Collaborative Knowledge Building Research Articles

Your body tells how you engage in collaboration: Machine‐detected body movements as indicators of engagement in collaborative math knowledge building

AbstractCollaborative learning, driven by knowledge co‐construction and meaning negotiation, is a pivotal aspect of educational contexts. While gesture's importance in conveying shared meaning is recognized, its role in collaborative group settings remains understudied. This gap hinders accurate and equitable assessment and instruction, particularly for linguistically diverse students. Advancements in multimodal learning analytics, leveraging sensor technologies, offer innovative solutions for capturing and analysing body movements. This study employs these novel approaches to demonstrate how learners' machine‐detected body movements during the learning process relate to their verbal and nonverbal contributions to the co‐construction of embodied math knowledge. These findings substantiate the feasibility of utilizing learners' machine‐detected body movements as a valid indicator for inferring their engagement with the collaborative knowledge construction process. In addition, we empirically validate that these inferred different levels of learner engagement indeed impact the desired learning outcomes of the intervention. This study contributes to our scientific understanding of multimodal approaches to knowledge expression and assessment in learning, teaching, and collaboration.Practitioner notesWhat is already known about this topic Previous research emphasizes the importance of gestures as essential tools for constructing common ground and reflecting shared meaning‐making in learning and teaching contexts. The prior studies in multimodal learning analytics (MMLA) suggest that certain forms of body movements and postures can be differentiated based on the automatic detection of upper body joint locations. Empirical observations indicate that co‐thought gestures typically involve smaller hand or arms movement that are closer to the gesturer's body than co‐speech gestures used in interpersonal communication. What this paper adds This paper fills the research gap by examining the use of gestures in collaborative learning, offering insights into how individuals contribute verbally and nonverbally to collaborative knowledge construction. This paper introduces the concept of using machine‐detected body movements as a viable proxy for inferring learners' engagement in collaborative knowledge‐building activities. Leverages sensor technologies for automatic detection of body movements, the innovative approach in this work seeks to overcome the time‐intensive and laborious process of manually coding gestures. Implications for practice and/or policy By recognizing the potential significance of learners' body movements in indicating engagement levels with collaborative knowledge‐building activities, instructors can set up computer‐supported collaborative learning (CSCL) environments to enable capturing these movements. Given the crucial role of gestures in learning, teaching, and collaboration, educators can create more equitable formative assessment practices for linguistically diverse students by developing strategies that align with multimodal forms of knowledge expression. Research can expand beyond mathematics to explore the transferability of these findings to other subjects, helping educators create comprehensive pedagogical approaches that leverage multimodal interactions across disciplines.

A joint evaluation method of regulated‐learning and cognitive quality in collaborative knowledge building

AbstractBackgroundBoth socially regulated learning and cognitive quality are important factors affecting collaborative knowledge building, but the current research lacks a joint quantified evaluation method that combines these two aspects.ObjectivesBased on the existing framework, we proposed a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building, and further extracted the evaluation indicators.MethodsThis study involved 74 learners enrolled in a course named ‘the Application of Modern Educational Technology’ as the subject of study, and made a case analysis with this method.Results and ConclusionsFirst, the indicators of this joint evaluation method could complement the indicators of traditional frequency statistics methods and facilitate an analysis of regulated learning and cognitive quality in collaborative knowledge building. Second, the group with a higher proportion of high‐level cognitive quality and socially shared regulation in collaborative knowledge building achieved better final scores. Finally, four types of groups were obtained through clustering: excellent, risky, mixed group, and highly collaborative group. This study introduced a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building and provides suggestions for future research.

Effects and mechanisms of analytics‐assisted reflective assessment in fostering undergraduates' collective epistemic agency in computer‐supported collaborative inquiry

AbstractBackgroundUndergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer‐supported collaborative inquiry approaches, such as KB—the focus of this study, and reflective assessment to foster collective epistemic agency.ObjectivesThis study used a quasi‐experimental design to examine the impact of reflective assessment on undergraduates' collective epistemic agency and the mechanisms of this impact.MethodsAn experimental class, comprising 40 undergraduates, engaged in KB inquiry enhanced by analytics‐assisted reflective assessment, while a comparison class, comprising 41 undergraduates, engaged in portfolio‐supported reflective assessment in KB inquiry. Classroom observations, audio recordings of group discussions and reflections, and student artefacts were collected in both conditions.Results and ConclusionsThe experimental class demonstrated significantly higher levels of collective epistemic agency than the comparison class, as indicated by their collective knowledge advancement and epistemic and metacognitive actions. Qualitative thematic analysis revealed that reflective assessment enhanced the undergraduates' collective epistemic agency through their collective monitoring and regulation of inquiry and ideas, negotiation and synthesis of the community's ideas, conceptualization of collective theories and identification of collective inquiry trajectories with an internal reflective structure.Major TakeawaysHigher‐level metacognitive actions are critical for productive collaborative inquiry and higher‐level epistemic agency that does not often occur naturally and should be promoted by appropriate scaffolding strategies. Students need data from learning analytics and accompanying prompt sheets to help them develop the internal metacognitive structure for agency and learning.

