Jin Eun Yoo

Background: The Teacher Self-Efficacy in Multicultural Classrooms (TSMC) scale, included in the Teaching and Learning International Survey (TALIS) 2018, aims to measure teachers’ confidence in handling culturally diverse classrooms. However, its cross-cultural psychometric properties remain underexplored. This study evaluates the structural validity of the TSMC scale, focusing on its performance across different cultural and educational contexts. Methods: As a preliminary analysis, a clustering procedure was conducted on 34 countries, excluding those with excessive missing rates from the original 47 TALIS participants. Based on the results, Japan and Colombia, which represented the two extreme ends, were selected for further investigation using the Generalized Partial Credit Model (GPCM). In particular, Japan and Colombia represent two educationally and culturally contrasting contexts with differences in curriculum flexibility, institutional practices, assessment approaches, and demographic composition. After confirming the assumption of unidimensionality, this study evaluated the structural validity of the TSMC scale by examining possible floor and ceiling effects, category response curves, test information curves, and person–item maps. Results: The analysis revealed pronounced ceiling effects in Colombia and floor effects in Japan, highlighting the scale’s limited capacity to discriminate among high- and low-ability respondents. Category response curves further illustrated that the middle response options failed to capture subtle differences in teacher self-efficacy, while test information curves and person-item maps indicated biased theta values, highlighting a misalignment between the instrument's theoretical structure and its practical application, thereby compromising its structural validity. Conclusions: To address the identified limitations, the present study suggests revising the TSMC scale by expanding theoretical depth, enhancing measurement sensitivity, pilot testing & iteration, and considering countries’ sociocultural differences for a more accurate interpretation of the results. The proposed refinements could enhance the scale’s validity and cross-cultural applicability, ensuring culturally responsive and psychometrically sound assessments of teachers’ multicultural self-efficacy. The recommendations hold implications for improving large-scale educational surveys and informing policy interventions to support culturally responsive teaching worldwide.

Background: Teachers’ team innovativeness—the collective capacity of teaching teams to generate, adopt, and implement new ideas and practices—is increasingly recognized for its role in enhancing instructional methods and fostering school innovation. However, empirical research identifying its predictors remains limited, particularly within large-scale educational contexts. Prior studies often analyzed a restricted set of variables using traditional multilevel models, which may not fully capture the complexity inherent in such data structures. Methods: This study utilized data from the Teaching and Learning International Survey (TALIS) 2018, encompassing responses from 2895 Korean middle school teachers and 163 principals. To address the multilevel structure of the data and perform comprehensive variable selection, we employed glmmLasso—a penalized regression technique that integrates generalized linear mixed models with LASSO penalties. This approach allowed for the identification of important predictors of teachers’ team innovativeness across 571 explanatory variables. Additionally, a simulation study was conducted to evaluate the performance of glmmLasso in comparison to traditional LASSO and elastic net (Enet) methods regarding predictive accuracy and variable selection. Results: The application of glmmLasso resulted in a more parsimonious model, selecting considerably fewer variables while maintaining predictive performance comparable to that of LASSO and Enet. Fourteen key predictors of teachers’ team innovativeness were identified, predominantly exhibiting positive associations and encompassing factors such as teacher collaboration, self-efficacy, and supportive school climate. Notably, the variable representing shared responsibility for school issues demonstrated a negative association with team innovativeness. The simulation study further supported glmmLasso’s superior accuracy in identifying true predictors and reducing false positives, particularly at the school level. Conclusions: This study contributes both methodologically and substantively by demonstrating the efficacy of glmmLasso in analyzing large-scale, nested educational data to uncover important predictors of teachers’ team innovativeness. The findings offer actionable insights for educational policymakers and school leaders aiming to cultivate innovative teaching teams through strategies that enhance teacher collaboration, bolster self-efficacy, and foster a supportive school climate. Future research should explore the applicability of glmmLasso across diverse educational contexts and consider the development of advanced penalized regression techniques to address challenges such as the optimal selection count thresholds and the appropriate size of ICC within multilevel data structures.

