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.

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 research on ethnomathematics has been widely carried out, there is still little empirical evidence reporting the mapping and trends of ethnomathematical research in Scopus-indexed journals. The purpose of writing this article is to map trends in ethnomathematical research in Scopus-indexed journals in the range from 1986 to 2022. The fundamental tenet of ethnomathematics is to study, appreciate, and exclusively take into account the application of mathematics by ethnic groups. In other words, in understanding mathematics to look attractive, it is necessary to make a connection between everyday life and the realm of abstract mathematics. Scopus is a resource for further research, identifying scientific uniqueness and gaps. This study uses bibliometric analysis methods using VOSviewer, article searches using Publish or Perish (version 8), and SEforRA applications. Bibliometric research provides a reliable and impartial way to gauge the contribution of an article to knowledge advancement and is a tool that is often used to analyze trends and performance on certain subjects. These findings reveal an increasing trend in ethnomathematical research and identify some topics or keywords that could form the basis for additional studies. In conclusion, for future studies, bibliometric analysis will provide information and knowledge about the evolution of ethnomathematical research.

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.

There are studies in the learning management literature examining the measure of system usage, but few explore how users apply the software tools to achieve specific work tasks, which in turn leads to perceived benefits. In the context of distance education, this study focuses on how Learning Management Systems (LMS) are fully used by faculty for their instructional needs. It extends existing research on LMS adoption by investigating how faculty members or instructors use the LMS tools for effective class teaching to achieve educational outcomes. Four usage patterns were identified: communication, content management, assessment, and class management. A model is presented to examine how these usage patterns interplay to achieve the perceived benefits. Data were collected from 544 instructors using LMS, such as Blackboard Learn, etc. Structural equation modeling using LISREL was employed to assess the research model. The results suggest that the usage for communication, content, and assessment activities positively impacts the usage for class management. In turn, the usage for class management influences the net benefits perceived by the instructors, and the usage for content also impacts perceived net benefits directly. These results provide practical guidelines for LMS developers’ design improvements and institutions’ policies, such as training instructors to fully utilize LMS features to achieve the maximum benefits of distance education.

This paper explores the potential of Eye Movement Miscue Analysis (EMMA) as a method to evaluate the complexity of picturebooks as reading material for primary school children. While EMMA has been applied to examine reading processes and strategies, this paper reports on the first study using EMMA to examine classroom picturebook complexity and its implications for developing readers. This research found EMMA method revealed specific nuances for understanding children’s reading practices in response to the complexity of the text at hand. Drawing together an internationally established reading teaching resource, the text complexity guide (Pinnell & Fountas, 2007) with miscue analysis reading assessment and eye movement technology, this research sought to gain insights into potential areas of complexity or challenge in picturebooks commonly available in Australian school libraries and classrooms. The method shared here examines text complexity ratings, children’s reading performance, and eye movements, as they read in natural classroom settings. Analysis of children’s reading miscues revealed that readers encountered challenges not anticipated through the use of the text complexity guide. Argued in this paper is that EMMA methodologies could extend understandings about text complexity beyond established frameworks and hence guide future assessments.