An automated analysis of topic distributions and features approach to promoting group performance, collaborative knowledge building and socially shared regulation in online collaborative learning

Online collaborative learning has been widely used in the field of education. However, unrelated or off-topic information is often included in online collaborative learning. Furthermore, the content of online discussion is often too shallow or narrow. To achieve productive collaborative learning, this study proposed and validated an automated analysis of topic distributions and features (AATDF) approach. In total, 189 college students in China participated in this study and were assigned to one of two experimental groups or a control group. Experimental Group 1 participated in online collaborative learning with the AATDF approach. Experimental Group 2 participated in online collaborative learning with the automated analysis of topic distributions (AATD) approach. The control group participated in traditional online collaborative learning without any specified approach. The results indicate that the AATDF approach can significantly promote group performance, collaborative knowledge building and socially shared regulation compared with the AATD and traditional online collaborative learning approaches. The results and implications are also discussed in depth. The main contribution of this study is that the AATDF approach can improve learning performance and bring online collaborative learning onto new ground. Implications for practice: The AATDF approach is very useful and effective for promoting group performance, collaborative knowledge building and socially shared regulation. Teachers and practitioners can provide personalised interventions and optimise collaborative learning design based on the analysis results of topic distributions and features. Developers can adopt deep neural network models to develop intelligent online

How Do Job Crafting Profiles Manifest Employees’ Work Engagement, Workaholism, and Epistemic Approach?

AbstractThe present study identifies job crafting profiles of public sector employees and how they differ in terms of employees’ work engagement, workaholism, and approach to learning. Participants represent various occupations from educational field (e.g., teachers), technical field (e.g., ICT-experts), and administrative field (e.g., customer servants). Using latent profile analysis, three job crafting profiles could be identified: Passive crafters (25%), Average crafters (57%), and Active crafters (18%). Passive crafters reported the lowest values in all approach-oriented job crafting strategies (increasing job resources and demands) and the highest value in avoidance-oriented job crafting (decreasing hindering job demands). Active crafters reached the highest values in all approach-oriented job crafting and the lowest value in avoidance-oriented job crafting. Average crafters used all job crafting strategies close to the average level. The lowest work engagement, workaholism, and reflective-collaborative approach to learning were reported by passive crafters. Both average crafters and active crafters reported higher workaholism and reflective-collaborative learning approach than passive crafters. Active crafters reported the highest work engagement. Study findings show the interplay between employees’ job crafting, work engagement, workaholism, and epistemic approach. This study extends workplace learning research field by offering new theoretical information and is the first one exploring job crafting profiles and their differences regarding employees’ epistemic approach; reflective learning, collaborative knowledge-building, and metacognition. Study discusses theoretical contributions and practical implementations, which may be used in work life induction, and in fostering job crafting and continuous workplace learning.

Impacts of three approaches on collaborative knowledge building, group performance, behavioural engagement, and socially shared regulation in online collaborative learning

AbstractBackgroundOnline collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning.ObjectivesThe study compared the impacts of the three learning approaches on collaborative knowledge building, group performance, socially shared regulation, behavioural engagement, and cognitive load in an online collaborative learning context. The first is the automatic construction of knowledge graphs (CKG) approach, the second is the automatic analysis of topic distribution (ATD) approach, and the third one is the traditional online collaborative learning (OCL) approach without any analytic feedback.MethodsA total of 144 college students participated in a quasi‐experimental study, where 48 students learned with the CKG approach, 48 students used the ATD approach, and the remaining 48 students adopted the OCL approach.Results and ConclusionsThe findings revealed that the CKG approach could encourage collaborative knowledge building, socially shared regulation, and behavioural engagement in building knowledge better than the ATD and OCL approaches. Both the CKG and ATD approaches could better improve group performance than the OCL approach. Furthermore, the CKG approach did not increase learners' cognitive load, but the ATD approach did.ImplicationsThis study has theoretical and practical implications for utilising learning analytics in online collaborative learning. Furthermore, deep neural network models are powerful for constructing knowledge graphs and analysing topic distribution.

