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.

This study aimed to develop and validate a comprehensive core competency assessment instrument specifically designed for undergraduate students at a research-focused university. Despite growing emphasis on competency-based education (CBE), there are limited psychometrically sound tools tailored to evaluate students’ level of core competencies in research-intensive universities. The current study proceeded in three phases: (a) development of a conceptual framework comprising six core competencies: Integrated Thinking, Knowledge Inquiry, Creative Integration, Global Citizenship, Communication & Collaboration, and Self-Management; (b) item generation and expert validation; and (c) validation through exploratory and confirmatory factor analyses. The final instrument included 77 items across the six competencies. CFA confirmed adequate model fit (CFI = .934–.957; RMSEA = .057–.088). The results showed that the validated instrument can provide a reliable and comprehensive assessment for students' core competencies in research-oriented university settings. This instrument can provide guidelines for developing competency-based education (CBE) curricula in higher education, as well as criteria for evaluating and refining existing CBE programs. This instrument functions as both a psychometrically robust assessment tool and a practical guide for institutional enhancement. It enables precise measurement of students’ core competencies, offering evidence that can inform curriculum design, academic advising, and policy development. In addition, the validated framework lays a strong groundwork for future research to investigate the long-term effects of competency-based education on student achievement, career readiness, and personal development across various higher education settings.

Educational researchers, as well as researchers in other disciplines, often work with ordinal data, such as Likert item responses and test item scores. Critical questions arise when researchers attempt to implement statistical models to analyse ordinal data, given that many statistical techniques assume the data analysed to be continuous. Could ordinal data be treated as continuous data, that is, assuming the ordinal data to be continuous and then applying statistical techniques as if analysing continuous data? Why and why not? Focusing on structural equation models (SEMs), particularly confirmatory factor analysis (CFA), this article discusses an ongoing debate on the treatment of ordinal data and reports a short review on the practices of conducting and reporting SEMs, in the context of mathematics education research. The author reviewed 70 publications in mathematics education research that reported a study involving SEMs to analyse ordinal data, but less than half discussed how data were treated or guided readers through the analysis; it is therefore harder to repeat such an analysis and evaluate the results. This article invites methodological discussions on SEMs with ordinal variables in the practices of educational research. Subsequently, a standard for reporting SEMs with ordinal data is proposed, followed by an example. This standard contributes to educational research by enabling researchers (self and others) to evaluate SEMs reported. The example demonstrates, using real-life research data, how two different approaches for analysing ordinal data (as continuous or as a product of discretisation from some continuous distributions) can lead to results that disagree.

Analysis of student grades and demographic data to understand the effects of modality and Covid-19 on academic performance is important for universities to understand the impact these factors may have on course grades. This study analyzes all the 615,964 complete undergraduate student-course records from Kennesaw State University (KSU) spanning from 2015 to 2024 to examine the impact of course modality and the Covid-19 pandemic on academic performance. The population dataset includes student demographics (e.g., sex, age, ethnicity), prior GPA, and course characteristics (e.g., department, modality). Descriptive statistics and trend analyses were employed to evaluate grade outcomes across in-person, online, and hybrid modalities over the 9-year period. Results indicate a temporary increase in mean course grades during the Covid-19 period, followed by a return to the pre-pandemic upward trend. Hybrid courses consistently exhibited the highest mean grades throughout the study period. However, consistent patterns across modalities, demographics, and academic units suggest that these factors have limited influence on grade outcomes. These findings raise questions about the reliability of GPA and course grades as indicators of learning success across different instructional contexts and student populations.

Despite progress made in recent years, women continue to be underrepresented in academic publishing. We aim to share insights from academic women researchers who participated in the Training Needs Assessment for developing their writing for publication skills in an Open Distance Learning institution in South Africa. The research questions that guided the study were: a) What specific challenges do academic women researchers face in developing research and publishing skills? b) What motivated academic women researchers to participate in a writing project? c) What type of support do academic women researchers identify as essential for advancing their research and publishing skills? The data were collected through an initial face-to-face meeting, followed by a Training Needs Assessment from eight purposively chosen participants in a case study design. The findings indicate participants’ challenges of time constraints, lack of confidence, and knowledge as obstacles that hindered their publishing. Despite their challenges, women researchers reported their motivation to participate in the writing project for career advancement, personal development, academic recognition, and their inspiration to publish their research work. The study found that women researchers required writing support, peer collaboration, mentorship, and institutional support to improve their writing for publishing skills. Supporting academic women researchers with focused training, engaging them in collaborative networks, and developing gender-sensitive policies is crucial for promoting equity, breaking down barriers, and ensuring their academic and professional success.

