Epistemic Networks Research Articles

A SeNA-based study of in-service teachers’ interaction patterns in an online learning community

In-service teachers, constrained by work commitments, adopt online learning methods for their graduate studies, emphasizing the significance of exploring the construction of online learning communities. Analyzing collaborative conversation texts within the online learning process of the “Learning Science” course at a university in Beijing, this study employed social epistemic network analysis (SeNA) to investigate interaction patterns of in-service teachers and propose optimization strategies. The findings of this study show that in asynchronous discussions without intervention, the overall situation of collaborative conversations among in-service teachers tends to be low-level. The social interaction network shows polycentricity, categorizing members into core and peripheral interactors based on distinct social interaction characteristics, highlighting substantial disparities in epistemic networks. Core interactors prioritize engaging with others, yet their contributions primarily echo interactions, reflecting superficial knowledge dimensions. Conversely, specific peripheral interactors prioritize independent reflection, sharing content largely derived from personal experiences or knowledge, signifying a commitment to independent and in-depth thinking within deeper knowledge dimensions. Moreover, learners’ online interactions are influenced by individual characteristics, instructional evaluation and the learning community atmosphere.

Modeling Group Discourse with Epistemic Network Analysis: Unpacking Connections, Perspectives, and Individual Contributions

AbstractOperationalizing and modeling group talk has proved to be a consistent challenge in educational research. In this paper, we suggest that epistemic network analysis (ENA) could provide unique insights concerning group talk. Specifically, we use ENA to model the talk orientations put forward in the Exploratory Talk framework (Cumulative, Disputational, Exploratory). Participants (n = 60, 67% female, 33% male) were undergraduate students in an Introduction to Psychology course who took part in three 90-min collaborative online tasks. We coded student discourse according to a set of basic communicative acts reflective of the Exploratory Talk framework. Then, using ENA, we identified different groups’ patterns of discourse at the group and individual level. Presenting the epistemic networks of four purposefully chosen groups, this paper offers three key contributions to modeling and conceptualizing group dialogue: (1) illustrating how ENA could offer new ways to analyze group talk by focusing on the frequency of co-occurrence of connections between a basic set of communicate acts rather than the different communicative acts used; (2) refining the theoretical conceptualization of Exploratory Talk by distinguishing two sub-variations—other-oriented vs. self-oriented Exploratory Talk—that differ according to the depth of engagement with other perspectives; (3) examining how ENA allows unpacking diverging dynamics of individual contributions to group discourse, focusing on the role of individuals that function as “instigators” or “connectors.”

Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analytics

AbstractEffective collaboration and teamwork skills are critical in high‐risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real‐world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.Practitioner notesWhat is currently known about this topic Immersive learning environments simulate real‐world situations, helping students improve their teamwork skills. In these settings, students can have multiple simultaneous conversations while working together on tasks at different physical locations. The dynamic nature of these interactions makes it hard for teachers to assess teamwork and communication and for students to reflect on their performance. What this paper adds We propose a method that employs multimodal learning analytics for automatically generating teamwork‐related insights into the content of student conversations. This data processing method allows for automatically transcribing and coding spatially distributed dialogue segments generated from students working in teams in an immersive learning environment and enables downstream analysis. This approach uses spatial analytics, natural language processing and automated speech recognition techniques. Implications for practitioners Automated coding of dialogue segments among team members can help create analytical tools to assist in evaluating and reflecting on teamwork. By analysing spatial and speech data, it is possible to apply learning analytics advancements to support teaching and learning in fast‐paced physical learning spaces where students can freely engage with one another.

Epistemic networks and the social nature of public engagement with science

AbstractThis theoretical paper focuses on the social processes of public engagement with science and their implications for science education. The core of our argument is that science education should help people become better at evaluating, using, and curating their epistemic networks to make personal and civic decisions and to understand the natural world. In this context, an epistemic network is a set of people who support sensemaking by providing new information and aiding in the interpretation and reconstruction of scientific knowledge in context. We believe epistemic networks are an important consideration for science education, particularly when misinformation plays an outsized role in the cultural landscape. Understanding when epistemic networks are useful and how science education should incorporate them requires a clear sense of how they work in different contexts. We start by contrasting the inevitably social nature of all public engagement with science with the particularly social or interpersonal nature of some public engagement with science. We draw on research from education, communication, and science and technology studies to develop the idea of an epistemic network and to describe two basic types: the individual resource network and the collective action network. We illustrate each type with an extended example that is hypothetical but informed by both research and experience. Finally, we discuss how science education can incorporate epistemic networks, as well as the challenges inherent in that educational strategy.

