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.

The process e-portfolio is a type of e-portfolio that helps students construct knowledge and familiarise themselves with their learning process through self-and peer assessment. Lecturers and students experienced difficulties when using the e-portfolio because Mahara 2019 was not updated. This qualitative research study proposes how lecturers should design e-portfolios for learning through formative assessment activities. Interpretivism was the researcher's standpoint, aiming to interpret how the study participants used e-portfolios for teaching and learning through formative assessment activities. This exploratory case study used semi-structured interviews and an e-portfolio checklist for data collection. It explored the use of the e-portfolio for formative assessment through the experiences of ten purposefully sampled lecturers. The findings suggest that an e-portfolio facilitates teaching and learning in open distance e-learning because it enables online delivery of the content and administering of assessments that afford students' learning of the module content through completing formative assessment activities. The e-portfolio facilitates co-teaching and co-learning because students become knowledge creators and active users instead of passive learners. This study recommends the use of process e-portfolios to facilitate assessment and learning in open-distance e-learning institutions.

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.

This scoping review comprehensively explores how artificial intelligence (AI) is being incorporated into accounting education, examining the evolving educational setting and its potentially transformative impact on the development of future accounting professionals. Following the Arksey and O'Malley (2005) methodology and PRISMA-ScR guidelines (Tricco et al., 2018), this review synthesizes systematically a diverse set of academic literature to determine major trends, new opportunities, and long-standing challenges of integrating AI into accounting pedagogical practices. Key findings demonstrate AI's transformative potential in enhancing student engagement, fostering deeper learning, aligning educational curricula with contemporary industry demands, and improving teaching efficiency through innovative tools and techniques. However, substantial challenges persist, including faculty preparedness, the complexity of curriculum redesign, resistance to change, and critical ethical considerations surrounding the use of AI in education. These findings emphasize the multifaceted nature of integrating AI into accounting pedagogy. The review emphasizes the need for cooperation between academia, industry practitioners, and policymakers to develop adaptive, forward-thinking pedagogical strategies and establish robust ethical frameworks. These efforts are essential to improve learners with the skills and competencies required to thrive in a dynamic, technology-driven professional environment.

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.

In recent decades, the work of teachers has been carried out in classrooms characterised by a high degree of diversity, necessitating the provision of pedagogical responses that promote the inclusion of students. This article is derived from the research, development and innovation (R+D+I) project 'Intergenerational Professional Development in Education Implications for the Professional Initiation of Teachers (DePrInED) and aims to analyse how teachers at compulsory education recognise, characterise and conceptualise the diversity and needs of students. A mixed methodological approach was employed, with information collected through focus groups and interviews with a total sample of 147 teachers from different educational levels (young, senior and retired). Thematic analysis, combined with a comparative analysis, was employed to analyse the data using ATLAS.ti software. The main categories analysed dealt with students' needs and their own diversity. The study's primary findings suggest that, in comparison to their retired counterparts, both young and veteran teachers make greater reference to the diversity and needs of students, and emphasise the scarcity of resources and support to effectively address the diversity of students in the classroom. Experienced teachers are found to make more frequent references to diversity and students' needs, while retired teachers make these references less often. This finding assumes particular significance in light of the paucity of studies that adopt a generational perspective to analyse how teachers perceive diversity.

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.

Ensuring the trustworthiness of qualitative research remains a critical challenge in educational research. However, early career researchers often lack structured guidance on enhancing the credibility of qualitative data analysis. A key issue is the limited discussion on inductive approaches that support systematic theme generation and theory development. To address this gap, this study examines how two early-career researchers employed a three-level inductive methodology during their PhD studies to strengthen the trustworthiness of their findings. Using an autoethnographic approach, the study finds that this methodology deepened their understanding of participants’ experiences, facilitated the emergence of valid themes, and reinforced credibility, transferability, dependability, and confirmability. These findings offer concrete strategies for researchers undertaking similar approaches to ensure trustworthiness in their qualitative inquiry. This study also highlights the importance of equipping PhD researchers in education with strategies to navigate qualitative research rigorously, ultimately enhancing the quality of their studies.

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.

This study delved into Terry Borton’s reflective model and 7E instructional model to produce comprehensive and guided tools that fit as observation and reflective tools for enhancing learners’ engagement and outcomes in Mathematics lessons. The aim was to gather insights that can inform strategies to adapt Borton’s model to produce tools to be used to observe and analyse 7E model-based Mathematics lessons to contribute to improved student outcomes. Literature information was used to explore, analyse, and synthesise the study’s related existing theories and models to provide a deeper understanding of constraints and prompt question descriptors to produce 21st-century tools to observe and reflect on a Mathematics lesson. After comparing different prompt question descriptors from different literature and Borton’s model, concise descriptors were retained for educational purposes to be analysed, considering the 7E model phases, to produce the guided tools. As a result, two products. “Classroom Observation-Guided Tool”, which includes a guided tool table with a last column for the observer to write comments during class. This column is used to identify gaps in student engagement and learning practices across the 7E phases, which may have been misused. The “Post-Lesson Discussion Guided Tool”, to make a positive post-lesson discussion session, enabling teachers to identify areas for improvement in student engagement to achieve better outcomes next time. Other researchers can study the applicability of 21st-century observation and reflection-guided tools to other subjects, exploring their long-term impact on teacher professional development to improve overall student achievement across all school subjects.

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.