This study applies explainable machine learning to identify which vocal attributes in a lecturer’s speech influence students’ views of a lecturer’s charisma, a key contributor to teaching quality. It further explores whether vocal qualities differ between male and female lecturers and how students of different genders respond to these differences, offering insights into voice-related factors that influence the impact of educators. Speech segments from YouTube videos featuring 200 native-English lecturers were evaluated by 900 students using charisma rating scales. A set of attributes related to three primary prosodic dimensions of voice - pitch, rhythm, and loudness - was computed. A random forest classifier was employed to predict the charisma level based on the speech attributes and to list and rank the attributes that contributed most to the prediction. The findings revealed prominent vocal attributes that achieved higher charisma scores in the students' ratings. Same-gender evaluations of charisma were mainly based on pitch, while cross-gender evaluations rely mostly on loudness or rhythm. The automated, interpretable method provides a reliable and efficient way to measure vocal charisma in academic lecturers. It can be adapted to examine additional individual factors that influence the perception of a lecturer’s charismatic presence. It may also be integrated into practice-based tools, designed to support instructors in improving their presentation skills. Our research bridges the fields of applied psychology and computer science to contribute to the development of educational technology.

Within the context of investigating belief systems, this work exemplifies a mixed-method approach. Two approaches are utilized to explore the philosophical, ontological, and epistemological assumptions that university students foster regarding the relationships between knowledge and reality. In the first step, written materials that elaborated on the matter at hand were subjected to content analysis with the assistance of Leximancer, a software that recognizes themes and concepts and turns textual data into concept maps that express networks of meaning. The second step involved conducting a cluster analysis on the data obtained from the questionnaire to identify distinct groups of participants who shared consistent epistemological viewpoints. The results obtained from the two approaches are in agreement and shed light on the prevalent epistemic inclination that favors a constructivist viewpoint. Discussion on the ramifications of the findings, as well as the methodological issues that are pertinent to the present illustration, is provided.

In recent years, significant developments have occurred in AI-based chatbots that have been effectively deployed in the educational field. However, given the novelty of this technology, descriptive analyses remain scarce. Although many review studies have focused on the effectiveness of chatbots, they generally present broad results, and only a few have addressed the impact of this technology on learning outcomes. The present study examines the educational implications of AI chatbots on various learning outcomes through a post hoc analysis conducted in accordance with PRISMA principles. It aims to aggregate and analyze findings from studies that examined the use of chatbots and their impact on specific learning outcomes. A total of 26 studies were selected from a pool of 6,721 published between 2021 and 2024 and indexed in the Scopus and Web of Science databases. Data analysis was conducted using the Newcastle-Ottawa Scale (NOS) for Education. The results revealed that AI-chatbot technology has a positive influence on several learning outcomes, including academic achievement, motivation, self-assessment, engagement in learning, self-efficacy, and language learning, among others. The studies also detailed the methodologies and tools employed in these investigations. The study also offers insights into how intelligent chatbots can be leveraged to enhance various learning outcomes.

Vocational education plays a pivotal role in nurturing talent and supporting national development. However, challenges such as outdated talent development concepts, insufficient teacher training, and a lack of attention to soft skills cultivation from both schools and students have hindered the comprehensive development of secondary vocational students. This study aims to explore the direct effect of perceived teachers’ transformational leadership on the soft skills of 324 secondary vocational students in China and to examine the mediating role of students’ self-efficacy in this relationship. Using Structural Equation Modeling (SEM), the results show that teachers’ transformational leadership has a significant positive effect on students’ soft skills (β = 0.33, p < .01). Moreover, self-efficacy partially mediates this relationship (indirect effect β = 0.07, p < .05), accounting for 22.6% of the total effect. These findings suggest that teachers’ inspirational motivation, individualized consideration, and intellectual stimulation directly foster students’ communication, teamwork, and problem-solving skills, while also indirectly strengthening them by enhancing students’ confidence. Practically, the study underscores the need for teacher training in transformational leadership and for policy initiatives that integrate soft skills into vocational curricula.