Examining the impact of teacher scaffolding in the knowledge building environment: Insights from students’ interaction patterns, social epistemic networks, and academic performance

Examining the impact of teacher scaffolding in the knowledge building environment: Insights from students’ interaction patterns, social epistemic networks, and academic performance

Learning analytics to explore the motivational profiles of non-traditional practical nurse students: a mixed-methods approach

Learning analytics provides a novel means to examine various aspects of students’ learning and to support them in their individual endeavors. The purpose of this study was to explore the potential of learning analytics to provide insights into non-traditional, vocational practical nurse students’ (N = 132) motivational profiles for choosing their studies, using a mixed-methods approach. Non-traditional students were somewhat older learners than those following a more straightforward educational pathway and had diverse educational or professional backgrounds. Institutional admission data and analytics were used to identify their specific study motives and distinct motivational profiles, and to illustrate the connections between the motives emerging in the motivational profiles. Furthermore, the association between the motivational profiles and study performance was examined. The results of qualitative content analysis indicated that non-traditional practical nurse students pursued such specialized training for various reasons, and that pragmatic, professional rationales were emphasized over prosocial, altruistic factors. Through the adoption of person-centered latent class analysis, three motivational profiles were identified: self-aware goal-achievers, qualification attainers, and widely oriented humanitarians. Additionally, the analyses of epistemic networks for the profiles showed the complex interplay between the motives, confirming that some motive connections appear to be more prominent than others. Moreover, the findings indicated that study motives reported at admission did not seem to dictate students’ later study performance, as no statistically significant associations were found between the motivational profile and the students’ final grade point average or study dropout. This investigation paves the way for more-targeted motivational support and the use of learning analytics in the context of vocational education and training.

The Role of Sudanese Doctors in the United Kingdom in Mitigating COVID-19 Vaccine Hesitancy Among Their Diaspora Communities.

People from ethnic minority groups are at risk of adverse sequelae of COVID-19, but their vaccine acceptance and enrollment rates remain suboptimal. Diaspora communities are unique in that they continue to engage in epistemic networks related to vaccination with dwellers in their country of origin. We describe the COVID-19 vaccine awareness campaign we conducted from January 2021 to November 2021 for Sudanese diaspora groups in the United Kingdom and the various techniques and methods used by health care professionals from the same ethnocultural background to address vaccine reluctance among this cohort. We explore the impact of our joint efforts with local authorities in Sudan to overcome the initial barriers to the national vaccination program in Sudan. The influence of the intercommunication between the diaspora and their relatives at home regarding COVID-19 vaccine misinformation is also discussed. Finally, we highlight these observations as key areas for future research to improve the global impact of vaccine deployment in pandemic situations.

Epistemic Network: The Jesuits and Tropical Cyclone Prediction, 1860–1900

Jesuit scientists played a key role in the establishment of the earliest warning systems for tropical cyclones in the 1870s and 1880s. Jesuit observatories and their cyclone prediction methods were unrivaled in the geopolitically and commercially competitive landscape in the Antilles and the Far East. Even Western governments and local mercantile communities in Cuba, China, and the Philippines entrusted the Jesuits with the direction of national and regional weather services in the 1880s and 1890s, while state observatories and imperial navies often surreptitiously drew on the Jesuits’ forecasts for their own storm warnings. The ability of the Society of Jesus to produce, codify, and disseminate scientific knowledge is here addressed in terms of the formation of an “epistemic network” of actors sharing certain beliefs as well as a common scientific enterprise. This actor-centered network did not mean, however, that there was some center of calculation or production through which Western domination of knowledge over the periphery would take place. Quite the contrary, the case of Jesuit cyclone prediction details a specific networked activity that deepens our understanding of how specific epistemic networks can be actively engaged in the processes of circulation and globalization while also adapting, modifying, and producing prediction methods in local contexts.

