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.

Pedagogical content knowledge (PCK) consists of a set of understandings, knowledge, skills, and dispositions necessary for effective performance in specific teaching and learning situations. Using Scopus, EBSCO, and Web of Science databases, the study examines the progress of the PCK in science teacher education between 2011 and 2021. In total, 59 articles were reviewed, and 13 were selected according to the inclusion criteria. Among the findings, it stands out that the articles emphasize a series of tools used when teaching applied sciences, such as the use of educational technologies beyond the textbook or the integration of students' thinking. The articles state that PCK transcends subject knowledge and leads to subject knowledge for teaching. Finally, the literature has tried to answer how science teachers use PCK in the classroom, demonstrating strategies and practical value, both of which are vital for the functioning and application of their educational work.

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.

This research article focuses on the design and validation of a questionnaire to analyse future teachers' perceptions of professional skills through the use of Augmented Reality (AR) in higher education, specifically for students in the field of Educational Sciences. The sample consisted of 575 students of Early Childhood Education, Primary Education and Pedagogy during the academic year (2021/2022). The focus of this study is to authenticate a questionnaire that measures the influence of Augmented Reality (AR) on aspects such as situated learning, motivation, and the necessary instructional preparations for the successful integration of AR within classroom educational encounters. The questionnaire is an online Likert-type scale developed based on three dimensions: situated learning, motivation and training. The data were analysed using the Statistical Package for the Social Sciences (SPSS) version 25 and JASP 0.17.1. The questionnaire met the standards recommended for validation. However, improvements to the instrument are suggested. In conclusion, validation of instruments is necessary to gain a rigorous understanding of the impact of new learning environments.

Education systems worldwide have been significantly disrupted due to the COVID-19 pandemic, creating an immediate need for a revamp of conventional teaching and learning techniques. To explore how this has affected the educational landscape, a scoping review was conducted. This scoping review aimed to examine the changes that occurred in the education field and to explore how it has transformed the educational landscape review. Using Arksey and O'Malley's methodology, 51 articles were selected for analysis from two leading databases: Scopus and Web of Science. All chosen articles were then subjected to thematic analysis. Three main aspects impacted by this global event were uncovered, which are technological advancements and digital transformation, changes in pedagogy and teaching methods, and mental health and well-being issues. This scoping review provides valuable insights into one of the most critical sectors affected by COVID-19, which can assist with planning future strategies for similar crises.

Mathematical modeling offers a promising approach to improving mathematics education. This study aims to determine if the concept of metacognitive awareness in the learning process is associated with mathematical modeling. This study also considers the interaction effect of sex and academic year level on both variables. Focusing the study on preservice elementary teachers might address potential issues and targeted intervention in their preparation program concerning their ability to teach and guide young learners in modeling activities. The research sample includes 140 preservice elementary teachers at Central Luzon State University, Philippines. Data collection used an adapted metacognitive awareness inventory and a validated researcher-made mathematical modeling competency test aligned with the K-12 mathematics curriculum in the Philippines. Results revealed that the preservice elementary teachers had a high metacognitive awareness and mathematical modeling competency, ranging from 22 to 31 out of 36 points. Besides, Factorial ANOVA indicates that academic year level positively affects both variables regardless of sex, and stepwise regression analysis unveiled that information management, declarative knowledge, and planning significantly predict 41.4% of the mathematical modeling competency variance. This suggests that developing metacognitive awareness supports preservice elementary teachers in performing modeling tasks that improve their competency level in mathematics.

