The present study aims to investigate the impact of the number of response categories in polytomously scored measurement instruments on latent class analysis (LCA) outcomes under varying conditions of sample size and latent class proportion distributions. Accordingly, a comprehensive Monte Carlo simulation study was conducted in which the number of item response categories, sample size, and latent class proportions were systematically manipulated. The simulation design was based on a two–latent-class model, and 100 replications were generated for each experimental condition to ensure the stability of parameter estimates. In line with scoring formats commonly employed in educational and psychological measurement contexts, the number of response categories was specified as three, four, and five. Sample size was operationalized at three levels: small (N = 100), medium (N = 500), and large (N = 1000). Latent class proportions were examined under both equal (0.50–0.50) and unequal (0.70–0.30) distribution conditions to reflect realistic population heterogeneity. All simulation data were analyzed using the poLCA package within the R statistical computing environment. Model fit and classification performance were evaluated using multiple indices, including the log-likelihood, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy. The results revealed a systematic decrease in entropy values—indicative of reduced classification accuracy—as the number of item response categories increased. Specifically, the highest entropy values were consistently observed in measurement structures comprising three response categories. These findings suggest that a reduced number of response categories facilitates more accurate class assignment in latent class analysis. Furthermore, increases in sample size were associated with improved classification performance across all experimental conditions, underscoring the importance of adequate sample sizes in LCA applications.

This study examined the mediating role of life satisfaction and the moderating role of organizational commitment in the relationship between teachers’ job satisfaction and psychological well-being. The sample consisted of 317 teachers working in public and private schools. Data were collected through an online survey including measures of job satisfaction, psychological well-being, life satisfaction, and organizational commitment. Mediation, moderation, and moderated mediation analyses were conducted using PROCESS Macro (Models 4, 1, and 14) with 5,000 bootstrap resamples. Results indicated that life satisfaction significantly mediated the association between job satisfaction and psychological well-being (b = .318). Organizational commitment significantly moderated the direct effect (b = -.011), although the conditional indirect effect was not statistically significant. The findings suggest that job satisfaction influences psychological well-being primarily through life satisfaction, while organizational commitment plays a limited contextual role.

This study examined the influence of rubric layout on ratings assigned to L2 English essays and raters’ decision-making process in the context of Turkish higher education. Raters scored 24 essays in two rounds with the same analytic rubric presented in standard and reverse layouts, responded to a criteria importance survey at six time points throughout the study, and provided written score explanations to justify their ratings. The two scoring rounds were conducted eight weeks apart, with the rubric categories reversed in the second round. A two-way repeated measures ANOVA revealed significant differences in scores between the two rounds. Multivariate generalizability analyses indicated that the rubric layout influenced variability in scores across raters. Content analysis of the written score explanations revealed a primacy effect, with raters placing greater emphasis on organization and grammar when these categories appeared earlier. However, raters consistently prioritized content and organization when justifying their scores. These findings suggest that the placement of rubric categories can affect raters’ focus and scoring, emphasizing the importance of careful rubric design to ensure consistent assessment outcomes.

Recent advances in machine learning have expanded the analytical toolkit available for large-scale assessment data; however, many high-performing approaches remain limited in terms of interpretability and their suitability for assessment-informed decision-making. This study used data from 7,250 students in the Türkiye sample of PISA 2022 mathematical literacy to benchmark and interpret the Tabular Prior-Data Fitted Network (TabPFN) for classifying student achievement. For the purpose of model evaluation, mathematical literacy was operationalised as the mean of ten plausible values and then converted into a binary outcome using the PISA Level 2 proficiency benchmark, consistent with the study’s classification-oriented aim. TabPFN was compared with eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Stacking, Random Forest, and Logistic Regression under consistent preprocessing and stratified cross-validation procedures. Beyond predictive performance, feature-importance patterns were examined using Shapley Additive Explanations (SHAP) for TabPFN and permutation importance for the baseline models. The findings showed that TabPFN achieved competitive performance and produced an interpretable predictor profile. Home resources and mathematics self-efficacy emerged as the most influential predictors, followed by curiosity and classroom disciplinary climate. These results suggest that tabular foundation models can support large-scale educational assessment not only by providing accurate classifications, but also by generating interpretable evidence about achievement-related learner and contextual factors. The findings should be interpreted as classification evidence around a meaningful proficiency threshold rather than as a model of continuous mathematics achievement.

Given the importance of interdisciplinary innovation ability (IIA) in addressing complex real-world problems, this study aims to develop a comprehensive assessment framework for IIA using a three-step methodological approach. First, an initial framework was constructed based on a systematic theoretical analysis. Second, the framework was refined and validated through a two-round Delphi process involving five experts. Third, the measurement model was validated using SPSS AMOS (version 24) with data collected from 209 undergraduate students participating in a university summer camp, to examine the reliability and validity of the proposed framework. The findings indicate that IIA comprises five primary dimensions—innovative personality, innovative thinking, innovative skills, innovative learning, and innovative product—along with 12 secondary indices. Compared with existing IIA assessment frameworks, the proposed framework demonstrates stronger theoretical grounding, a more comprehensive structure, and empirical validation, thereby offering both theoretical insights and practical implications for future interdisciplinary innovation research.