Belonging in science classrooms: Investigating its relation to students' contributions and influence in knowledge building

AbstractMeaningful participation in science and engineering practices requires that students make their thinking visible to others and build on one another's ideas. But sharing ideas with others in small groups and classrooms carries social risk, particularly for students from nondominant groups and communities. In this paper, we explore how students' perceptions of classrooms shape their contributions to classroom knowledge building in science across a wide range of classrooms. We examine the claim that when students feel a sense of belonging in class, they contribute more and perceive their ideas to be more influential in knowledge building. Data comes from classroom exit tickets (n = 10,194) administered in 146 classrooms as part of a 10‐state field test of a new middle‐school science curriculum, OpenSciEd, which were analyzed using mixed effects models. We found that students' sense of belonging predicted the degree to which they contributed ideas out loud in class (Odds ratio = 1.57) as well as the degree to which they perceived their contributions as influencing others (Odds ratio = 1.53). These relationships were particularly strong for students who reported a lower a sense of belonging. We also found significant differences by both race and gender in whether students said they contributed and believed their ideas influenced those of others. These findings suggest that a learner's sense of belonging in class and willingness to contribute may be mutually reinforcing, highlighting the need to promote content‐specific strategies to foster belonging in ways that support collaborative knowledge building.

Cross Academia and Public Citizen Engagement for Developing Active Citizenship Competences

This paper explored the effect of the different open cross-university and public knowledge building activities on participants’ active citizenship competencies. A survey was conducted with the participants from 7 activities in higher education context in the INOS project. The results report that active citizenship competences could be better developed in cross-academia and public citizen engagement activities that target collaborative knowledge building and data management rather than in more individual crowdsourcing activities. The results indicate that the politico-legal competencies were the most difficult to develop through different activities and they require more specific design concerns in the future citizen science activities.

Coordinating modalities of mathematical collaboration in shared VR environments

This study investigates how learners collaboratively construct embodied geometry knowledge in shared VR environments. Three groups of in-service teachers collaboratively explored six geometric conjectures with various virtual objects (geometric shapes) under the guidance of a facilitator. Although all the teachers were in different physical locations, they logged into a single virtual classroom with their respective groups and were able to see and manipulate the same geometric shapes as well as see their collaborators’ avatars and actions on the shapes in real time in the shared virtual space. This paper introduces a novel multimodal data analysis method for analyzing participants’ interactive patterns in collaborative forms of actions, gestures, movements, and speech. Results show that collaborative speech has a strong simultaneous relationship with actions on virtual objects and virtual hand gestures. They also showed that body movements and positions, which often focus on virtual objects and shifts in these movements away from or around the object, often signal key interactional collaborative events. In addition, this paper presents five emergent multimodality interaction themes showing participants’ collaborative patterns in different problem-solving stages and their different strategies in collaborative problem-solving. The results show that virtual objects can be effective media for collaborative knowledge building in shared VR environments, and that structured activity design and moderate realism may benefit shared VR learning environments in terms of equity, adaptability, and cost-effectiveness. We show how multimodal data analysis can be multi-dimensional, visualized, and conducted at both micro and macro levels.

In Dialogue: The Future of Critical Studies in Literacy Research

For the final In Dialogue of our editorial term, we wanted to invite some luminary voices in literacy studies to think together about the future of critical studies in literacy research. We asked Betina Hsieh, Danielle Filipiak, Tiffany Nyachae, David Kirkland, and Carol Brochin what they thought would push the field forward: What would or should literacy studies and English education look like in the future, including what collective priorities should be emphasized? We invited them to think together, to imagine what might be possible or necessary in a world that is on fire. In giving these scholars the “last word” of our editorial term, we are hoping that this effort toward intergenerational, collaborative knowledge building can be one of the seeds of hope that will help us grow toward a better future.