The role of indoor positioning analytics in assessment of simulation‐based learning

AbstractSimulation‐based learning provides students with unique opportunities to develop key procedural and teamwork skills in close‐to‐authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the assessment of simulation‐based learning by making salient aspects of students' activities visible for evaluation. Although descriptive analytics have been used to study students' motor behaviours in simulation‐based learning, their validity and utility for assessing performance remain unclear. This study aims at addressing this knowledge gap by investigating how indoor positioning analytics can be used to generate meaningful insights about students' tasks and collaboration performance in simulation‐based learning. We collected and analysed the positioning data of 304 healthcare students, organised in 76 teams, through correlation, predictive and epistemic network analyses. The primary findings were (1) large correlations between students' spatial‐procedural behaviours and their group performances; (2) predictive learning analytics that achieved an acceptable level (0.74 AUC) in distinguishing between low‐performing and high‐performing teams regarding collaboration performance; and (3) epistemic networks that can be used for assessing the behavioural differences across multiple teams. We also present the teachers' qualitative evaluation of the utility of these analytics and implications for supporting formative assessment in simulation‐based learning. Practitioner notesWhat is currently known about this topic Assessing students' performance in simulation‐based learning is often challenging and mentally exhausting. The combination of learning analytics and sensing technologies has the potential to uncover meaningful behavioural insights in physical learning spaces. Observational studies have suggested the potential value of analytics extracted from positioning data as indicators of highly‐effective behaviour in simulation‐based learning. What this paper adds Indoor positioning analytics for supporting teachers' formative assessment and timely feedback on students' group/team‐level performance in simulation‐based learning. Empirical evidence supported the potential use of epistemic networks for assessing the behavioural differences between low‐performing and high‐performing teams. Teachers' positively validated the utility of indoor positioning analytics in supporting reflective practices and formative assessment in simulation‐based learning. Implications for practitioners Indoor positioning tracking and spatial analysis can be used to investigate students' teamwork and task performance in simulation‐based learning. Predictive learning analytics should be developed based on features that have direct relevance to teachers' learning design. Epistemic networks analysis and comparison plots can be useful in identifying and assessing behavioural differences across multiple teams.

In Trust We Trust: Epistemic Vigilance and Responsibility

ABSTRACT Much of what we know we know through testimony, and knowing on the basis of testimony requires some degree of trust in speakers. Trust is therefore very valuable. But in trusting, we expose ourselves to risks of harm and betrayal. It is therefore important to trust well. In this paper, I discuss two recent cases of the betrayal of trust in (broadly) academic contexts: one involving hoax submissions to journals, the other faking an identity on social media. I consider whether these betrayals suggest that we ought to be less trusting in contexts like these. I argue that we should not: the acquisition of knowledge is dependent on trust, and we cannot intentionally reduce the extent to which we trust in these kinds of contexts without risking destroying it utterly. Instead, we must trust in our epistemic networks and the way they work to filter out deception.

Harnessing the power of promising technologies to transform science education: prospects and challenges to promote adaptive epistemic beliefs in science learning

ABSTRACT Forming learners’ science concepts and conceptual change entails adaptive epistemic beliefs to support a high degree of interactivity within a coherent knowledge structure. Adaptive epistemic beliefs are characterized by beliefs that knowledge is uncertain and should be justified through experimentation or multiple sources dependent upon the task contexts. Thus, assessing and evaluating learners’ adaptive epistemic beliefs is a complex process that requires laborious analysis of learner artifacts based on reliable and valid coding schemes. This article aims to describe new ways of assessing and applying technologies that can measure and foster adaptive epistemic beliefs. We propose new strategies for a theoretically-based human-and-machine symbiotic Learning Analytics (LA) framework. The application of this LA framework may facilitate the development of real-time detecting and representation of the individual and collective epistemic belief networks as well as diagnosing and providing appropriate scaffolds to promote adaptive epistemic beliefs via the design of personalised pedagogical feedback with experts’ input. The heuristic application of technology infrastructure may propel a movement for more tangible and personalised learning in science education. The current gaps of using AI-based emerging technologies in science learning and implications for science education are discussed to advance science education in new directions.