The purpose of this study is to describe the development and validation process of a Scale of Competency of Digital Age Teaching (SCoDAT). The scale is intended to diagnose preservice teachers’ self-efficacy in integrating digital technologies in lesson planning, implementation, and reflection. Initial competency items were created based on literature review and prior qualitative inquiry, and were reviewed by a group of experts consisting of general and content- specific teacher education faculty members. After multiple rounds of expert reviews, a total of 21 items were developed, composed of 4 areas: lesson planning, lesson actions, student assessment, and lesson reflection. After Cronbach’s alpha and G6 values were examined for internal consistency, a second-order, four-factor Confirmatory Factor Analysis (cfa) model was fitted and evaluated with chi-square goodness-of-fit statistics and fit indices. Overall, the scale and its subscales demonstrate good internal consistency and construct validity. The SCoDAT could serve as both a formative and summative assessment for teacher educators and as a self-assessment for preservice teachers, enabling them to diagnose their professional competence in digitally equipped classrooms.

This study explores the relationship between Teacher Digital Competencies (tdc) and Teacher Self-Efficacy (tse), which affects student outcomes and academic success. With the increasing use of technology in education, the covid19 pandemic highlighted the need for effective use of technology in classrooms. This research examines if tse and other factors such as sociodemographic and school characteristics explain tdc for Korean and Colombian teachers. The study also compares tse levels before and after the pandemic. Results show that tse, sociodemographic factors, and school characteristics explain Colombian tdc, whereas only tse explains Korean tdc. Colombian teachers had higher tse than Korean teachers before and after the pandemic. While tse levels decreased for both countries, there was a surprising increase in the means for tse related to ict. This study highlights the need for further research on tse and tdc in different societies.

Background: Large-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized regression approaches, including least absolute selection and shrinkage operator (LASSO) and elastic net (Enet), have been advanced as useful tools capable of handling large number of predictors for variable selection for model generation. While the utility of penalized regression within educational research is emerging, less application of these machine learning methods, including random forest, to predictor variable selection in large-scale international data appears in the literature. In response, this study compared LASSO, Enet, and random forest for predictor variable selection, including the traditional forward stepwise (FS) regression approach, for students’ test anxiety or, more specifically, schoolwork-related anxiety based on PISA 2015 data. Methods: Prediction of the three machine learning methods were compared for variable selection of 188 indicators of schoolwork-related anxiety. Data were based on US students (N = 5593) who participated in PISA 2015. With the exception of FS, LASSO, Enet, and random forest were iterated 100 times to consider the bias resulting from data-splitting to determine the selection or non-selection of each predictor. This resulted in the reporting of number of selected variables into the following five count categories: 1 or more, 25 or more, 50 or more, 75 or more, and all 100 iterations. Results: LASSO and Enet both outperformed random forest but did not differ from one another in terms of prediction performance in 100 iterations of modeling. Correspondingly, LASSO was compared to FS in which, of the 188 predictors, 27 were identified as key indicators of schoolwork-related anxiety across 100 iterations, and 26 variables were also statistically significant with FS regression. Aligned with previous research, key indicators included personal, situational, and mathematics and reading achievement. Further, LASSO identified 28 variables (14.89%) statistically unrelated to schoolwork-related anxiety, which included indicators aligned to students’ academic- and non-academic behaviors. Conclusions: LASSO and Enet outperformed random forest and yielded comparable results in which determinants of schoolwork-related anxiety included personal and environmental factors, including achievement goals, sense of belonging, and confidence to explain scientific phenomenon. LASSO and FS also identified similar predictor variables related, as well as unrelated, to schoolwork-related anxiety. Aligned with previous research, females reported higher schoolwork-related anxiety than males. Mathematics achievement was negatively related to anxiety, whereas reading performance was positively associated with anxiety. This study also bears significance as one of the first penalized regression studies to incorporate sampling weights and reflect the complex sampling schemes of large-scale educational assessment data.

The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for International Student Assessment (PISA), we sought to identify variables related to school belonging by searching for hundreds of predictors in one model using the group Mnet machine learning technique. The study repeated 100 rounds of model building after random data splitting. After exploring 504 variables (384 student and 99 parent), 32 variables were finally selected after selection counts. Variables predicting a sense of school belonging were categorized as individual/parent variables (e.g. motivation to achieve, tendency to cooperative learning, parental support) and school-related variables (e.g. school satisfaction, peer/teacher relationship, learning/physical activities). The significance and implications of the study as well as future research topics were discussed.