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Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.

PurposeThe primary objective of this study is to prioritize the main intentions behind investment in cryptocurrency, in spite of its volatile nature and no regulatory framework.Design/methodology/approachThis research paper has worked on collective constructs of the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and social support theory with an added construct of financial literacy. A fuzzy analytical framework has been applied to prioritize the intentions of investors.FindingsThe result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)” is the least influencing factor considered by investors. The subdimensions ranked in the top priority by investors are as follows: “I want to invest in cryptocurrencies because I have a good level of financial knowledge (FL1)”; “The people who are important to me will think that I should use cryptocurrencies (SI2)”; “I have the necessary resources to use cryptocurrencies (FC2).” The least importance is given to “It will be easy for me to become an expert in the use of cryptocurrencies (EE3).”Research limitations/implicationsFew of the constructs of the UTAUT, the TAM and social support theory have been considered while prioritizing intentions. Different other intentions also prevail under different theories that need to be researched further.Practical implicationsUnlike previous studies, this research adds the archetype of social commerce, social support and utility theories to analyze and prioritize the behavioral perspective of using cryptocurrencies in digital transactions.Originality/valueThis paper fills the gap in the research study, along with assisting the regulators and cryptocurrency practitioners to widen their knowledge base and to recognize the prioritized intentions.

To describe the development and application of logic model templates for systematic reviews and health technology assessments (HTAs) of complex interventions. This study demonstrates the development of a method to conceptualize complexity and make underlying assumptions transparent. Examples from systematic reviews with specific relevance to Sub-Saharan Africa (SSA) and other low- and middle-income countries (LMICs) illustrate its usefulness. Two distinct templates are presented: the system-based logic model, describing the system in which the interaction between participants, intervention, and context takes place; and the process-orientated logic model, which displays the processes and causal pathways that lead from the intervention to multiple outcomes. Logic models can help authors of systematic reviews and HTAs to explicitly address and make sense of complexity, adding value by achieving a better understanding of the interactions between the intervention, its implementation, and its multiple outcomes among a given population and context. They thus have the potential to help build systematic review capacity—in SSA and other LMICs—at an individual level, by equipping authors with a tool that facilitates the review process; and at a system-level, by improving communication between producers and potential users of research evidence.

Small‐scale fisheries and aquaculture constitute critical pillars of food security, livelihoods, and rural economies across East Africa, yet persistent gendered inequalities continue to constrain both equity and sectoral performance. This study provides a synthesis of empirical evidence from Kenya, Uganda, and Tanzania to examine how structural, institutional, and socio‐cultural processes reproduce gender disparities across fisheries and aquaculture value chains. Drawing on a narrative review of peer reviewed and grey literature published between 2015 and 2025, the analysis applies the Harvard Analytical Framework (HAF) to interrogate four interrelated dimensions: Division of labour, access to and control over productive resources, influencing institutional and normative factors, and gender integration within project cycles. The findings indicate that gender inequalities are most pronounced in asset ownership, decision making authority, and benefit distribution rather than participation alone. Women remain concentrated in labour intensive and lower value post‐harvest activities, while men dominate harvesting, aquaculture production, licensing systems, and leadership positions within co management institutions. Across the three countries, men retain control over the majority of productive assets, including boats, fishing gear, land, and aquaculture infrastructure, while women's access to credit, extension services, and modern technologies remains constrained. These disparities are reinforced by inheritance regimes, gender norms, and male dominated governance structures that shape access to resources and control over income. Development interventions frequently expand women's participation without addressing underlying structural constraints, thereby contributing to the reproduction of institutional inequalities. By linking structural analysis to policy relevant pathways, the study argues that inclusive blue economy development requires tenure reform, gender responsive financing mechanisms, institutional restructuring, and sustained normative change. The review positions gender equity as a governance and productivity imperative and provides an evidence based foundation for advancing resilient and inclusive fisheries and aquaculture systems in East Africa.

This paper provides a summary account of Activity-Centred Analysis and Design (ACAD). ACAD offers a practical approach to analysing complex learning situations, in a way that can generate knowledge that is reusable in subsequent (re)design work. ACAD has been developed over the last two decades. It has been tested and refined through collaborative analyses of a large number of complex learning situations and through research studies involving experienced and inexperienced design teams. The paper offers a definition and high level description of ACAD and goes on to explain the underlying motivation. The paper also provides an overview of two current areas of development in ACAD: the creation of explicit design rationales and the ACAD toolkit for collaborative design meetings. As well as providing some ideas that can help teachers, design teams and others discuss and agree on their working methods, ACAD has implications for some broader issues in educational technology research and development. It questions some deep assumptions about the framing of research and design thinking, in the hope that fresh ideas may be useful to people involved in leadership and advocacy roles in the field.

