dependent variable (chemistry achievement). Before the analysis, all necessary statistical assumptions were tested: normality of residuals was checked through histograms and P-P plots; linearity was verified via scatterplots; and the absence of multicollinearity was confirmed using Variance Inflation Factor (VIF < 10) and Tolerance (> .10) values. Two separate regression models were constructed to identify the unique contributions of motivational and strategic components, ensuring a clear differentiation between students' "will" and "skill."

Multiple linear regression analyses revealed a differentiated pattern of predictors that highlight the complex nature of SRL in science education. Motivational beliefs significantly predicted achievement, accounting for 15.6% of the variance (R2 = .156, p < .05). Among these, task value emerged as the strongest positive predictor (β = .363, p = .002), indicating that students who perceive chemistry as meaningful and personally relevant are significantly more likely to succeed. This finding reinforces the expectancy-value theory (Wigfield & Eccles, 2000), suggesting that internal motivation is a more powerful driver than external exam pressure. Learning strategies significantly predicted achievement accounting for 20.7% variance (R2 = .207, p < .05). A striking "rehearsal paradox" was identified in this section. While high-stakes environments often encourage rote learning, rehearsal strategies emerged as a significant negative predictor of achievement (β= -.284, p = .018). This empirically demonstrates that reliance on surface-level processing hinders conceptual mastery in chemistry (Zohar & Dori, 2003). Conversely, time and study environment management significantly contributed to success (β= .273, p = .018), highlighting the critical role of resource regulation in competitive contexts. These findings advocate for a transformative pedagogical shift within European science education. To develop resilient, autonomous learners, instruction must move beyond content delivery toward explicit strategy instruction. Specifically, science educators should focus on enhancing students' "task value perceptions" by connecting abstract chemistry concepts to real-world applications. Furthermore, the results emphasize the need to discourage rote memorization in favor of metacognitive and resource management strategies. By offering a comparative lens on how competitive environments shape learning, this study contributes to the international discourse on fostering deep scientific understanding in the 21st century.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451-502). Academic Press. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of Self-Regulation of Learning and Performance (2nd ed.). Routledge. Sungur, S. (2007). Modeling the relationships between students' motivational beliefs, self-regulated learning strategies, and effort regulation. Turkish Online Journal of Educational Technology, 6(4), 119-126. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive commentary. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. Zohar, A., & Dori, Y. J. (2003). Higher order thinking skills and low-achieving students: Are they mutually exclusive? Journal of the Learning Sciences, 12(2), 145-181.