Shifting students to a growth mindset can increase their achievements. Nevertheless, only a few studies have been conducted on this topic in developing countries. This study aims to examine the relationship between growth mindset, school context, and mathematics achievement in Indonesia. Using a multilevel model on the PISA 2018 data, this study explored the variables that contributed to mathematics achievement. The multilevel analysis showed that students’ gender, growth mindset, index of economic social, and cultural status were statistically significant predictors of students’ mathematics achievement. Girls have been reported to have a higher mathematics achievement than boys in Indonesia. As the students’ growth mindset increases, so do their mathematics achievement.

Having health insurance is an important decision for enjoying the security of a safe future. Health insurance can protect people from a large amount of medical costs. More Indonesian have health insurance now a days than ever before since the government is committed to supporting the universal health care. This study aims to determine factors affecting the health insurance ownership and to understand their relations in Indonesia using a binary logistic regression model. The data used in this study came from the fifth wave of the Indonesian Family Life Survey (IFLS). There were 29,508 respondents where 14,653 among them have health insurance. The logistic regression model suggested that job, education, chronic condition, marital status, and inpatient care were statistically significant to the health insurance ownership (yes/no), while not significant for gender and health condition. Fitting the logistic regression model with age as the only explanatory variable yielded that the probability of having health insurance increased in line with age.

Genomic selection (GS) is revolutionizing plant breeding since the selection process is done with the help of statistical machine learning methods. A model is trained with a reference population and then it is used for predicting the candidate individuals available in the testing set. However, given that breeding phenotypic values are very noisy, new models must be able to integrate not only genotypic and environmental data but also high-resolution images that have been collected by breeders with advanced image technology. For this reason, this paper explores the use of generalized Poisson regression (GPR) for genome-enabled prediction of count phenotypes using genomic and hyperspectral images. The GPR model allows integrating input information of many sources like environments, genomic data, high resolution data, and interaction terms between these three sources. We found that the best prediction performance was obtained when the three sources of information were taken into account in the predictor, and those measures of high-resolution images close to the harvest day provided the best prediction performance.

Health status of a population plays an important role in developing a country. A better health can promote economic growth and foster development of the country. The aim of this study was to investigate relationships among age, sex, weight, height, smoking behavior, and blood pressure on health status of adults in Indonesia. The path analysis was constructed using the secondary data of the fifth wave of the Indonesian Family Life Survey in 2014/2015. This survey was a large national survey with representing about 83% of the Indonesian population. The sample comprised 24,263 adults aged older than 17 years. The hypothesized model suggested that age, sex, weight, height, and smoking behavior had an effect on blood pressure and that all variables influenced health status. All path coefficients were statistically significant. The age, gender, and weight variables had positive relationships with blood pressure while in the opposite direction to the height and smoking behavior. The blood pressure, age, and smoking behavior had negative relationships with health status while in the opposite direction to the sex, weight, and height. Short male respondents who ever smoked and had high blood pressure were reported to have poor health status as age increased and weight decreased.

Genomic selection (GS) is a predictive methodology that is revolutionizing plant and animal breeding. However, the practical application of the GS methodology is challenging since a successful implementation requires a good identification of the best lines. For this reason, some approaches have been proposed to be able to select the top (or bottom) lines with more Precision. Despite the varying popularity of methods, with some being notably more efficient than others, this paper delves into the fundamentals of these techniques. We used five models/methods: (1) RC, known as the Bayesian Best Linear Unbiased Predictor (GBLUP); (2) R, which is like RC but uses a threshold; (3) RO, Regression Optimum, that leverages the RC model in its training process to fine-tune the threshold; (4) B, Threshold Bayesian Probit Binary model (TGBLUP) with a threshold of 0.5 to classify the cultivars as top or non-top; (5) BO is the TGBLUP but the threshold used is an optimal probability threshold that guarantees similar Sensitivity and Specificity. We also present a benchmark comparison of existing approaches for selecting the top (or bottom) performers, utilizing five real datasets for comprehensive analysis. For methods that necessitate a rigorous tuning process, we suggest a streamlined tuning approach that significantly decreases implementation time without notably compromising performance. Our analysis revealed that the regression optimal (RO) method outperformed other models across the five real datasets, achieving superior results in terms of the F1 score. Specifically, RO was more effective than models R, B, RC, and BO by 60.87, 42.37, 17.63, and 9.62%, respectively. When looking at the Kappa coefficient, the RO model was better than models B, BO, R, and RC by 37.46, 36.21, 52.18, and 3.95%, respectively. In terms of Sensitivity, the RO model outperformed models B, R, and RC by 145.74, 250.41, and 86.20, respectively. The second-best model was the model BO. It is important to point out that in the first stage, the BO and RO approaches train a classification and regression model, respectively, to classify the lines as the top (bottom) or not the top (not the bottom). However, both the BO and RO approaches optimize a threshold in the second stage to perform the classification of the lines that minimize the difference between the Sensitivity and Specificity. The BO and RO methods are superior for the selection of the top (or bottom) lines. For this reason, we encourage breeders to adopt these approaches to increase genetic gain in plant breeding programs.

How School Culture and Climate Mediated Student’s Mathematics Achievement: a Path Analysis of PISA 2022 Indonesia Data. Indonesia’s declining mathematics performance in PISA 2022 highlights the need to examine key educational factors affecting student achievement. In the post-pandemic context, school culture and climate have become increasingly important in shaping learning outcomes. This study investigates how these factors mediate the effects of teacher support, gender, and socioeconomic and cultural status (ESCS) on students’ mathematics achievement. Objectives: This study aims to: (1) examine the effects of teacher support, gender, and socioeconomic and cultural status on school climate and culture indicators (bullying victimization, sense of belonging, feeling safe, and school safety risks); (2) assess the influence of these indicators on students’ mathematics achievement; (3) analyze the direct effects of the independent variables on mathematics achievement; and (4) evaluate the mediating role of school climate and culture in these relationships. Methods: This quantitative study used cross-sectional data from the PISA 2022 Indonesia dataset, comprising 12,491 students from 410 schools. Data were analyzed using a parallel multiple mediation model with path analysis, employing Maximum Likelihood estimation and nonparametric bootstraping with 1,000 replications. Findings: The results showed that all independent variables significantly affected all four indicators of school culture and climate. Bullying victimization, lack of feeling safe, and school safety risks had significant negative effects on math achievement, while a sense of school belonging had a significant positive effect. Teacher support and gender had significant negative effects on mathematics achievement, while socioeconomic and cultural status had significant positive effects. All mediating variables significantly mediated the relationships between the independent variables and math achievement. Conclusion: Interventions to improve math achievement should not rely solely on direct teacher support, but also prioritize building a positive school climate by strengthening students’ sense of belonging and reducing bullying as these are critical pathways influencing academic success. Keywords: PISA 2022, mathematics achievement, path analysis, mediation model.