Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6, 1249-1263 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 17 March 2025; Revised: 13 May 2025; Accepted: 16 May 2025; Published: 16 June 2025 * Correspondence: meyta.dkurniasih@students.unnes.ac.id Using SEM-PLS to examine the correlation between habit of mind and belief of mathematics pre- service students Meyta Dwi Kurniasih1*, YL Sukestiyarno2, Wardono3, Tri Sri Noor Asih4 1,2,3,4Universitas Negeri Semarang, Semarang, Indonesia; meyta.dkurniasih@students.unnes.ac.id (M.D.K.), sukestiyarno@mail.unnes.ac.id (Y.S.), wardono@mail.unnes.ac.id (W.) inung.mat@mail.unnes.ac.id (T.S.N.A.). Abstract: Mathematical belief, encompassing cognitive, affective, and dispositional aspects, fundamentally shapes individuals’ attitudes toward mathematics. It reflects seriousness, confidence, and subjective stances in mathematical thinking and learning. In teacher education, prospective teachers’ beliefs significantly influence their instructional choices and student achievement. This study investigates the direct effect of Habit of Mind on mathematical belief using second-order confirmatory factor analysis within a Structural Equation Modeling (SEM) framework. Data from 200 prospective mathematics teachers were collected via a cross-sectional survey. Results indicate a strong, positive impact of Habit of Mind on mathematical belief. Importance-Performance Map Analysis (IPMA) further suggests that fostering consistent, reflective thinking habits enhances mathematical beliefs. Among Habit of Mind dimensions, Applying Past Knowledge to New Situations, Metacognition, and Thinking Interdependently emerged as the most influential. These cognitive dispositions can be systematically developed through well-designed instructional strategies in university settings. The study highlights the necessity of integrating Habit of Mind development into teacher education programs to strengthen mathematical beliefs and support more effective mathematics teaching and learning. Keywords: Beliefs, Habit of mind, Mathematics, Preservice students, SEM-PLS. 1. Introduction The primary objective of education is to cultivate high-quality human resources—individuals who are adaptable, progressive, and competitive within their respective fields of expertise [1, 2]. These competencies are expected to enhance a nation’s competitiveness, enabling it to thrive amid globalization across various sectors [3]. Accordingly, higher education should not only focus on the transmission of scientific knowledge but also promote character development by integrating cognitive, affective, and psychomotor domains [4]. Within the affective domain, belief in mathematics is one aspect that warrants particular attention. Beliefs function as a driving force behind actions, representing an internal commitment to behavior. They play a critical role in eliminating doubts that may hinder engagement and in fostering the development of decisive actions [5]. In the context of mathematics learning, students’ beliefs refer to the attitudes they exhibit during their coursework [6]. Academic success in mathematics is influenced not only by students’ skills and abilities but also by their confidence, which significantly contributes to their performance [7]. Beliefs are a key determinant of students’ success in learning mathematics [8]. Students who engage seriously in mathematics by completing assignments diligently, participating actively in discussions, and submitting work thoroughly and on time demonstrate strong mathematical confidence. Positive beliefs increase students’ willingness to engage meaningfully with mathematical concepts, while negative beliefs can hinder their learning outcomes. Beliefs also bridge the gap between teachers’ knowledge and classroom practice [9] and are considered strong predictors of decision-making https://orcid.org/0000-0003-0269-8302 https://orcid.org/0000-0003-2377-5872 https://orcid.org/0009-0005-1039-4538 https://orcid.org/0000-0002-1287-4579 1250 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate throughout life. Belief systems develop over time and are shaped by cultural contexts, mathematics classroom experiences, teaching methods, and personal