Critical discourse analysis (CDA) is a qualitative analytical approach for critically describing, interpreting, and explaining the ways in which discourses construct, maintain, and legitimize social inequalities. CDA rests on the notion that the way we use language is purposeful, regardless of whether discursive choices are conscious or unconscious. CDA takes a number of different approaches and incorporates a variety of methods that depend on research goals and theoretical perspectives. This methodological guide presents a general CDA analytic framework and illustrates the application of that framework to a systematic literature review of CDA studies in education. CDA research studies are no less likely than other forms of scholarly research to reproduce ideological assumptions; qualitative rigor and trustworthiness are discussed.

Investigating effectiveness of learning analytics is a major topic of research, with a recent systematic review finding 689 papers in this field (Larrabee Sonderlund et al., 2019). Few of these (11 out of 689) highlight the potential of interventions based on learning analytics. The Open University UK (OU) is one of few institutions to systematically develop and implement a learning analytics framework at scale. This paper reviews the impact of one part of this framework - the Analytics for Action (A4A) process, focusing on the 2017-18 academic year and reviewing both feedback from module teams and interventions coming out of the process. The A4A process includes hands-on training for staff, followed by data support meetings with educators when the course is live to students. The aim being to help educators with making informed, evidence-based interventions to aid student retention and engagement. Findings from this study indicate that participants are satisfied with the training and that the data support meetings are helping in providing new perspectives on the data. The scope and nature of actions taken by module teams varies widely, ranging from no intervention at all to interventions spanning over multiple presentations. In some cases, measuring the impact of the actions taken will require data analysis from further presentations. The paper also presents findings indicating room for improvement in the follow up of the actions agreed, support given to module teams to implement such actions and final evaluation of impact on student outcomes.

East Asia is a region of signal importance for global order because of its economic dynamism and growing heft, China’s challenge to the United States as incumbent regional and global hegemon, and other conflict hotspots like the Korean peninsula. This requires academic analysis that both appreciates the subtleties inherent to this region and can relate them to the wider systemic context. Many analysts have begun to allude to the challenging characteristics that are present in the international relations of East Asia, in particular struggling to explain how growing levels of economic interdependence can coexist with heightened security tensions. This article offers a research prospectus that suggests ways of analyzing these apparently contradictory trends. It proposes the development of research questions and approaches that are more suited to studying the international relations of a region with characteristics that we define as dual, hybrid, and contingent. We propose a Conjunctions Analytical Framework that explores what happens at the conjunctions of the regional-global and the unit-regional/global levels of analysis—the “grey areas” where social formations meet and interact. We aim to help shape the future study of the IR of East Asia and to suggest more effective ways of analyzing the complex reality of East Asia’s regional and global politics.

Framework analysis and applied qualitative research can be a perfect match, in large part because framework analysis was developed for the explicit purpose of analyzing qualitative data in applied policy research. Framework analysis is an inherently comparative form of thematic analysis which employs an organized structure of inductively- and deductively-derived themes (i.e., a framework) to conduct cross-sectional analysis using a combination of data description and abstraction. The overall objective of framework analysis is to identify, describe, and interpret key patterns within and across cases of and themes within the phenomenon of interest. This flexible and powerful method of analysis has been applied to a variety of data types and used in a range of ways in applied research. Framework analysis consists of two major components: creating an analytic framework and applying this analytic framework. This paper details the five steps in framework analysis (data familiarization, framework identification, indexing, charting, and mapping and interpretation) through conducting secondary analysis on this special issue’s common dataset. This worked example adds to the existing framework analysis methodology literature both through describing the analysis specifics and through highlighting the importance of multiple considerations of units of analysis. This paper also includes reflection on the myriad reasons that framework analysis is valuable for applied research.

The judicial reforms undertaken during the last thirty years have generated abundant literature on the subject, by authors with training in legal, political, or economic sciences. Most of these researchers acknowledge the extreme complexity of these reforms and use analytical frameworks specific to their discipline to understand the underlying issues and find possible solutions. Such studies typically aim to identify the strategies and tactics followed by the reformers, as well as the role of the main stakeholders (both within the justice institutions and outside) in the outcome of the reforms. The combination of these three disciplines in the study of the reforms through methodologies such as \"process tracing/analytical narratives,\" including elements of historical analysis, provides an integrated framework that should enable new progress in the study of a key area of democratic governance. To that end, this article presents a selection of the relevant literature about judicial reforms, based on the author's experience, and highlights the role of quantitative and perception data in a field where citizen expectations are increasing, but reliable and comparable information is scarce.