Kelley’s Discrimination Index (DI) is a simple and robust, classical non-parametric short-cut to estimate the item discrimination power (IDP) in the practical educational settings. Unlike item–total correlation, DI can reach the ultimate values of +1 and ‒1, and it is stable against the outliers. Because of the computational easiness, DI is specifically suitable for the rough estimation where the sophisticated tools for item analysis such as IRT modelling are not available as is usual, for example, in the classroom testing. Unlike most of the other traditional indices for IDP, DI uses only the extreme cases of the ordered dataset in the estimation. One deficiency of DI is that it suits only for dichotomous datasets. This article generalizes DI to allow polytomous dataset and flexible cut-offs for selecting the extreme cases. A new algorithm based on the concept of the characteristic vector of the item is introduced to compute the generalized DI (GDI). A new visual method for item analysis, the cut-off curve, is introduced based on the procedure called exhaustive splitting.

A new index of item discrimination power (IDP), dimension-corrected Somers’ D (D2) is proposed. Somers’ D is one of the superior alternatives for item–total- (Rit) and item–rest correlation (Rir) in reflecting the real IDP with items with scales 0/1 and 0/1/2, that is, up to three categories. D also reaches the extreme value +1 and ‒1 correctly while Rit and Rir cannot reach the ultimate values in the real-life testing settings. However, when the item has four categories or more, Somers’ D underestimates IDP more than Pearson correlation. A simple correction to Somers’ D in the polytomous case seems to lead to be effective in item analysis settings.  In the simulation with real-life items, D2 showed very few cases of obvious underestimation and practically no cases of obvious overestimation. With certain restrictions discussed in the article, D2 seems to be a good alternative for these classic estimators not only with dichotomous items but also with the polytomous ones. In general, the magnitudes of the estimates by D2 are higher than those by Rit, Rir, and polychoric correlation and they seem to be close of those of bi- and polyserial correlation coefficients without out-of-range values.

What are missing in the U.S. education policy of “college for all” are supporting data and indicators on K-16 education pathways, i.e, how well all students get ready and stay on track from kindergarten through college. This study creates synthetic national longitudinal education database that helps track and support students’ educational pathways by combining two nationally-representative U.S. sample datasets: Early Childhood Longitudinal Study- Kindergarten (ECLS-K; Kindergarten through 8th grade) and National Education Longitudinal Study (NELS; 8th grade through age 25). The merge of these national datasets, linked together via statistical matching and imputation techniques, can help bridge the gap between elementary and secondary/postsecondary education data/research silos. Using this synthetic K-16 education longitudinal database, this study applies machine learning data analytics in search of college readiness early indicators among kindergarten students. It shows the utilities and limitations of linking preexisting national datasets to impute education pathways and assess college readiness. It discusses implications for developing more holistic and equitable educational assessment system in support of K-16 education longitudinal database.

This work describes the design and validation of a questionnaire to assess the expectations of higher education professors regarding virtual education (CEDVES). The sample included 546 professors, 299 men (54.66%) and 247 women (45.23%), from different scientific disciplines of a university in Chile. The final version consisted of 38 items answered using a five-point Likert scale. Nine factors were identified from the exploratory factor analysis. This configuration accounts for 75% of the variance. The structure of the instrument was studied using confirmatory factor analysis. It was found that nine factors produced a good fit, derived from a hierarchical solution in which all these factors depend on a factor of second general order. Each of the scales, like the general factor, present good indicators of reliability. The analysis indicates that this questionnaire has adequate validation and could be broadly used in higher education.

The implementation of learning activities in schools has not provided opportunities or encouragement for students in developing their procedural knowledge. This research aimed to test the effectiveness of developing Bloom’s Taxonomy revision-oriented learning activities to grade IV elementary learners’ procedural knowledge capabilities and learning outcomes. This research used quasi-experiment with a quasi-experimental design which consisted of a posttest-only control design. The population of this study was sixth-grade students of 9 schools with an overall number of 229 students. The sample in the study was 50 students, there were 26 students from the experimental class and 24 students from the control class. A test method with 10 question items was used as a data collection method. The data analysis methods and techniques used were quantitative descriptive analysis and inferential statistical analysis. Then the data were analyzed using the MANOVA test assisted by the IBM SPSS Statistics 21.0 program. The hypothesis test results showed a significance value of .000 (Sig<.05). It can be concluded in procedural capabilities and learning outcomes between groups of students there is a significant difference from following learning by implementing Bloom's Taxonomy Revision oriented learning activities with the experimental and control group.

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.