reflection. According to Lau [10] there are three interconnected components of teachers’ mathematical belief systems: their view of the nature of mathematics, their model of teaching mathematics, and their conception of how mathematics is learned. For instance, a teacher who views mathematics as a problem-solving discipline is more likely to encourage student exploration and conceptual understanding, while one with an instrumentalist view may emphasize rote memorization and procedural fluency [11]. Research has demonstrated that changes in teachers’ beliefs, knowledge, and practices occur gradually over time [12]. As a result, fostering a positive classroom environment is essential to promoting favorable attitudes toward mathematics. Whether through classroom activities, homework, practice, or assessments, it is vital to support and encourage students in completing tasks so they remain motivated, confident in their mathematical abilities, and engaged in problem-solving processes [13]. Students’ mathematics learning is also shaped by internal factors, including attitudes, beliefs, motivation, self-confidence, and anxiety [14]. Classroom-based mathematics instruction gradually influences students' mathematical beliefs, which in turn affect how they engage with and comprehend course material. Low mathematical belief can result in reduced participation in learning, a limited understanding of mathematical structures, and difficulty applying mathematical knowledge in everyday contexts [15]. Moreover, students with low mathematical belief often lack confidence when solving mathematical problems. Teachers play a pivotal role in shaping students’ attitudes and behaviors, thereby significantly impacting learning outcomes [16]. They must contribute actively to the development of students’ mathematical understanding by providing opportunities for intellectual challenge and engagement in higher-order thinking through the purposeful selection of effective teaching strategies and tasks [17]. Mathematics education seeks to cultivate cognitive dispositions that support effective problem- solving in both academic and real-life contexts [18]. Habits of Mind—defined as tendencies toward intelligent behavior play a vital role in mathematical problem-solving and substantially influence pre- service teachers’ mathematical beliefs [19]. Investigating the relationship between Habits of Mind and mathematical beliefs among pre-service teachers is therefore essential, as these factors can significantly shape their future teaching practices and influence students’ learning experiences. It is well recognized that student success is greatly influenced by habitual behaviors. When practiced consistently, positive habits can cultivate productive skills. A habit may be defined as a learned pattern of responding to specific situations, repeated consistently over time [20]. Meanwhile, Habits of Mind refer to intelligent behaviors applied when encountering problems that do not have immediately obvious solutions [21]. This includes processes by which students construct their own understanding and act constructively in the face of dilemmas, uncertainty, or complexity. As such, cultivating Habits of Mind can enhance mathematical thinking and reinforce positive mathematical beliefs. Habits of Mind are characterized by the behaviors of effective problem solvers when faced with dilemmas, paradoxes, and complex problems without clear solutions [22]. Pre-service teachers’ beliefs about mathematics encompass their personal philosophies, attitudes, and values related to the nature of mathematics as well as its teaching and learning. These beliefs may vary widely between individuals and even within the same individual depending on context. Beliefs about mathematics may range from viewing it as a static body of knowledge to perceiving it as a dynamic and evolving field of inquiry [23]. A growth mindset as opposed to a fixed mindset encourages greater effort, resilience, and persistence in the face of academic challenges, ultimately leading to improved academic performance [24]. Mathematical beliefs are shaped by value judgments formed through individuals’ past experiences with mathematics [25]. These subjective beliefs significantly influence students’ strategies and behaviors in mathematical problem-solving [26]. Furthermore, beliefs are closely intertwined with both affective and cognitive domains in mathematics education [27]. Therefore, exploring the interconnections between beliefs and related constructs can offer deeper insights into how mathematics is learned and taught [10]. 