The main thrust of the study was to explore the third-year English student’s sentiments on the application of the artificial intelligence (AI)-integrated paraphrasing tools. Specifically, it aimed to investigate the benefits and drawbacks brought by AI paraphrasing tools towards the writing skills of the students. The investigation utilized a semi-structured interviews with an open-ended questionnaire using an audio-video recorder. The data gathered were interpreted using the Thematic Analysis of Braun and Clarke. The study was carried out at one of the campuses of a state university located at Candijay, Bohol, Philippines. Using the Purposive sampling technique, twelve (12) respondents provided information on the research endeavor. The findings revealed that students had a positive opinion of AI-integrated paraphrase tools: they saw them as helpful resources that significantly improve their academic writing process; it includes plagiarism reduction, efficiency, and timesaving, and aids in rephrasing text. The findings also revealed the risks and issues of using AI-integrated paraphrasing tools, such as Prone to plagiarism, automated suggestions dependency, and loss of original meaning and context. With that, the students showed how they deal with those risks and issues, including as responsible users and thorough editing and reviewing. In accordance with the study, students are encouraged not to rely excessively on AI-integrated paraphrase tools, even though they can improve their writing abilities. This research emphasizes that students play a pivotal role in ensuring the appropriateness of texts generated by AI-integrated paraphrasing tools by mastering the art of proper paraphrasing.

This study examines the impact of digital tools on fraction comprehension among 5th-grade students with learning difficulties in mathematics. It assesses the effectiveness of three teaching methods: educational software, video tutorials, and their combination. The research involved 252 students from 8 state-funded elementary schools, employing a quantitative experimental design with pre- and post-test assessments. Grounded in Constructivist Learning Theory and Technological Pedagogical Content Knowledge (TPACK), this research explored how technology can enhance mathematical understanding. Results indicated that the combined method achieved the highest improvement (58%, p < .001, Cohen’s d = 3.03), significantly outperforming educational software alone (33%, p = .015, Cohen’s d = 2.52) and video tutorials alone (7%, p = .987, Cohen’s d = 0.14). These findings highlight the substantial benefits of integrating diverse digital tools to effectively support mathematics learning among students facing additional educational challenges.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

Self-report surveys are extensively utilized in educational research to understand students’ perceptions and experiences. However, younger children, particularly those in elementary school, may exhibit a tendency to provide socially desirable responses, potentially compromising the data quality. This study examined the prevalence and impact of socially desirable responses in self-report surveys administered to elementary school students. A total of 1,024 students from grades 4 and 5 in five elementary schools participated in the study. Socially desirable responses were measured using detection items embedded within questionnaires. The findings indicate that (a) more than 20% of elementary school students demonstrated socially desirable responses; (b) female students and those with higher academic achievement were more likely to provide socially desirable responses; (c) socially desirable responses skewed the sample distribution by inflating mean scores and reducing standard deviations; and (d) while internal correlations within scales remained relatively stable, external validity, as reflected in correlations between self-reports and academic performance metrics, was significantly affected after adjusting for socially desirable responses. These results underscore the importance of addressing socially desirable responses when interpreting self-report data from young students. The study concludes with practical recommendations for improving the validity of self-report surveys in educational research.

The personalization of learning and teaching processes represents an advanced approach to education that adapts content, pace, and teaching methods to the individual needs and preferences of students. This approach relies on analyzing diverse student characteristics, such as their knowledge level, progress, learning style, and interests. Achieving these goals is significantly supported by the use of information and communication technology, which facilitates and enhances the implementation of personalization in technology-enhanced learning (TEL). The primary objective of personalization is to increase student engagement, motivation, and support in achieving learning outcomes through individualized learning paths, real-time progress tracking, and feedback. This systematic literature review examines existing personalization approaches in secondary and higher education, supported by technology. The study investigates their effectiveness and provides recommendations for future research. Results reveal that personalized teaching methods—primarily through recommender systems, adaptive learning platforms, and algorithm-driven models—are effective in tailoring educational experiences by leveraging diverse student data, such as demographics, prior achievements, learning styles, and digital engagement. The review shows a predominant focus on higher education, particularly in subjects related to computer science and digital technologies. Quantitative evaluations complemented by qualitative insights, consistently indicate that personalization enhances content mastery, motivation, and overall satisfaction, with no significant negative effects identified.