Mapping the nature of science in the Italian physics curriculum: from missing links to opportunities for reform

ABSTRACT The article describes the analysis of the Italian physics curriculum documents in terms of the coverage of nature of science (NOS). NOS is not explicitly presented as part of the Italian high-school physics curriculum. The article focuses on analysing the implicit aspects of the curriculum documents. The Family Resemblance Approach (FRA) to NOS was used as an analytical lens. FRA was chosen because of the broad range of categories that it provides to trace curriculum content. Curriculum documents were analysed using the FRA categories as well as an Epistemic Network Analysis (ENA) which has shown important limitations in the FRA categories ‘Political Power Structures, Scientific Ethos, Professional Activities’. These aspects of NOS belong to the social-institutional aspects of the FRA framework. There were also some inconsistencies between the general section and the specific sections of the curriculum. The study elaborates on how FRA and ENA can be used to analyse NOS content in a science curriculum. The epistemic networks provide concrete illustrations of where curriculum revision can best be carried out to promote underrepresented aspects of NOS. Implications for future research and recommendations for curriculum reform internationally are suggested.

The Dynamics of Retraction in Epistemic Networks

Sometimes retracted or refuted scientific information is used and propagated long after it is understood to be misleading. Likewise, retracted news items may spread and persist, despite being publicly established as false. In this article, we explore the dynamics of retraction using agent-based models of epistemic networks, to see why false beliefs might persist despite retraction. We find that, often, those who received false information simply fail to receive retractions because of social dynamics. Surprisingly, delaying retraction can increase its impact. Further, retractions are most successful when issued by the original source of misinformation, rather than a separate source.

Combining the Knowledge Appropriation Model and epistemic networks to understand co-creation and adoption of learning designs using log data

Social practices are well-known mediators in the adoption of educational innovations during professional learning, as postulated by the Knowledge Appropriation Model (KAM). However, understanding how teachers adopt new pedagogical approaches at scale is often difficult due to the lack of evidence available about their daily practices. In that sense, log data from online authoring and learning tools offer the possibility of better understanding the creation process of a learning design that reifies an educational innovation. 
 This paper explores how statistical models and Epistemic Network Analysis (ENA) can help us understand large-scale patterns in the co-creation and adoption of educational innovations, using KAM as a theoretical framework to analyse log data. More concretely, this paper presents a case study on Go-Lab, an initiative to promote inquiry-based learning at school. Its authoring and learning tool -Graasp- gives us a unique opportunity to track, not only the (co)creation of learning designs, but also their potential implementation in the classroom. The case study uses the aforementioned methodological approach to analyse the role of large-scale support initiatives in the co-creation and adoption of learning designs.

Vulnerability in Social Epistemic Networks

ABSTRACT Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks.

Exploring the Inclusion of Nature of Science in Biology Curriculum and High-Stakes Assessments in Hong Kong

Nature of science (NOS) has become one of the major emphases in curriculum and assessment. This study explores what NOS is included and how this is achieved in the Hong Kong biology curriculum and 7-year high-stakes assessments. While conceptualizations of the official definition of NOS differ, this study employs the family resemblance approach (FRA) to analyze the inclusion of NOS in the curriculum and the high-stakes assessments. In one characterisation of FRA (i.e. Erduran and Dagher 2014), NOS comprises a cognitive-epistemic system and a social-institutional system. There is a total of 11 categories in these two systems and these categories are interconnected with one another. By adopting the epistemic network analysis (ENA), the frequency of connections among these NOS categories in the curriculum and the high-stakes assessments can be visualized and compared. Two main similarities are observed when comparing the epistemic networks of the curriculum and the high-stakes assessments: (1) a greater emphasis on the cognitive-epistemic rather than the social-institutional system and (2) the rarity of connections between categories in the social-institutional system. However, the high-stakes assessments differ from the curriculum in that they have more connections between the cognitive-epistemic system and the social-institutional system. Moreover, assessment items in the Hong Kong biology examinations involve a limited range of contexts and skills. Implications for the design of curricula and high-stakes assessments will be discussed.