1251 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate 2. Method This research employed a cross-sectional design, providing a snapshot of the variables of interest at a single point in time. Such a design facilitates the examination of relationships between variables without manipulation [28]. A quantitative approach was utilized to identify and measure the strength and direction of the correlation between Habits of Mind and beliefs about mathematics among pre- service teachers. The unit of analysis was the individual, with participants completing a questionnaire consisting of sorted choices and statements. Research variables were measured perceptually using a five- point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The independent variable, Habits of Mind, was assessed through seven indicators: persisting (PS), managing impulsivity (MI), thinking flexibly (TF), metacognition (MC), applying past knowledge (APKN), remaining open to continuous learning (ROCL), and thinking interdependently (TI). The dependent variable, beliefs about mathematics, was measured using five indicators: certainty of knowledge (CK), role of the lecturer (RL), systematic process (SP), innate ability (IA), and quick learning (QL). A total of 200 responses were collected from mathematics education students during the 2022–2023 academic year. Respondents were drawn from five cohorts: Batch 1 included 42 students (37 women, 5 men); Batch 2 had 23 students (19 women, 4 men); Batch 3 comprised 37 students (31 women, 6 men); Batch 4 consisted of 89 students (77 women, 12 men); and Batch 5 had 9 students (8 women, 1 man). The distribution of respondents is illustrated in Figure 1. Figure 1. Sample size. Data analysis for hypothesis testing in this study was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM offers a robust statistical approach for examining complex relationships between latent variables, especially when handling non-normal data and exploratory research questions. This method is particularly suitable for investigating the correlation between Habits of Mind and beliefs about mathematics among pre-service students [29]. It enables the assessment of both direct and indirect effects, providing insights into the mechanisms through which 1252 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate Habits of Mind may influence beliefs about mathematics. Prior to hypothesis testing, the relationships between the latent variables, namely Habits of Mind and beliefs about mathematics, were first examined using SmartPLS software. 3. Results and Discussion This research was conducted using a quantitative approach, employing descriptive statistics and hypothesis testing with Partial Least Squares Structural Equation Modeling (PLS-SEM). The variables involved in this study were Habits of Mind and Beliefs about Mathematics. Data were collected from 200 prospective teacher students and are presented descriptively in Table 1. Table 1. Descriptive, Normality statistics. Construct Item Code Mean Main Max St. Deviation Excess kurtosis Skewness Persisting (PS) PS 1 4.297 3 5 0.524 -0.623 0.180 PS 2 4.267 3 5 0.551 -0.442 0.027 PS 3 3.823 3 5 0.716 -1.025 0.276 PS 4 3.949 3 5 0.585 -0.086 0.008 PS 5 3.838 3 5 0.665 -0.768 0.195 PS 6 4.201 3 5 0.609 -0.485 -0.137 PS 7 3.712 3 5 0.711 -0.926 0.483 PS 8 3.607 3 5 0.679 -0.655 0.679 Managing Impulsivity (MI) MI 1 4.066 3 5 0.581 -0.061 -0.006 MI 2 4.069 3 5 0.614 -0.357 -0.040 MI 3 3.664 3 5 0.694 -0.806 0.564 MI 4 3.904 3 5 0.712 -1.023 0.142 MI 5 4.195 3 5 0.538 -0.049 0.119 MI 6 4.252 3 5 0.592 -0.503 -0.141 Thinking Fexibly (TF) TF 1 3.997 3 5 0.729 -1.117 0.005 TF 2 4.366 3 5 0.557 -0.797 -0.138 TF 3 4.045 3 5 0.538 0.451 0.038 TF 4 3.718 3 5 0.647 -0.718 0.352 TF 5 4.009 3 5 0.582 -0.036 -0.001 TF 6 3.835 3 5 0.731 -1.099 0.268 TF 7 3.763 3 5 0.616 -0.571 0.198 TF 