Bridging the Discursive and Material Dimensions of Europeanity and Europeanisation

The article aims to capture the significatory diversity of the concepts of Europeanity and Europeanisation, through the development of a semantic map in order to visualize the different concepts that define Europeanity and Europeanisation, and their interconnections. Embedded in the field of Communication and Media Studies, this semantic map combines 19 different approaches, structured through one main dimension, the discursive versus the material, which allows bridging the major rift in the conceptual reflections about Europeanity and Europeanisation. Moreover, the semantic map uses two support dimensions, with the discursive dimension intersecting with the essentialist versus relationist dimension, and the material dimension intersecting with the socio-spatial versus politico-spatial dimension. In order to construct this semantic map, phases of both general and targeted literature reviews were combined with a participatory theory-building method, which was grounded in collaborative knowledge building and collaborative theory construction approaches.

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL.

Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

Students’ Reflections on Knowledge Building Process and Scaffolding

Collaborative learning environments such as “knowledge building community” and “online learning community” have gained importance with the inclusion of new technologies in the education process. In order to provide active participation expected in these learning environments, scaffolds such as sentence openers and role assignments are used in order to support, guide and enrich students' knowledge building processes. In this study, it is aimed to examine the pre-service teachers' opinions about online collaborative knowledge building process and the scaffolds used in this process. Seventy-seven pre-service teachers participating in the study participated in the knowledge building process in four different groups, using different scaffolds and no scaffolds. In this process, students were asked to complete the knowledge building questionnaire consisting of 12 questions of five likert types and a semi-structured interview form consisting of 10 questions reflecting their own experiences. Qualitative research method was used in the research and the data were analyzed by content analysis. In addition, some descriptive statistics such as percentage frequency are included. It was observed that the opinions of the students about their experiences in the collaborative knowledge building process were mostly positive. The students mostly stated that “it is important to determine our goals and to plan our progress”. The students defined their contributions in the knowledge building process as the most “providing diversity of opinions”. In addition, the scaffolds used by pre-service teachers in the online collaborative knowledge building process and their reflections on the better quality knowledge building process were included and interpreted in the context of technology-supported education.

Research on the Influence of Socially Regulated Learning on Online Collaborative Knowledge Building in the Post COVID-19 Period

Online teaching has become an important initiative to maintain normal educational order in the post COVID-19 period. However, learners face multiple challenges in the online learning process, which cannot be successfully carried out without the support of socially regulated learning (SoRL). This study designed SoRL intervention strategies from the perspective of shared metacognitive scripts. A total of 77 undergraduate students participated in this study and were randomly assigned into experimental and control groups. The students in the experimental group received an SoRL intervention, and the students in the control group learned with the traditional online collaborative learning approach. The results showed that there was variability in the supply of SoRL intervention scripts and the actual selection status of the learners. The regulation foci activated in this study were time management, content monitoring, and atmosphere activation. Atmosphere activation drove collaborative learning activities to continue. Time management and content monitoring drove collaborative knowledge building (CKB) to a deeper level. This study is of great significance in revealing the impact mechanism of SoRL on CKB.

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

AbstractComputer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. Practitioner notesWhat is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation. Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context. What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context. The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context. Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently.

Collaborative Knowledge Building and Social Capital in Gender-Based Violence, Northern Iraq

As the number of COVID-19 continue to increase exponentially in an alarming rate, so as the number increase in Gender-based Violence (GBV). Different authorities and human right groups have making effort to tackle the increase in GBV but still more need to be done. The purpose of this study is to explore the role of collaborative knowledge building and social capital in the fight against gender-based violence. To analyse this issue, the study developed a model with 3 constructs; social capital, Crisis preparedness and collaborative knowledge building. The data generated from the survey was analysed using statistical tools in SPSS and model fitness was tested based on reliability and validity. The study’s findings indicate that 69% of the respondents believe that women are at risk of increase in domestic gender-based violence due to the pandemic. Respondent’s perceptions on collaborative knowledge building and social capital varies significantly. The hypothesis testing has shown that all the hypothesis is supported due to attainment of path coefficient greater than 0.1, t-value of greater than 1.96 and level of significance of above 0.05.

Construction of Data Resource Sharing Platform for College Students’ Ideological and Political Education Based on Constraint Clustering