This study aims to develop a valid, and reliable scale to measure middle school students' acceptance levels of flipped mathematics classrooms. The study was conducted with three study groups (n1 = 326, n2 = 721, n3 = 84) consisting of middle school students (5th-8th grades). In the scale development process, the researchers employed a sequential exploratory mixed-methods research design, following both qualitative procedures (literature review, expert opinions, and student opinions) and quantitative procedures (construct validity, nomological validity, convergent validity, discriminant validity, criterion validity, and internal consistency reliability). In the study, the results of the validity and reliability analyses of the Flipped Mathematics Classrooms Acceptance Scale (FMCAS), which consists of 38 items and 6 factors on a 5-point Likert-type scale, were found to be at the desired level. As a result, a valid and reliable scale was developed and approved for middle school students within the scope of the research.

Applied education models aim to prepare students for professional careers by bridging the gap between theoretical knowledge and practical experience. However, existing literature on these models has mostly focused on expert and institutional perspectives, with limited consideration given to students' perceptions and expectations. This study examines an applied education model implemented at an industrial university. Taking a holistic approach, the study evaluates student feedback and the opinions of academic and industry experts to fill this gap in the literature. The research used a hybrid methodological framework combining the Pythagorean Fuzzy Analytic Hierarchy Process and a decision tree algorithm. The data were obtained from 76 students and 13 academic and industry experts. The Pythagorean Fuzzy Analytic Hierarchy Process was used to determine the weights of the evaluation criteria based on the opinions of students and experts, and the decision tree algorithm was used to analyze the structural and predictive relationships between the criteria. Correlation analysis was also performed to examine the relationships between the criteria. The findings suggest that the criteria of ‘Developing a foreign language’, ‘Developing a network’, ‘Graduating with a high average’, and ‘Developing program/software skills’ significantly impact students’ five-year career planning. The results reveal that the dimensions of academic guidance, industry collaboration, and skills development form a complementary structure. This study makes a theoretical contribution to the literature on applied education by presenting a multi-criterion, data-driven analysis model that integrates student perspectives with expert evaluations to develop feasible suggestions for strengthening the alignment between higher education and industry.

This meta-analysis examines the correlation between academic self-efficacy and academic procrastination by synthesizing findings from primary studies. It aims both to present an overall correlation effect size and to identify potential moderators for variation across studies. The study is significant as it not only provides a general effect size but also explores moderator variables that may account for differences in outcomes. Journal articles, theses, and conference papers were reviewed through multiple search engines, and studies meeting the inclusion criteria were analyzed. Studies were included if a Pearson correlation coefficient between academic self-efficacy and academic procrastination was reported in an educational context, reported in Turkish or English. Forty-four primary studies reporting Pearson correlation coefficients (r) were included, representing a total sample of N = 18,330 participants. Alongside the overall effect size, analyses of publication bias and heterogeneity were carried out. There was no evidence of publication bias when it was evaluated using a variety of complimentary techniques, such as funnel plot, Begg & Mazumdar’s rank correlation test, Egger’s regression test, and Rosenthal’s fail-safe N method. Using the random effects method, the mean effect size was moderate and negative (r ̅ = −.37, 95% CI: [−.414, −.317]). Heterogeneity tests indicated significant variability across primary studies (I² = 93.03%), suggesting that moderator analyses were warranted. Among potential moderators, publication year, the proportion of female participants, and publication type did not exhibit a moderating effect, while study country and educational level significantly moderated the relationship. These factors demonstrated a moderate effect on the self-efficacy–procrastination link.

This study reports the Turkish adaptation and psychometric evaluation of the Teaching Mathematics for Social Justice Beliefs (TMfSJ-beliefs) scale with preservice teachers. A two-phase design was implemented across ten universities, comprising a pilot study (n = 133) and a main study (n = 359) with preservice teachers enrolled in primary and elementary mathematics teacher education programmes. Following international guidelines for test adaptation, we obtained permission from the original authors, applied forward-back translation, conducted expert review to examine semantic and conceptual equivalence, and carried out cognitive interviews before piloting the translated 11-item, six-point Likert-type scale. Using polychoric correlations and the WLSMV estimator, we ran exploratory factor analysis on the pilot data and confirmatory factor analysis on both pilot and main samples. Findings supported a three-factor, 11-item structure corresponding to the original subscales (Value of Mathematics for All Students, Social Justice Should Be Integrated into Mathematics Instruction, and Mathematics and Its Relation to Inclusiveness), with acceptable fit in the main sample (CFI = .93, TLI = .91, RMSEA = .08, SRMR = .07). Cronbach’s alpha and McDonald’s omega indicated adequate internal consistency for two subscales and somewhat lower, though theoretically coherent, reliability for the inclusiveness subscale. Overall, the Turkish TMfSJ-beliefs scale shows promising utility for researching preservice teachers’ beliefs about teaching mathematics for social justice, while further validation with in-service teachers and more diverse samples remains necessary.

This study aims to adapt SABR-2.2 to the Turkish language and culture. The tool was developed to evaluate the quality of preschool teachers’ book-reading activities with children. The adaptation of the SABR-2.2 was conducted in line with the theoretical and practical models and criteria. A total of 150 volunteer preschool teachers employed in preschools and kindergartens in Ankara participated in the study. According to the results, the factor loadings for 11 items ranged from .40 to .83, accounted for 46% of the total variance, and indicated a unidimensional structure. The goodness-of-fit indices obtained from the confirmatory analyses (χ2/df = 1.43, CFI = .95, GFI = .87, NNFI = .92, TLI = .92, RMSEA = .080, SRMR = .090) indicated an acceptable to good level of model-data fit. For the 11-item scale, Cronbach’s α was .81, and McDonald’s ω was .90. These values indicate that the scale works with high internal consistency. In addition, the total scores obtained from the scale seem to provide high discrimination between the lower and upper groups. In line with these findings, the scale is considered a valid and reliable instrument for evaluating teachers’ book reading practices in the Turkish culture.