8 3.787 3 5 0.564 -0.271 -0.001 Metacognition (MC) MC 1 4.048 3 5 0.583 -0.061 -0.005 MC 2 4.192 3 5 0.542 -0.059 0.101 MC 3 3.919 3 5 0.749 -1.210 0.134 MC 4 3.868 3 5 0.591 -0.242 0.041 MC 5 3.958 3 5 0.557 0.221 -0.016 MC 6 3.991 3 5 0.582 -0.036 0.001 MC 7 4.021 3 5 0.566 0.132 0.004 MC 8 4.042 3 5 0.557 0.221 0.016 MC 9 3.571 3 5 0.624 -0.557 0.625 Applying Past Knowledge to New Situations (APKN) APKN 1 4.108 3 5 0.514 0.576 0.155 APKN 2 3.889 3 5 0.660 -0.717 0.124 APKN 3 3.532 3 5 0.651 -0.383 0.836 APKN 4 4.150 3 5 0.498 0.531 0.287 APKN 5 4.216 3 5 0.515 -0.057 0.248 APKN 6 3.886 3 5 0.638 -0.577 0.104 APKN 7 4.207 3 5 0.533 -0.085 0.143 1253 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate Construct Item Code Mean Main Max St. Deviation Excess kurtosis Skewness APKN 8 4.264 3 5 0.522 -0.405 0.210 APKN 9 3.910 3 5 0.770 -1.300 0.156 Remaining Open to Continous Learning (ROCL) ROCL 1 3.961 3 5 0.686 -0.868 0.050 ROCL 2 3.847 3 5 0.746 -1.171 0.257 ROCL 3 4.006 3 5 0.763 -1.285 -0.010 ROCL 4 4.381 3 5 0.560 -0.812 -0.190 ROCL 5 4.060 3 5 0.602 -0.251 -0.025 ROCL 6 3.634 3 5 0.661 -0.686 0.566 Thinking Interdependently (TI) TI 1 3.598 3 5 0.693 -0.652 0.733 TI 2 3.730 3 5 0.710 -0.941 0.443 TI 3 4.024 3 5 0.547 0.352 0.015 TI 4 4.138 3 5 0.519 0.389 0.172 TI 5 3.817 3 5 0.755 -1.192 0.318 TI 6 4.267 3 5 0.573 -0.483 -0.079 TI 7 3.757 3 5 0.758 -1.147 0.439 TI 8 3.820 3 5 0.746 -1.156 0.306 Certainty of Knowledge (CK) CK 1 3.736 3 5 0.765 -1.142 0.489 CK 2 4.300 3 5 0.559 -0.580 -0.046 CK 3 4.132 3 5 0.616 -0.431 -0.088 CK 4 3.778 3 5 0.742 -1.110 0.383 CK 5 4.270 3 5 0.594 -0.547 -0.172 CK 6 3.667 3 5 0.710 -0.855 0.584 CK 7 4.207 3 5 0.582 -0.348 -0.062 CK 8 4.060 3 5 0.602 -0.251 -0.025 CK 9 3.625 3 5 0.719 -0.782 0.702 CK 10 4.120 3 5 0.608 -0.362 -0.067 Quick Learning (QL) QL 1 3.634 3 5 0.713 -0.792 0.669 QL 2 3.544 3 5 0.668 -0.431 0.842 QL 3 3.949 3 5 0.639 -0.553 0.045 QL 4 3.745 3 5 0.746 -1.089 0.454 Systematic Process (SP) SP 1 4.204 3 5 0.591 -0.388 -0.087 SP 2 4.237 3 5 0.560 -0.340 0.006 SP 3 4.177 3 5 0.571 -0.198 -0.009 SP 4 3.958 3 5 0.638 -0.537 0.036 SP 5 4.321 3 5 0.597 -0.634 -0.261 SP 6 3.898 3 5 0.668 -0.767 0.120 SP 7 3.733 3 5 0.696 -0.894 0.417 SP 8 3.799 3 5 0.726 -1.059 0.328 SP 9 3.871 3 5 0.696 -0.936 0.181 SP 10 4.009 3 5 0.761 -1.275 -0.015 SP 11 4.201 3 5 0.558 -0.193 0.031 SP 12 3.820 3 5 0.691 -0.909 0.256 SP 13 4.336 3 5 0.560 -0.701 -0.102 SP 14 3.901 3 5 0.801 -1.424 0.181 Innate Ability (IA) IA 1 3.664 3 5 0.658 -0.722 0.489 IA 2 4.027 3 5 0.607 -0.278 -0.013 IA 3 3.625 3 5 0.723 -0.793 0.708 IA 4 3.682 3 5 0.756 -1.020 0.604 Role of Lecturer (RL) RL 1 4.150 3 5 0.576 -0.163 -0.014 RL 2 3.562 3 5 0.644 -0.506 0.719 RL 3 3.883 3 5 0.659 -0.713 0.130 1254 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate Construct Item Code Mean Main Max St. Deviation Excess kurtosis Skewness RL 4 4.102 3 5 0.560 0.090 0.028 RL 5 3.916 3 5 0.705 -0.986 0.120 RL 6 4.261 3 5 0.586 -0.502 -0.128 RL 7 4.138 3 5 0.624 -0.497 -0.107 RL 8 3.793 3 5 0.617 -0.536 0.164 RL 9 3.709 3 5 0.729 -0.980 0.512 RL 10 3.691 3 5 0.704 -0.877 0.519 RL 11 4.048 3 5 0.562 0.159 0.013 RL 12 3.850 3 5 0.640 -0.615 0.145 RL 13 3.910 3 5 0.678 -0.830 0.113 RL 14 4.057 3 5 0.595 -0.183 -0.018 RL 15 3.547 3 5 0.668 -0.444 0.831 RL 16 3.483 3 5 0.642 -0.129 0.986 Table 1 presents the descriptive statistical results for the components of Habits of Mind and Beliefs about Mathematics. Habits of Mind are described by seven constructs, with the highest average observed in Managing Impulsivity (MI) at 4.03, which pertains to the ability to manage time effectively and think before acting. The lowest average was found in Thinking Interdependently (TI) at 3.89, reflecting the capacity to collaborate and learn with others in a team setting. Regarding Beliefs about Mathematics, the highest average was 4.03 in Systematic Process (SP), indicating that classroom learning follows a sequential order aligned with students' cognitive development. The lowest construct was Quick Learning (QL), with an average of 3.72. Moreover, the skewness values ranging between -2 and 2 suggest that the research data originate from a normally distributed population [30]. Structural model assessment included evaluating path coefficients and their significance levels, representing the direct effects between variables [31]. Convergent and discriminant validity tests using SEM-PLS were performed on each instrument item for Habits of Mind and Beliefs about Mathematics to confirm the validity and reliability of the measures [32]. 