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Prior studies have mainly focused on testing collaborative programming learning (CPL) patterns while neglecting the exploration of the dynamic evolution of social epistemic interaction patterns among different groups. Studying the social and epistemic network nature of learner interaction is crucial to understanding the CPL process. This study aims to explore the social epistemic interaction patterns and their evolutionary path among different groups. In this quasi-experimental design, 51 high school students were randomly allocated into 17 groups. Content analysis was used to analyze online collaborative conversations and interaction contents in the early, middle, and later periods of CPL. Social epistemic network and cluster analyses revealed three interaction patterns. The results showed that groups in cluster 1 were composed of core roles, which exhibited a multi-center balanced collaboration pattern (MBCP), and their social epistemic interaction levels showed a continuous upward trend; groups in cluster 2 included core, semi-core, and edge roles, respectively, and demonstrated a hierarchical center-led coordination pattern (HLCP) that initially gained but later declined in social epistemic interaction levels; groups in cluster 3 included core and edge roles, and displayed a single-center feedback cooperation pattern (SFCP), which remained consistently low in social epistemic interaction levels. Our findings emphasize the importance of CPL's social epistemic interactions. By recognizing these patterns, educators can better facilitate meaningful student interactions, fostering deeper learning and social development.

PurposeTo explore strategies for enhancing cultural relevance in K–12 education in Kisii by addressing the knowledge gap between formal schooling, which tends to be Eurocentric and the local cultural context of learners. The study seeks to bridge this gap by identifying themes in indigenous knowledge and practices for integration into the formal education system.Design/methodology/approachSemi-structured interviews were conducted involving 60 participants aged 12 and above, drawn from the Kisii tribe in Kenya, including students, parents, teachers, administrators and cultural experts. The data collected from the discourse was analyzed using epistemic network analysis (ENA) to identify patterns and connections in the participants’ opinions.FindingsNine key cultural elements emerged that can be leveraged to support learning and teaching in Kisii K–12 schools: rites of passage, language, heritage, oral traditions, values, beliefs, rewards and punishment, practical learning over theory and local STEM. The views of students diverged substantially from those of cultural experts on which of these themes were most relevant to education.Originality/valueThe study presents a novel cultural integration and augmentation (CIA) framework and the transfer and adoption of universal principles (TAUP) model. These models offer practical guidance for teacher training to support culturally relevant curriculum development in K-12 education in Kisii and introduce a framework that is transferrable to other settings in the Global South.

PurposeThis study examines how model-eliciting activities (MEAs) help students in a private school with middle-income high school students elicit computational thinking (CT) in an unplugged activity. The focus is on equitable participation across diverse academic backgrounds in resource-limited settings.Design/methodology/approachThe research employed epistemic network analysis (ENA) to analyze video-recorded conversations of two three-student teams solving an unplugged tic-tac-toe MEA. Participants represented varied academic tracks, programming experience levels and socioeconomic backgrounds. The researchers coded the conversations for computational thinking constructs: decomposition, pattern recognition, abstraction and algorithms.FindingsENA revealed similar network structures between teams and participants despite their different compositions. Both teams demonstrated robust connections across all four computational thinking constructs. The unplugged MEA format enabled equitable participation regardless of prior programming experience or academic background, with balanced engagement observed across all team members.Originality/valueThis study uniquely applies ENA to examine computational thinking development through unplugged MEAs in Mexico’s educational context. It provides empirical evidence for MEAs as tools for democratizing access to computational thinking education in resource-limited settings while introducing a methodological framework for analyzing cognitive development in collaborative learning environments.

PurposeConstructivists propose that engagement and perspective in active learning experiences influence learning. Participatory evaluation (PE), rooted in constructivism, offers insights into how participant feedback can shape studies and eliminate the risk of perpetuating mistakes, biases, hindering results, momentum and traction within the intended community.Design/methodology/approachThis report covers two studies pertaining to tertiary education in rural Kenya: Study A and its successor, Study B. In 2023, Study A aimed to develop a tertiary education model for economically vulnerable students in rural Kenya. Study A converted qualitative transcribed interview data from Kenyan mentors and educators into quantitative data informing the tertiary model. Despite the implementation of this tertiary model in Kenya, economic instability persisted among the enrolled tertiary students. In 2024, a follow-up, Study B, engaged the same Kenyan mentors and educators who participated in the initial study to evaluate the findings of Study A.FindingsThe democratic process of PE revealed previously overlooked issues, enriching understanding and highlighting areas for improvement. Study B unearthed salient issues missed in Study A, including issues of funding and political challenges crucial for the sustainability of tertiary models and subsequent tertiary schools.Originality/valueThis study showcases the application of ENA and PE to enhance educational models, unveiling hidden connections and refining existing frameworks. This paper seeks to describe how epistemic network analysis discourse networks can be evaluated through PE to strengthen or modify existing models and explore the possibility of very salient unseen and also notable connections.

Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world.

Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.

As technology-enhanced children's learning has gained wide attention, programmable robots have been gradually introduced in early childhood education. Hence, it would be valuable to understand how young children perceive robot programming learning. Draw-a-picture technique is an ideal method to elicit ideas, thoughts, and feelings for children with limited literacy, and epistemic network analysis (ENA) is a novel analytical method to analyze children's conceptions through the visualized network model. Therefore, this study employed a draw-a-picture technique and ENA to explore 189 5-6-year-old young children's conceptions of robot programming learning and probe whether their conceptions differ by gender and learning achievements. Results revealed that most children believed that with robot programming kits, they could engage in programming activities with peers in any location and held positive emotions and attitudes. In addition, young children's conceptions of robot programming learning differ notably by gender and learning achievements. Based on the current findings, several suggestions were proposed, which could set a reference for future robot programming teaching in early childhood education.

Grounded on constructivism, mining a complex mix of social and cognitive interrelations is key to understanding collaborative discussion in online learning. A single examination of one of these factors tends to overlook the impact of the other factor on learning. In this paper, we innovatively constructed a social-cognitive engagement setting to jointly characterize social and cognitive aspects. In the online discussion forum, this study jointly characterized students' social and cognitive aspects to investigate interactive patterns of different social-cognitive engagements and social-cognitive engagement evolution across four periods (i.e., creation, growth, maturity, and death). Multi-methods including social network analysis, content analysis, epistemic network analysis, and statistical analysis was applied in this study. The results showed that the interactive patterns of social-cognitive engagement were affected by both social network position and cognitive level. In particular, students' social network position was a vital indicator for the contributions to cognitive level of students, and cognitive level affected the related interactions to some extent. In addition, this study found a nonlinear evolutionary development of students' social-cognitive engagement. Furthermore, maturity is a critical period on which teachers should focus, as the co-occurrence of social-cognitive engagement reaches a maximum level in this period. Based on the results, this multi-perspective analysis including social and cognitive aspects can provide insightful methodological implications and practical suggestions for teachers in conducting in-depth interactive discussions.

Research on computer-supported collaborative learning (CSCL) is often concerned with the question of how scaffolds or other characteristics of learning may affect learners’ social and cognitive engagement. Such engagement in socio-cognitive activities frequently materializes in discourse. In quantitative analyses of discourse, utterances are typically coded, and differences in the frequency of codes are compared between conditions. However, such traditional coding-and-counting-based strategies neglect the temporal nature of verbal data, and therefore provide limited and potentially misleading information about CSCL activities. Instead, we argue that analyses of the temporal proximity, specifically temporal co-occurrences of codes, provide a more appropriate way to characterize socio-cognitive activities of learning in CSCL settings. We investigate this claim by comparing and contrasting a traditional coding-and-counting analysis with epistemic network analysis (ENA), a discourse analysis technique that models temporal co-occurrences of codes in discourse. We apply both methods to data from a study that compared the effects of individual vs. collaborative problem solving. The results suggest that compared to a traditional coding-and-counting approach, ENA provides more insight into the socio-cognitive learning activities of students.

In the future of intelligent education, enhancing the cognitive ability of students is a promising avenue worth investigating. Although most educational services address students’ explicit knowledge needs, exploring beyond cognitive boundaries to upgrade cognition are rare. In this study, we employ a hybrid approach combining quantitative questionnaire and qualitative epistemic network analysis to investigate the ‘cognitive upgrading’ experiences of 235 university students. Findings of the inquiry indicate: ① ‘Cognitive upgrading’ represents a prevalent and objectively discernible necessity for students, indicating the significance for facilitating its promotion; ②Current information resources cannot directionally guide students towards cognitive upgrading, necessitating the intervention of a third-party to render sporadic events manageable; ③The existing paradigms of cognitive upgrading can be categorized into eight quadrants. There still lacks a data-driven approach to be fully explored for more effectively facilitating the happening occurrence; ④Students with disparity in genders, educational backgrounds, and disciplines exhibit significant differences in cognitive upgrading, suggesting that training programs tailored to students’ characteristics can provide targeted support for enhancing cognitive upgrading efficacy. The study provides valuable insights for educators to implement cognitive upgrading strategies and paves a new path towards cognitive upgrading in intelligent education.