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.

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Statistical literacy, which is the ability to use statistics in daily life, is an essential skill for facing society 5.0. This study aims to explore first-year university students’ ability to properly use simple descriptive statistics and data visualization. Qualitative data were collected using a set of questions from 39 undergraduate students. Many students were able to calculate various descriptive statistics, but some of them were still unable to determine suitable statistics to describe the data clearly. Related to data visualization, many students failed to provide a meaningful chart that effectively shows the difference between two groups of data. Students with higher statistical literacy tend to use comparison or variability reasoning to determine the usage of descriptive statistics, and use data-based reason in visualizing the data. Improvement in statistical teaching – both in the university and the secondary school – is needed so that the students can use descriptive statistics and data visualization correctly.

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.

Literasi statistika merupakan salah satu kompetensi yang harus dimiliki setiap orang guna menghadapi era Revolusi Industri 4.0. Di jenjang pendidikan dasar dan menengah, materi atau kajian mengenai statistika diberikan sebagai bagian dari mata pelajaran matematika. Penelitian ini bertujuan untuk mengetahui sejauh mana muatan literasi statistika didukung oleh mata pelajaran Matematika dari tingkat Sekolah Dasar (SD), Sekolah Menengah Pertama (SMP), hingga Sekolah Menengah Atas (SMA) secara konkret melalui buku teks pelajaran yang digunakan. Proses pengumpulan data dilakukan dengan analisis isi terhadap dokumen Kompetensi Dasar mata pelajaran matematika sesuai Kurikulum 2013 Revisi 2016 serta buku-buku pelajaran matematika yang mengacu pada Kurikulum 2013 Revisi 2016 khususnya yang diterbitkan oleh Kementerian Pendidikan dan Kebudayaan Republik Indonesia. Berdasarkan penelitian ini, diketahui bahwa sebagian besar kompetensi literasi statistika telah termuat dalam buku teks matematika Kurikulum 2013 pada jenjang SD, SMP, dan SMA. Beberapa perubahan masih dapat dilakukan guna meningkatkan keluasan dan kedalaman literasi statistika. Analysis of statistical literacy content in Curriculum 2013 mathematics textbookAbstractStatistical literacy was one of the competencies that must be mastered by everyone to face the 4.0 Industrial Revolution era. At the level of primary and secondary education, statistics was studied as a part of the mathematics subject. In this research. We examine the extent to which statistical Literacy contents were supported by Mathematics subjects from the Elementary School (SD) level, Junior High School (SMP) level, to the Senior High School (SMA) level. This research focused on the textbooks used in the learning process. The data were collected by analyzing the contents of the mathematics curriculum document (following the Curriculum 2013 Revision Edition of 2016) as well as mathematics textbooks referring to the Curriculum 2013 Revision Edition of 2016 especially books published by the Ministry of Education and Culture of the Republic of Indonesia. Based on this research, it is known that the majority of statistical literacy competencies have been supported by the Curriculum 2013 mathematics textbook at SD, SMP, and SMA level. Some improvements could still be made to increase the breadth and depth of statistical literacy.