1255 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate Figure 2. Initial PLS Research Model. 1256 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate Table 2. First Order Construct. Construct Item Code Outer Loading Composite Reliability Average Variance Extracted (AVE) PS PS 1 0.817 0.850 0.591 PS 2 0.846 PS 4 0.581 PS 6 0.802 MI MI 1 0.688 0.751 0.389 MI 3 0.426 MI 4 0.444 MI 5 0.761 MI 6 0.718 TF TF 1 0.507 0.771 0.364 TF 2 0.699 TF 3 0.678 TF 5 0.643 TF 6 0.541 TF 7 0.522 MC MC 1 0.663 0.905 0.615 MC 2 0.801 MC 5 0.749 MC 6 0.824 MC 7 0.846 MC 8 0.809 APKN APKN 1 0.793 0.903 0.617 APKN 4 0.829 APKN 5 0.880 APKN 7 0.827 APKN 8 0.867 APKN 9 0.420 ROCL ROCL 1 0.427 0.761 0.401 ROCL 2 0.502 ROCL 3 0.597 ROCL 4 0.802 ROCL 5 0.757 TI TI 1 0.617 0.836 0.392 TI 2 0.510 TI 3 0.556 TI 4 0.734 TI 5 0.602 TI 6 0.724 TI 7 0.573 TI 8 0.658 CK CK 1 0.425 0.885 0.504 CK 10 0.771 CK 2 0.744 CK 3 0.700 CK 4 0.425 CK 5 0.852 CK 7 0.846 1257 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate CK 8 0.768 QL QL 1 0.821 0.766 0.540 QL 2 0.870 QL 4 0.434 SP SP 1 0.777 0.899 0.563 SP 11 0.681 SP 13 0.748 SP 2 0.849 SP 3 0.778 SP 5 0.778 SP 6 0.617 IA IA 2 0.875 0.695 0.543 IA 3 0.567 RL RL 1 0.755 0.893 0.460 RL 11 0.634 RL 12 0.620 RL 14 0.748 RL 2 0.432 RL 3 0.687 RL 4 0.736 RL 6 0.736 RL 7 0.762 RL 8 0.600 Figure 2 and Table 2 present the results of construct validity testing based on the items within each variable. These confirmatory results aim to verify and assess the relationships between each item and their corresponding indicators in the Habits of Mind and Beliefs about Mathematics variables. Accordingly, a consistent Partial Least Squares (PLS) approach was employed. Construct validity ensures that a set of measurable variables accurately represents the intended construct [33]. Indicators of construct validity typically include convergent validity, composite reliability (CR), and discriminant validity. The results of the convergent validity and CR assessments are shown in Table 2. This analysis reveals that each item’s loading factor is ≥ 0.40, composite reliability is ≥ 0.70, average variance extracted (AVE) values exceed 0.50, and CR values surpass 0.70. Therefore, the convergent validity and composite reliability of the constructs are deemed satisfactory. Table 3. Second Order Construct. Construct Code Outer Loading Composite Reliability Average Variance Extracted (AVE) Habit of Mind PS 0.612 0.908 0.589 MI 0.649 TF 0.739 MC 0.842 APKN 0.906 ROCL 0.744 TI 0.833 Belief of Math CK 0.903 0.870 0.587 QL 0.435 SP 0.842 IA 0.644 RL 0.898 1258 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate In Table 3, the variable Habit of Mind is measured by seven constructs, while the variable Belief about Mathematics is measured by five constructs. These constructs are valid, as indicated by outer loading values ranging from 0.435 to 0.903, demonstrating strong correlations between the measurement items and their respective variables. The reliability of the Habit of Mind variable is acceptable, with a composite reliability (CR) of 0.908, exceeding the threshold of 0.70, and convergent validity supported by an average variance extracted (AVE) of 0.589, which is greater than 0.50. Similarly, the Belief about Mathematics variable demonstrates acceptable reliability with a composite reliability of 0.870 and convergent validity with an AVE of 0.587. Among the seven valid measurement items for Habit of Mind, the strongest indicators are Applying Past Knowledge to New Situations (APKN = 0.906) and Metacognition (MC = 0.842). Applying past knowledge in mathematics teaching has been shown to positively influence students’ achievement [34]. This effect is attributed to increased student interest and the recognition that mathematics is a dynamic field that has evolved and continues to evolve. Identifying specific aspects of prior mathematical