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.

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.

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 qualitative study explored the implementation of Differentiated Instruction (DI) in Philippine multigrade classrooms with the aim of understanding teachers’ experiences, strategies, and challenges, as well as developing a performance appraisal tool. Guided by its specific objectives, the research examined how teachers plan, deliver, and manage differentiated lessons while addressing the diverse learning needs of students across multiple grade levels. Findings revealed that effective DI is rooted in intentional instructional planning, including learner profiling, curriculum mapping, and flexible pacing. Instructional delivery was enriched through the use of thematic and multimodal strategies, ability-based groupings, and contextually relevant teaching aids, although technological access and training remained persistent barriers. Classroom management practices emphasized inclusive routines, peer collaboration, and adaptive learning spaces. Teachers also highlighted the importance of assessment tools and reflective teaching practices in continuously improving instruction. In response to these findings, the study developed the Multigrade Differentiated Instruction Performance Appraisal Tool (MDI-PAT), which synthesizes theoretical frameworks with authentic classroom practices. The MDI-PAT serves as both a self-assessment and professional development guide for multigrade educators, promoting ongoing improvement in DI competencies. The study concludes that enhancing teacher capacities in planning, delivery, assessment, classroom management, and reflective practice is essential for fostering inclusive and effective learning environments in multigrade contexts. The insights and tools presented provide a practical framework for educational stakeholders seeking to enhance multigrade instruction in resource-constrained 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.