Digital competency mapping dataset of pre-service teachers in Indonesia.

This dataset used the Digital Competency Scale (DCS) to describe Indonesian pre-service teachers' perceptions. The DCS instrument consisted of five constructs/dimensions, which are: 1) data and information literacy, 2) communication and collaboration, 3) digital content creation, 4) safety, and 5) problem-solving, with a total of 36 items using five-point agreement Likert scale. The data was gathered from 23 education and teacher training faculties at Muhammadiyah Universities in 14 provinces across Indonesia in the academic year 2021/2022. A total of 1400 students (18 to 23 years old) in their first to fifth years of study were recruited using the convenience sampling technique, where they participated in filling in the survey electronically using Google Form. The dataset was analysed with the Rasch model measurement approach using WINSTEPS version 5.2.3 software for data cleaning and validation, and reliability and validity testing of the instrument. This dataset analysis can help teacher-training institutions, or higher education policymakers design effective programmes to improve pre-service teachers' digital competencies. Furthermore, researchers can compare this dataset with more rigorous data from other countries.

The relationship between school climate and students' non-cognitive skills: A systematic literature review.

The school climate plays a substantial part in student development. A positive and nurturing school climate encourages the growth of all-rounders and holistic individuals. Past literature has highlighted several domains related to the school climate, including academic performance, well-being, student engagement, attendance in school, delinquent behaviors, bullying, and school safety. However, little is known about the development of other related domains, like non-cognitive skills, in school. The purpose of the study is to review the linkages of school climate with the development of students' non-cognitive skills. The current study employs a systematic literature review that adheres to PRISMA to determine the association between school climate and students' non-cognitive skills. First, this study conducted three stages of rigorous and systematic searching: identification, screening, and eligibility. As a result of the search, this study identified 65 relevant articles from Scopus, Web of Science, and ScienceDirect as the leading databases and Google Scholar and Dimension. ai as supporting databases. Next, the current study highlights five clusters based on the analysis of network visualization by the VOSviewer software. These clusters are: the non-cognitive skills' intrapersonal and interpersonal key characteristics, a nurturing school climate and the presence of non-cognitive skills mitigate deviant behaviour in school settings, the non-cognitive skills as a predictor of academic outcomes, the multifaceted antecedents of school climate in promoting the development of student non-cognitive skills, the role of school domains and non-cognitive attributes towards students' cognitive development. This systematic literature review contributes a novel framework and an in-depth understanding of the relationship between school climate and students' non-cognitive skills. The current research serves as a starting point for future researchers to delve deeper into this subject matter to provide educational organisations with valuable guidance when navigating for better educational outcomes. In addition, the current review will shed light on the school climate and students' non-cognitive skills to further examine what has already been learnt and the missing links, contributing to the body of knowledge on this topic.

Mental health condition of vocational high school students during COVID-19 pandemic in Indonesia.

This study aimed to determine the mental health condition of vocational high school students in Indonesia during the pandemic using the Rasch analysis method with the DASS-42 instrument. A total of 1381 vocational students in Indonesia participated in this study through the questionnaire. The results showed that more than 60 % of Indonesian vocational students experienced mental health problems during the COVID-19 pandemic due to social restrictions, and online learning. Furthermore, the findings of this study showed that mental health issues were mostly experienced by female students, firstborn children, and students who live in rural areas, and from middle-income backgrounds.

Measuring changes in hydrolysis concept of students taught by inquiry model: stacking and racking analysis techniques in Rasch model.

This research aimed to employ stacking and racking analysis techniques in the Rasch model to measure the hydrolysis conceptual changes of students taught by the process-oriented guided inquiry learning (POGIL) model in the context of socio-scientific issues (SSI) with the pretest-posttest control group design. Such techniques were based on a person- and item-centered statistic to determine how students and items changed during interventions. Eleventh-grade students in one of the top-ranked senior high schools in the eastern part of Indonesia were involved as the participants. They provided written responses (pre- and post-test) to 15 three-tier multiple-choice items. Their responses were assessed through a rubric that combines diagnostic measurement and certainty of response index. Moreover, the data were analyzed following the Rasch Partial Credit Model, using the WINSTEPS 4.5.5 software. The results suggested that students in the experimental group taught by the POGIL approach in the SSI context had better positive conceptual changes than those in the control class learning with a conventional approach. Along with the intervention effect, in certain cases, it was found that positive conceptual changes were possibly due to student guessing, which happened to be correct (lucky guess), and cheating. In other cases, students who experienced negative conceptual changes may respond incorrectly due to carelessness, the boredom of problem-solving, or misconception. Such findings have also proven that some students tend to give specific responses after the intervention in certain items, indicating that not all students fit the intervention. Besides, stacking and racking analyses are highly significant in detailing every change in students' abilities, item difficulty levels, and learning progress.