knowledge that effectively enhance student learning outcomes is crucial. Among the five valid measurement items for Belief about Mathematics, the strongest indicators are Certainty of Knowledge (CK = 0.903) and Role of Lecturer (RL = 0.898). Table 4. Fornell Larcker Criterion. CK QL SP IA RL PS MI TF MC APKN ROCL TI CK 0.785 QL 0.346 1.000 SP 0.623 0.357 0.801 IA 0.599 0.133 0.408 1.000 RL 0.721 0.350 0.683 0.559 0.779 PS 0.455 0.261 0.482 0.304 0.445 0.879 MI 0.517 0.190 0.489 0.342 0.472 0.510 0.836 TF 0.597 0.284 0.528 0.439 0.548 0.406 0.500 0.833 MC 0.642 0.259 0.448 0.475 0.596 0.377 0.463 0.575 0.796 APKN 0.747 0.346 0.618 0.553 0.710 0.450 0.506 0.625 0.697 0.840 ROCL 0.566 0.276 0.525 0.382 0.511 0.475 0.492 0.509 0.529 0.593 0.861 TI 0.665 0.353 0.518 0.537 0.620 0.486 0.402 0.530 0.626 0.716 0.618 0.863 Table 4 presents the Fornell-Larcker criterion, which is used to assess discriminant validity. Discriminant validity ensures that constructs are theoretically distinct and empirically verified through statistical testing. According to the Fornell and Larcker criterion, the square root of the average variance extracted (AVE) for each construct should be greater than its correlations with other constructs [35]. As shown in Table 4, the square root of the AVE for all constructs exceeds their correlations with other variables. For example, the square root of the AVE for Certainty of Knowledge (CK) is 0.785, which is higher than its correlations with all other constructs. Since the square roots of the AVE values for all latent variables are greater than their correlations with other constructs, the discriminant validity requirements for this model have been satisfied. Table 5. Path Analysis. Hypothesis Path Std. Beta Std. Error T-Value P-Value Confident Interval 5.0% 95.0% Decision H1 Habit -> Belief 0.837 0.032 26.081 0.000 0.781 0.889 Supported The findings of this study provide compelling empirical support for the first hypothesis (H1), which posits that Habits of Mind (HoM) significantly influence the enhancement of mathematical beliefs among pre-service teachers. The statistical analysis revealed a robust and statistically significant 1259 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate positive correlation between the two constructs, as evidenced by a path coefficient of 0.837 and a p-value of 0.000 (p < 0.05). These results underscore that fostering the development of Habits of Mind in teacher preparation programs is not only beneficial but essential for nurturing constructive beliefs about mathematics. Within the field of mathematics education, this correlation holds significant implications, particularly given the well-documented role that teacher beliefs play in shaping pedagogical decisions, instructional approaches, and students' learning outcomes. This study sought to examine the nuanced relationship between cognitive dispositions manifested through Habits of Mind and affective constructs such as beliefs about mathematics. One of the key contributions of this research lies in its demonstration that HoM are not merely abstract cognitive tendencies, but practical, cultivable habits that significantly shape how future educators conceptualize, engage with, and ultimately teach mathematics. The idea that thinking dispositions impact mathematical belief systems is increasingly being recognized in the literature, with researchers such as Sabanal, et al. [36] highlighting that teachers who view mathematics as an essential tool for professional and personal development are more inclined to exhibit adaptive instructional behaviors rooted in positive mathematical beliefs. Consistent with the findings of Hawash, et al. [37] the current study affirms that individuals who possess well-developed HoM such as persistence, managing impulsivity, striving for accuracy, and metacognitive awareness tend to hold more sophisticated and growth-oriented beliefs about mathematics. These beliefs include seeing mathematics as a subject that is logical, creative, and learnable by all students. Such beliefs contrast sharply with fixed, procedural views that regard mathematics as static and reserved for the intellectually elite. Beliefs of this nature significantly