This study investigated the influence of CThink4CS² Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS² Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

The objective of this study was to develop a measure that possesses both reliability and validity in order to evaluate innovative thinking within the realm of education. To achieve this, the instrument's validity and reliability were evaluated through quantitative methods in two distinct phases. A team of educational experts conducted the process of establishing content validity and ensuring that the items on the instrument accurately reflected the intended constructs of creative thinking. Following that, the assessment of concept validity was conducted using confirmatory factor analyses. The aforementioned investigations resulted in the discovery of a five-factor solution consisting of 25 elements, all of which demonstrated scores beyond the crucial threshold. This successful outcome confirmed the presence of distinct factors representing different dimensions of innovative thinking. The study enrolled 1250 students from vocational education institutions as participants. The data obtained from the participants was subjected to principal component analysis and confirmatory factor analysis, leading to the development of a model that exhibited a good match with the empirical data. This indicated the effectiveness of the developed instrument in measuring innovative thinking capacity. In conclusion, the research effectively developed an accurate and dependable tool for evaluating innovative thinking in the realm of education. The gathering of positive data from the participants and meticulous quantitative analyses were responsible for this.

Examining the academic locus of control, procrastination, and school satisfaction is crucial for understanding student well-being and educational outcomes. The purpose of this study was to explore the potential mediating role of academic procrastination in the association between academic locus of control and school satisfaction in a sample of adolescents (N = 628; X̄ = 13.30, SD = 1.74) residing in various regions of Azerbaijan. The results revealed that higher levels of internal locus of control were positively related to school satisfaction, while increased levels of external locus of control were negatively associated with school satisfaction. The analysis using structural equation modeling showed that school satisfaction was partially influenced by academic procrastination, which acted as a mediator in the relationship between internal locus of control and school satisfaction. Additionally, academic procrastination fully mediated the relationship between external locus of control and school satisfaction. These findings were further discussed in the context of existing literature, and recommendations were provided for future research in this area.

Evaluation of child-friendly school (CFS) policies is essential to determine the achievements of school efforts in reducing violence cases. This research aims to proving the reliability and validity of CFS policy evaluation instruments in elementary schools with different locations. This investigation uses the Context Input Process Product (CIPP) evaluation model to confirm the factor structure or dimensions of several observed variables (items) to evaluate the reliability and validity of the CFS policy evaluation tool in elementary schools. The validity and reliability of previously created instruments are evaluated quantitatively, but this study utilises different subjects and study sites. There were 320 respondents, with 145 school principals and 175 teachers taken randomly. Confirmatory factors analysis (CFA) results show goodness of fit (GOF), and the model is acceptable. The CFS evaluation instrument can be accepted after eliminating several question items and modifying them. All items of the teal instrument meet the goodness of fit criteria in terms of chi-square and root mean square error of approximation (RMSEA). The instrument for evaluating CFS policies in primary schools using the CIPP model has met a valid and reliable psychometric property test so that it can be applied.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.

Although regression to the mean is pervasive in data analysis, educational researchers often misconstrue it as evidence of genuine change and mistakenly attribute random changes to treatment effects. A statistical phenomenon where extreme values naturally move closer to the average after repeated treatment, regression to the mean is especially susceptible to misinterpretations in educational studies with pretest-posttest or longitudinal designs. In such studies, observed changes are frequently assumed to be the effects of treatment, even in cases where the changes are statistical artifacts. Using a hypothetical case and two real-world studies, this paper investigates the technical challenges that regression to the mean poses and introduces a hybrid Bayesian model that mitigates its effects more effectively than conventional approaches, such as multiple baseline adjustments and formulaic corrections. In particular, the hybrid Bayesian model relies on multiple baseline measurements to minimize distortions associated with regression to the mean during the pretest phase and leverages prior knowledge—such as standard deviations and population means—to refine post-test data adjustments. It follows that the model provides educational researchers with an innovative tool for accurately evaluating interventions and enhancing the effectiveness of various research-driven educational policies and practices.

This article investigates the adoption, simplification, and usage recommendations of the Indonesian Index of Learning Style Short Form (ILS-SF). The aim is to refine the initial Indonesian ILS, compare the suitability between engineering/non-engineering and high school/university, and assess their learning styles. The participants were 678 students (413 females), with an average age of 19.4±1.92 years. The methods used in this study were adopting the existing Indonesian version of ILS, simplifying–reducing the number of items, empirical verification (validity and reliability), and Indonesia data assessment. The results show that the original ILS could be simplified without sacrificing the quality of the model. On the contrary, validity and reliability measures have increased. Confirmatory Factor Analysis (CFA) supports a reduction from 44 to 15 items. It confirms the validity with favorable indices such as CFI (0.972), TLI (0.966), RMSEA (0.021), SRMR (0.049), and GFI (0.999)—Active-Reflective Cronbach's alpha at 0.507, Sensing-Intuitive at 0.590, and Visual-Verbal at 0.553. Indonesian ILS-SF is faster, simpler, more suitable for engineering than non-engineering, and more ideal for undergraduate than high school students. The analysis revealed that sensory (40.2%), active (18%), and visual (10.2%) preferences dominate among Indonesian students. This study highlights assessment tools tailored to diverse educational contexts.

Recent studies identified that faculty research productivity increased when they felt competent in conducting research. Faculty level of research competency varies due to academic training, context, country, discipline, and experiences; however, what is faculty research competency? The core competencies of faculty research are unclear; thus, the current study systematically reviewed the literature. Researchers used Boolean searches of four popular databases to identify 553 articles for first-level screening. These yielded 46 peer-reviewed journal articles for full-text analysis, six of which focused on faculty populations (40 on non-faculty). Six core components of faculty research proficiency were identified: finding and reviewing literature, planning a study, collecting and analyzing data, writing research, disseminating research findings, and managing research projects. Compared to non-faculty populations, faculty are uniquely more engaged in research project management. Researchers also identified 18 sub-competencies that will help to measure faculty research competency more reliably in the future. Finally, as the identified studies relied on self-reported measurements that may carry self-representation bias, an aspirational implication is to develop a competency-based diagnostic test for measuring faculty research competence.

Developing efficient and reliable tools for assessing early mathematical skills remains a critical priority in educational research. This study aimed to develop and validate a brief version of the Prueba Uruguaya de Matemática (Uruguayan Mathematics Test, PUMa), a digital tool to assess mathematical abilities in children aged 5 to 6. The original test included 144 items covering both symbolic (66%) and non-symbolic (34%) tasks, such as approximate number system, counting, numerical ordering (forward and backward), math fluency, composition and decomposition of numbers, and transcoding auditory-verbal stimuli into Arabic-visual symbols. Unlike most existing tools that require individual administration by trained professionals and lack cultural adaptation for Latin American contexts, PUMa is self-administered, culturally grounded, and suitable for large-scale assessments using tablets. Using a sample of 443 participants and applying parametric and non-parametric models within the framework of Item Response Theory (IRT), along with correlations with TEMA-3, preliminary evidence was generated showing that the brief version retained precision and validity. The resulting shortened tests included 69 and 73 items for the parametric and non-parametric versions, yielding a balanced representation of symbolic (56%) and non-symbolic (44%) tasks. Despite item reduction, ability scores remained highly correlated between original and brief versions (r > .90), and both brief versions demonstrated strong internal consistency (α = .94). PUMa improves upon existing assessments by combining cultural relevance, group-based digital administration, and real-time data collection, offering a scalable solution for early identification and intervention. These features support personalized educational strategies that foster cognitive and academic development from the earliest stages.