Online Mental Health Survey for Addressing Psychosocial Condition During the COVID-19 Pandemic in Indonesia: Instrument Evaluation.

BACKGROUND: Regular monitoring of the pandemic's psychosocial impact could be conducted among the community but is limited through online media. This study aims to evaluate the self-rating questionnaire commonly used for online monitoring of the psychosocial implications of the coronavirus disease 2019 (COVID-19) pandemic. METHODS: The data were taken from the online assessment results of two groups, with a total of 765 participants. The instruments studied were Self-Rating Questionnaire (SRQ-20), post-traumatic stress disorder (PTSD), and Center for Epidemiological Studies Depression Scale-10 (CESD-10), used in the online assessment. Data analysis used Rasch modeling and Winsteps applications. Validity and reliability were tested, and data were fit with the model, rating scale, and item fit analysis. RESULTS: All the scales for outfit mean square (MnSq) were very close to the ideal value of 1.0, and the Chi-square test was significant. Item reliability was greater than 0.67, item separation was greater than 3, and Cronbach's alpha was greater than 0.60; all the instruments were considered very good. The raw variance explained by measures for the SRQ-20, PTSD, and CESD-10 was 30.7%, 41.6%, and 47.6%, respectively. The unexplained eigenvalue variances in the first contrast were 2.3, 1.6, and 2.0 for the SRQ-20, PTSD, and CESD-10, respectively. All items had positive point-measure correlations. CONCLUSION: The internal consistency of all the instruments was reliable. Data were fit to the model as the items were productive for measurement and had a reasonable prediction. All the scales are functionally one-dimensional.

Training and Assessing Model for the Ability of Community Health Volunteers in Anthropometric Measurement Using the Rasch Stacking and Racking Analyses.

Background: Inappropriate anthropometric measurements of infants and toddlers lead to a misclassification in nutritional status and loss of important interventions. Considering the practice conducted in this program within a country, its impact on millions of children must be considered. This study assesses the ability of community health volunteers (CHVs) before and after anthropometric training on infants and toddlers. Methods. This study used a quantitative approach with a quasiexperimental and pretest-posttest design. The pre- and posttraining assessments of CHVs were conducted by standardized trainers using instruments developed according to WHO standards. There were 11 and 13 statement items for infants' and toddlers' indicators of assessment in anthropometric measurements, respectively. The result of the assessment was then analyzed using Rasch modeling with stacking and racking data analysis techniques. Results: The CHVs' skills before training were far from adequate. Although widely varied, all trainees improved their abilities. Stacking analysis showed that the skills of all CHVs in measuring infants and toddlers increased by 2.68 and 3.34 logits (p < 0.01), respectively. Racking analysis showed a decrease in the perceived difficulty of all items by 2.61 and 3.07 logits for infant and toddler measurements, respectively (p < 0.01). The results of the racking analysis showed that the difficulty in measuring the anthropometrics of infants decreased more than that of toddlers. Conclusions: CHVs' capacity to monitor child growth must be refreshed regularly. Standardized and proper training and assessment were developed to make CHVs reliable in taking anthropometric measurements of infants and toddlers.

A Framework for Assessing High School Students' Statistical Reasoning.

Based on a synthesis of literature, earlier studies, analyses and observations on high school students, this study developed an initial framework for assessing students' statistical reasoning about descriptive statistics. Framework descriptors were established across five levels of statistical reasoning and four key constructs. The former consisted of idiosyncratic reasoning, verbal reasoning, transitional reasoning, procedural reasoning, and integrated process reasoning. The latter include describing data, organizing and reducing data, representing data, and analyzing and interpreting data. In contrast to earlier studies, this initial framework formulated a complete and coherent statistical reasoning framework. A statistical reasoning assessment tool was then constructed from this initial framework. The tool was administered to 10 tenth-grade students in a task-based interview. The initial framework was refined, and the statistical reasoning assessment tool was revised. The ten students then participated in the second task-based interview, and the data obtained were used to validate the framework. The findings showed that the students' statistical reasoning levels were consistent across the four constructs, and this result confirmed the framework's cohesion. Developed to contribute to statistics education, this newly developed statistical reasoning framework provides a guide for planning learning goals and designing instruction and assessments.