impact how pre-service teachers approach mathematical content and their expectations for students’ engagement, ability, and achievement. Further analysis of specific Habits of Mind revealed that Applying Past Knowledge to New Situations (APKN), Metacognition (MC), and Thinking Interdependently (TI) were the most influential in fostering mathematical beliefs. Each of these sub-constructs contributes to different dimensions of mathematical belief development. APKN enables future educators to transfer their learning across contexts, promoting a belief in the coherence and transferability of mathematical knowledge. Metacognition fosters reflective thinking, which is critical for evaluating one’s understanding, adjusting teaching strategies, and reinforcing the belief that mathematics involves reasoning and insight, not just memorization. Thinking interdependently underscores the social nature of mathematical knowledge construction and promotes beliefs that value communication, collaboration, and collective problem- solving. A critical implication of these findings concerns the opportunity for intervention in teacher education. Prior research has consistently shown that beliefs about mathematics are formed early and often remain unchanged throughout a teacher's career [38]. However, this study challenges the notion that such beliefs are immutable. When prospective teachers are exposed to structured activities that foster reflective thinking, critical inquiry, and collaborative exploration hallmarks of HoM they begin to reconstruct previously held beliefs. Consequently, the integration of HoM principles into coursework, practicum, and professional learning communities may serve as a lever for transformative belief change. Additionally, the study reinforces the link between Habits of Mind, belief systems, and self-efficacy. As Lau [10] assert, mathematical self-efficacy a teacher’s belief in their ability to teach mathematics effectively has a direct bearing on student achievement. The present findings suggest that cultivating HoM can serve as a means to strengthen self-efficacy beliefs. When teachers believe in their capacity to solve mathematical problems and to teach them meaningfully, they are more likely to embrace complex tasks, implement student-centered strategies, and provide the kind of persistence-driven instruction that leads to deeper learning. Belief systems also influence classroom climate and pedagogical practices. Gullo, et al. [39] report that early-career teachers who possess adaptive beliefs about mathematics tend to implement mastery- oriented instructional strategies and provide emotional support to students. This study contributes to 1260 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate this discussion by showing that HoM such as persistence and metacognition not only shape beliefs but also promote instructional behaviors that foster inclusive and supportive learning environments. Teachers with strong HoM are more likely to see student mistakes as opportunities for learning rather than signs of failure an attitude that translates into the creation of classrooms where risk-taking is encouraged and errors are treated as part of the mathematical process. This connection also aligns with the growing body of literature on teacher leadership and professional identity. Bosica, et al. [40] found that pre-service teachers who are exposed to leadership development programs that emphasize reflective inquiry, critical thinking, and peer collaboration are more likely to develop strong instructional identities. The current study extends this by demonstrating that HoM not only promote leadership dispositions but also cultivate a belief in the importance, relevance, and accessibility of mathematics. This belief can empower teachers to become change agents in their schools—leading initiatives that promote mathematical thinking across disciplines and fostering a culture of inquiry. Another noteworthy implication relates to the relationship between teacher beliefs and classroom management. According to Cohen and Katz [41] teachers who possess strong self-efficacy rooted in robust beliefs and cognitive habits tend to manage classrooms more effectively and employ strategies that maximize engagement. Similarly, Wettstein, et al. [42] found that self-efficacious teachers are more adaptable, empathetic, and student-centered. In this light, HoM function as both cognitive and emotional regulators that empower teachers to respond effectively to the complex, evolving demands of classroom teaching. Despite these encouraging findings, the study also points to areas where further research is warranted. One such area is the specific role of teacher leadership and mentoring programs in reinforcing Habits of Mind and belief development. Although Warren [43] posits that such programs have the potential to foster sustained professional growth, their direct impact on mathematics instruction and belief systems remains underexplored. Future research might investigate how leadership programs that emphasize reflective practice and collaborative problem-solving can be leveraged to promote both HoM and adaptive mathematical beliefs. The role of mentorship is similarly critical. Alegado and Soe [44] emphasize that mentorship during the early stages of a teacher’s career provides the emotional and intellectual scaffolding necessary for the development and reinforcement of positive instructional beliefs. In the context of this study, mentorship that models and encourages the use of HoM can serve as a catalyst for belief transformation. Structured mentoring that includes opportunities for dialogue, reflection, and co- teaching can help pre-service teachers internalize both the habits and the beliefs that underpin effective mathematics instruction. In conclusion, the present study makes a significant contribution to the discourse on mathematics teacher education by demonstrating a strong and meaningful correlation between Habit of Mind and beliefs about mathematics among pre-service teachers. These findings have far-reaching implications for how teacher preparation programs are conceptualized and implemented. Programs that aim to produce reflective, resilient, and relational mathematics educators must go beyond content delivery and actively cultivate the habits of thinking that shape how future teachers believe, teach, and lead. By embedding Habit of Mind into curriculum design, field experiences, and mentoring structures, institutions can empower the next generation of educators to see mathematics as a dynamic, creative, and empowering discipline—one that they can teach with confidence and conviction. 4. Conclusion The findings of this study indicate a positive correlation between Habits of Mind and beliefs about mathematics among prospective teachers. Specifically, students who exhibit strong cognitive habits tend to demonstrate higher confidence in their mathematical abilities. This suggests that fostering thinking habits, such as persistence, metacognition, and applying past knowledge to new situations, can be effectively facilitated through teacher education programs that prioritize the development of prospective teachers’ competencies. Moreover, strengthening these cognitive dispositions not only enhances 1261 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 1249-1263, 2025 DOI: 10.55214/25768484.v9i6.8099 © 2025 by the authors; licensee Learning Gate mathematical self-efficacy but also contributes to more adaptive and constructive beliefs about mathematics, which are critical for effective teaching. Consequently, teacher preparation institutions should intentionally integrate Habit of Mind cultivation within their curricula and practicum experiences to empower future educators to foster positive mathematical beliefs in themselves and their students. Ultimately, this approach can promote more resilient, reflective, and capable mathematics teachers, positively impacting student learning outcomes. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgment: We gratefully acknowledge the Graduate School of Universitas Negeri Semarang and Universitas Muhammadiyah Prof. Dr. HAMKA (UHAMKA) for their invaluable support throughout the research process and its successful completion. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] I. Gligorea, M. Cioca, R. Oancea, A.-T. Gorski, H. Gorski, and P. 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