As the globe gradually entered the post-pandemic phase, electronic portfolio practises during the COVID-19 pandemic should be examined for future implementation. During the lockdown, electronic portfolio use was observed in higher education institutions by urging the provision of teaching and learning in a virtual mode. Under these conditions, the study analyses empirical e-portfolio practices and proposes a co-design model for effective e-portfolio implementation. This study is based on a systematic review, which included searching for and retrieving 221 papers from academic paper databases in English, Chinese, and Spanish; systematic screening using the Rayyan tool and the PRISMA model; and finally, extracting 12 publications, which were analysed by VOS Viewer and Nvivo, focusing on collaboration. The data collected allows for gathering several patterns of collaboration in e-portfolio practice. Based on the results obtained, a co-design strategy is suggested, which includes collaborative frameworks in e-portfolio implementation processes such as the community of inquiry (CoI) and community of practice (CoP). The co-design strategy provides the formulation of implementation recommendations related to collaborative e-portfolio. Conclusions reflect on utilising e-portfolios collaboratively in higher education settings by presenting a co-design strategy that is supported by the CoI and CoP frameworks.

Many education and learning experts currently recommend the flipped classroom model as an alternative to learning after the COVID-19 pandemic. This study aims to explore the impact of the flipped classroom model on social skills and problem-solving skills for cadets. This research used a sequential mix method involving 50 maritime students in semester 7 of the Engineering Study Program at the Maritime Sciences Polytechnic Makassar, South Sulawesi, Indonesia. Researchers used two main instruments, namely problem-solving skill tests and interviews. Furthermore, in the quantitative analysis, the researcher ran paired sample t-tests and one-way Multivariate Analysis of Covariance (MANCOVA) using the SPSS 25.00 program. In addition, researchers also analysed qualitative data from interviews using thematic analysis techniques. The results showed that the flipped classroom model proved to have a positive effect on the problem-solving skills of maritime students. Other findings state that the cadets also respond positively to the flipped classroom model. Researchers recommend that teachers use the flipped classroom model, especially in dealing with learning in the post-pandemic era, like today.

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.

In the context of Vietnam's primary schools undergoing a digital transformation, this research investigates the relationship between organizational culture (OC), digital capabilities (DC), and organizational readiness (OR) for change. This survey, which employs a quantitative methodology, includes 892 teachers and school managers from different elementary schools. Analyses were conducted using SPSS Statistics 26.0. The study shows a favorable relationship between digital skills and organizational readiness, suggesting that more digitally capable institutions are better equipped to handle change. Furthermore, a significant correlation exists between corporate culture, digital skills, and organizational readiness, indicating that schools with a creative and supportive culture are more prone to embrace digital change. These results advance knowledge of the variables affecting organizational change-readiness in Vietnam's primary school digital transformation. These results also have significant implications for educational policymakers, school administrators, and other stakeholders facilitating digital transformation in primary schools. By recognizing the benefits of digital capabilities and organizational culture for organizational change readiness, decision-makers can implement strategies to foster a supportive culture and enhance digital capabilities within educational institutions, ultimately leading to more successful and effective digital transformation initiatives.

This paper explores information related to misconceptions and conceptual change during the last thirty years 1992-2022 to be used as a preliminary study in science education. This study used bibliometric analysis with the help of the Scopus database. This paper used a bibliometric analysis study with the Scopus database and the help of MS Excel, VosViewer, and Rpackage software to visualize the data obtained. The results of this research found that Indonesian researchers have contributed the most in terms of the number of documents published in Australia and the United States. Additionally, research on these two topics has decreased since 2019 due to the Covid-19 pandemic. In addition, these findings present trends in the areas of misconceptions and conceptual change that can be used as baseline data for future research. Studies related to misconceptions will continue to develop because they cannot be separated from the inside of education, whether at any level of elementary school, middle school, or college. This is an opportunity that must be taken advantage of by institutions and policies in an effort to improve and create quality of education, teacher resources, and students.

This pilot study is part of a larger “Decolonization of Digital Learning Spaces” project, which aims to develop research tools for communities that are remote and/or excluded geographically, politically, economically, socially, culturally, and linguistically. The project’s ultimate goal is to work alongside these communities to design their own digital learning tools, networks, and online educational environments by accessing and leveraging their knowledge and skills. Testing the single-criterion card sorting method is the first step toward this goal. Card sorting is an easy, enjoyable, and cost-effective method for data collection and analysis, particularly for researchers working in remote areas with limited access to electricity or the Internet. The pilot explored single-criterion card sorting as a method to elicit knowledge from two diverse cultural and linguistic groups engaged in learning activities within their communities. These groups were from a Deaf and Hard of Hearing (DHH) community in Canada (engaged in a bow-making workshop) and a rural Kabyle community in Algeria (engaged in a traditional cooking lesson). Despite low participant numbers, distinct patterns emerged, indicating the method's effectiveness. The results, though anticipated, were non-random, demonstrating the potential of card sorting in producing patterns indicative of how individuals and/or communities categorize their world(s). Kabyle sortings focused on ingredients, highlighting older individuals as teachers passing along knowledge, while the DHH sortings emphasized face-to-face contact and hand movements in communication. The findings, though modest, established relationships, provided insights into the research context and offered logistical understanding, paving the way for further work with DHH and Kabyle communities towards the design of digital learning spaces.