Online learning communities have changed the “silo” learning structure of traditional online learning and provided an effective support environment for wisdom sharing and collaborative knowledge building. The construction of a good online learning community has become a core issue in the field of online learning research. The study abstracts the construction of online learning community as a constrained clustering problem and proposes an intelligent construction method for online learning community based on constrained clustering. In particular, the three principles include the combination of limited freedom of choice and continuous iterative improvement, the combination of openness and standardization of the sharing platform, and the combination of public welfare and paid attributes of data resources. This paper discusses how to share valuable basic statistical data and social survey data in the field of college students’ ideological and political education (IPE) and make them public to the whole society by building a data resource sharing platform, so as to improve the utilization of data on the one hand and support comparative research topics in related fields through statistical analysis among survey data on the other hand. The experimental results show that the proportion of low-level posts (KC1 and KC2) of the two groups of students is relatively high (79.4% and 76.5%, respectively), indicating that the students in the learning community have carried out a lot of knowledge sharing and discussed and compared them to a certain extent. The proportion of high-level posts by students in both groups is very small (20.8% and 23.5%, respectively), but the number of high-level (KC3, KC4) posts by the students in the experimental group is significantly higher than that of the students in the control group, indicating that the students in the experimental group have good exchanges and discussions have resulted in meaningful negotiation and testing on some issues.

Mining Large Open Online Learning Networks: Exploring Community Dynamics and Communities of Performance

Online learning communities are becoming increasingly popular as they are known to support collaborative dialogue and knowledge building. Previous studies have typically focused on small, closed learning communities from an individual, static, and aggregated perspective. This research aims to advance our understanding of open and large online learning networks by exploring and characterizing community level dynamics and communities of performance. To achieve this goal, we mined a large open online learning network of over 30,000 students and approximately one million posts. First, we analyzed overall community network development by building temporal social networks. Subsequently, we studied sub-community dynamics using community detection algorithms, and, following that, investigated the interaction between community dynamics and communities of performance using best colleague correlation and Kruskal-Wallis test. Results found that large open online learning communities begin with a very large network having numerous small sub-communities. These communities consist of students who are similar in performance with strong links. The overall network size gradually shrinks, as does the number of sub-communities, and these communities evolve over time for their membership formulation with students who are more different in performance with weaker ties. Theoretical, practical, and methodological implications are then discussed. This study pushed the online learning community research to examine large and open networks by taking a more community-based and dynamic view of investigation.

Collaborative analytics-supported reflective Assessment for Scaffolding Pre-service Teachers’ collaborative Inquiry and Knowledge Building

Helping pre-service teachers (PSTs) develop competencies in collaborative inquiry and knowledge building is crucial, but this subject remains largely unexplored in CSCL. This study examines the design and process of collaborative analytics-supported reflective assessment and its effects on promoting PSTs to develop their competencies in collaborative inquiry and knowledge building. We used a quasi-experimental design that lasted 18 weeks. The experimental group was a class of 40 PSTs who took a liberal studies course with a knowledge building design enhanced by collaborative analytics-supported reflective assessment. The comparison group was a class of 28 PSTs taught by the same instructor who studied the same inquiry topics but experienced a regular knowledge building environment using portfolios. The analysis of the PSTs’ Knowledge Forum discourse showed that collaborative analytics-supported reflective assessment helps PSTs develop collaborative inquiry competencies for community knowledge advancement. The analysis of the PSTs’ reflection using collaborative analytics and prompt questions showed that the design using KBDeX visualization and knowledge building rubrics helped them engage in productive collaborative knowledge building inquiry by involving them in continuous monitoring, analysis, negotiation, synthesis of inquiry, identification of promising routes for inquiry, and actions to guide further collective inquiry. Implications for designing CSCL collaborative-analytics enriched with reflective assessment and student agency, and broadening CSCL and knowledge building approaches to pre-service teacher education are discussed.

Cross-community knowledge building with idea thread mapper

Research on computer-supported collaborative learning faces the challenge of extending student collaboration to higher social levels and enabling cross-boundary interaction. This study investigated collaborative knowledge building among four Grade 5 classroom communities that studied human body systems with the support of Idea Thread Mapper (ITM). While students in each classroom collaborated in their local (home) discourse space to investigate various human body functions, they generated reflective syntheses— “super notes”—to share knowledge progress and challenges in a cross-community meta-space. As a cross-community collaboration, students from the four classrooms further used the Super Talk feature of ITM to investigate a common problem: how do people grow? Data sources included classroom observations and videos, online discourse within each community, students’ super notes and records of Super Talk discussion shared across the classrooms, and student interviews. The results showed that the fifth-graders were able to generate high quality super notes to reflect on their inquiry progress for cross-classroom sharing. Detailed analysis of the cross-classroom Super Talk documented students’ multifaceted understanding constructed to understand how people grow, which built on the diverse ideas from each classroom and further contributed to enriching student discourse within each individual classroom. The findings are discussed focusing on how to approach cross-community collaboration as an expansive and dynamic context for high-level inquiry and continual knowledge building with technology support.