Abstract:Collaboration has garnered global attention as an important skill for the 21st century. While researchers have been doing work on collaboration for nearly a century, many of the questions that the field is investigating overlook the need for students to learn how to read and respond to different collaborative settings. Existing research focuses on chronicling the various factors that predict the effectiveness of a collaborative experience, or on changing user behaviour in the moment. These are worthwhile resea… Show more
“…In the second article, Worsley, Anderson, Melo, and Jang (2021) talk about the importance of developing new data collection tools for supporting collaboration analytics. They present a paper describing the development of a tool called BLINC (Building Literacy in In-Person Collaboration) that was informed by data collected from university students regarding their concerns about collaboration.…”
Section: Summary Of Papersmentioning
confidence: 99%
“…Collaboration analytics is a perfect example of this -by developing new data collection tools that allow us to visualize social interactions in real time, it could allow students to better understand group interactions and how to interact with others. The BLINC platform (Worsley et al, 2021) is a first step in this direction. By providing information about the group's discussion, the tool allows students to start a conversation on how their collaborative processes could be improved.…”
Section: What Is Collaboration Analytics?mentioning
This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.
“…In the second article, Worsley, Anderson, Melo, and Jang (2021) talk about the importance of developing new data collection tools for supporting collaboration analytics. They present a paper describing the development of a tool called BLINC (Building Literacy in In-Person Collaboration) that was informed by data collected from university students regarding their concerns about collaboration.…”
Section: Summary Of Papersmentioning
confidence: 99%
“…Collaboration analytics is a perfect example of this -by developing new data collection tools that allow us to visualize social interactions in real time, it could allow students to better understand group interactions and how to interact with others. The BLINC platform (Worsley et al, 2021) is a first step in this direction. By providing information about the group's discussion, the tool allows students to start a conversation on how their collaborative processes could be improved.…”
Section: What Is Collaboration Analytics?mentioning
This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.
“…Further, we incorporate the use of ENA and its visualisations to support our collaborative autoethnography process. This use of ENA primarily to support our reflective process (Alhadad, 2018;Worsley et al, 2021) (rather than of analysis of research findings per se) is novel. Collectively, all authors found that our collaborative reflections were enhanced using ENA as it helped us interrogate and question our assumptions and understanding of each other, and in supporting our reflexivity beyond what we were able to engage in prior to introducing the ENA in our CAE.…”
Section: Concluding Reflections and Implicationsmentioning
We interrogated a students as partners (SaP), co-curricular program that focuses on supporting student learning. To center power and equity in SaP, the program was grounded in social design-based experiment methodology. We considered the manifestation of power and equity beyond higher education, to that of broader socio-political contexts. Collaborative autoethnography (CAE) was used to garner a richer understanding of student-staff experiences of the program. Through CAE, power emerged as central to our collective experiences, and a recognition that power asymmetry in students as partners programs is complex and multi-layered. We found that to address power imbalances in these programs requires considered strategies and intentional designs. Further, CAE, in and of itself, can be a powerful way to foster self-awareness, mutual trust, respect, and the acknowledgement of others in student-staff partnerships. We conclude by recommending the importance of deliberate design for equity and power towards consequential learning and transformational change.
“…Additionally, current research showcases an urgency for guiding systems: Molenaar and Knoop-Van Campen (2019) and Amarasinghe et al (2020) suggest that a recommendation service should be developed to existing LA systems. Correspondingly, Worsley et al (2021) specifically draw attention to the need for systems identifying problems and at the same time offering possible avenues for solving the issues. Ultimately, the focus of collaboration analytics should be on really having an impact on teaching and learning about how to collaborate better (Wise and Jung, 2019;Worsley et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Correspondingly, Worsley et al (2021) specifically draw attention to the need for systems identifying problems and at the same time offering possible avenues for solving the issues. Ultimately, the focus of collaboration analytics should be on really having an impact on teaching and learning about how to collaborate better (Wise and Jung, 2019;Worsley et al, 2021). Until now, what kind of pedagogical action to choose for a particular group of students has remained an untouched avenue (van Leeuwen and Rummel, 2020).…”
Orchestrating collaborative learning (CL) is difficult for teachers as it involves being aware of multiple simultaneous classroom events and intervening when needed. Artificial intelligence (AI) technology might support the teachers’ pedagogical actions during CL by helping detect students in need and providing suggestions for intervention. This would be resulting in AI and teacher co-orchestrating CL; the effectiveness of which, however, is still in question. This study explores whether having an AI assistant helping the teacher in orchestrating a CL classroom is understandable for the teacher and if it affects the teachers’ pedagogical actions, understanding and strategies of coregulation. Twenty in-service teachers were interviewed using a Wizard-of-Oz protocol. Teachers were asked to identify problems during the CL of groups of students (shown as videos), proposed how they would intervene, and later received (and evaluated) the pedagogical actions suggested by an AI assistant. Our mixed-methods analysis showed that the teachers found the AI assistant useful. Moreover, in multiple cases the teachers started employing the pedagogical actions the AI assistant had introduced to them. Furthermore, an increased number of coregulation methods were employed. Our analysis also explores the extent to which teachers’ expertise is associated with their understanding of coregulation, e.g., less experienced teachers did not see coregulation as part of a teacher’s responsibility, while more experienced teachers did.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.