Key Conditions of Adaptive Governance for Resilient Urban Areas: Insights from the Markermeer-IJmeer Region in the Amsterdam Metropole Region, the Netherlands

Climate change, ecological degradation and socio-economic developments are increasingly putting pressure on people’s living environments. Societies, regions and cities need to increase their resilience through adaptive governance, which is their capacity to adapt to changing relationships between society and ecosystems. In this article, we explore how three core conditions for adaptive governance, referred to as; 1) discourse arenas, 2) epistemic networks and 3) leadership, have proved to be useful in the shaping of the Markermeer-IJmeer region, part of the Amsterdam Metropole Region in the Netherlands. We find that discourse arenas and epistemic networks have set the scene for societal actors to invest in a sustainable transformation of the area. Moreover, they were a push factor for the transformation of opinions how to govern and plan the area. Actors identified links to overcome the division between socio-economic development and environmental conservation in the Amsterdam Metropole region. Actors from the industry took steps to include nature conservation. We recommend that adaptive governance should be enhanced with notions such as discourse, learning, trust, responsibility and leadership in future research and policy making for resilient urban areas.

Factoring civil society actors into health policy processes in low- and middle-income countries: a review of research articles, 2007-16.

Civil society actors have substantially increased their participation in global and national health policymaking processes since the 1970s. Civil society roles in shaping such significant global health milestones as the Doha Declaration on Intellectual Property Rights, the Framework Convention on Tobacco Control and the recently adopted United Nations Sustainable Development Goals are well documented, but knowledge of civil society actor influence on health policy processes in low- and middle-income countries remains fragmented. This study analyses 24 peer-reviewed research articles published between 2007 and 2016 to identify factors affecting civil society influence in the pre-implementation stages of the policy process. The articles reviewed span 13 health issues and more than 50 countries in four regions of the world. This body of work focuses on civil society as represented by formal groups, primarily domestic and to some extent international non-governmental organizations, but also social movements, professional associations and faith-based organizations, among others. The studies document several actor-centred and contextual factors that affect civil society actor power, commonly across stages of the policy process. Crucially, civil society actors were challenged to impact the process in countries that lacked participative norms and governing structures. When repressive conditions existed, regime changes and donors sometimes helped to open doors to participation. The power of civil society actors was enhanced when they joined strong epistemic networks and broader coalitions of stakeholders, were resourced, and framed issues in ways that resonated with national policies and political priorities. The synthesis offers guidance to practitioners on factors to consider in strategy development and points to several issues for further investigation by health policy analysis scholars, including the implications of issue (non)adoption by civil society actors and contestation dynamics among those with differing perspectives.

Reinterpreting urban institutions for sustainability: How epistemic networks shape knowledge and logics

Long term urban resilience demands a transition to a low-carbon society but poses a dilemma: the institutions that stabilise and perpetuate sociotechnical systems must become agents of radical change. The possibility of alternative futures challenges the logics and values central to institutional identity.‘Sustainability transitions’ thus raise questions of institutional reinterpretation. The extent of such reinterpretation hinges on the everyday ‘institutional work’ of actors who bring diverse understandings to bear on their roles and responsibilities. These understandings derive not only from actors’ professional roles but also from their engagement in wider epistemic networks.Based on case studies of three urban organisations in northern England, this paper examines the impact and influence of epistemic networks in validating or challenging approaches to sustainability transitions. The research found such networking a necessary, but not sufficient, condition for institutional reinterpretation. Epistemic networks serve five functions: they inspire, legitimise and facilitate potential transitions, and challenge slow progress - but they can also limit transitions. From these findings, it is argued that epistemic networks are central to the identification and development of nascent ‘transition arenas’ (Loorbach, 2010) where more sustainable, and ultimately more resilient, futures may be tested and trialled.

Моделируя технонауку: от эпистемических сетей к дискурсивным играм

Моделируя технонауку: от эпистемических сетей к дискурсивным играм