International Journal of Computer-Supported Collaborative Learning最新文献

A key commitment of computer-supported collaborative learning research is to study how people learn in collaborative settings to guide development of methods for capture and design for learning. Computer-supported collaborative learning research has a tradition of studying how the physical world plays a part in collaborative learning. Within the field, a material turn is emerging that considers how digital and tangible technologies actively contribute to collaborative learning processes. Studying how tangible materials produce collaborative learning visibly and algorithmically is particularly important at a time when advanced algorithms are integrated into educational contexts in ways that are not always transparent. However, the needed methodologies for capturing how non-human agents take part in collaborative learning remains underdeveloped. The present study builds on current CSCL research that investigates materials in collaborative learning and introduces posthumanist perspectives with the aim to decenter humans methodologically and to probe empirically whether and how these perspectives contribute to empirical understanding of collaborative learning processes. Taking fiber crafts (e.g., weaving and fabric manipulation) as a context for computational learning, the present study conducted a posthumanist analysis of differences among human and non-human participants in collaboration using video data to investigate how middle school youths and fiber craft components performed algorithms over time. The findings show how both youths and craft materials actively contributed to the performance of algorithms. In weaving, algorithms became repeated youth-material movements one dimension at a time. In fabric manipulation, algorithms became a repeated confluence of component parts. Decentering humans through an analysis of differences among human and non-human introduced human-material collaboration as a productive unit of analysis for understanding how materials and people together contribute to producing what can be recognized as computational performance. The findings of this research contribute to ongoing conversations in CSCL research on how computational materials can be considered in collaborative learning and present a new approach to capture collaborative learning as physical expansion over time. The study has implications for future research on capturing collaborative computational learning and designing physical computational learning opportunities that show technology as evolving.

By adopting a situated and interactional approach, we explore students’ immersive VR experiences as resources for collaborative meaning making and learning. We draw on data from a project in which teachers and researchers co-developed a learning design for upper secondary students involving immersive VR technology. In this learning design, students viewed a cinematic VR film where they encountered different people telling personal stories about exclusion and discrimination, followed by reflective group dialogues with their teacher about their experiences in this environment. Through a detailed interaction analysis of these dialogues, we identify four dimensions that characterize students’ meaning making: (1) the feeling of taking part in conversations, (2) attending to bodily expressions of others, (3) students’ own bodily responses, (4) teacher guidance. We discuss how the findings from our analysis contribute to the field of CSCL, and which also have implications for instructional work that includes the use of immersive VR environments.

Abstract

We investigated how the level of variance in students’ prior knowledge may have influenced their collaborative interactions and science learning in small groups. We examined learning outcomes from 102 groups from seven science teachers’ classes and discourse from two contrasting groups: Homogeneous versus heterogeneous. We examined individual and group outcomes using hierarchical linear modeling (HLM) to explore the effect of membership in a homogeneous or heterogeneous group on students’ learning. We then used social network analyses (SNA) to identify any differences in interaction patterns between the two contrasting groups as they conducted multiple compost simulations. Finally, we examined students’ discussions in these groups to better understand their interactions. In our HLM analysis, we found that students in homogeneous groups made significantly greater learning gains than students in heterogeneous groups. The SNA and thematic analysis of the discussions in our contrasting groups helped us identify that the interactions in the homogeneous group were more distributed, while the interactions in the heterogeneous group were more centralized around the member with the greatest prior knowledge, and that these patterns were stable over time. We also found that the students in the homogenous group engaged in richer discussions that were more supportive and built upon one another’s ideas, which may have influenced their group and individual learning outcomes. While our findings indicate that students in homogeneous groups learn more and collaborate better, we discuss how some heterogeneity may be helpful, and group formation should focus on avoiding extreme cases of heterogeneity and provide students with scaffolding for collaboration.

Teachers in a collaborative learning (CL) environment have the demanding task of monitoring several groups of students at the same time and intervening when needed. This withitness (both the situational awareness and interventions taken in class) of the teacher might be increased with the help of a guiding dashboard alerting the teacher of problems and providing suggestions for interventions. This paper introduces a quasi-experiment carried out in authentic classrooms. We examined how a mirroring and an alerting & guiding dashboard affected the withitness of teachers in a face-to-face learning environment while students discussed and used a collaborative writing tool. Twenty-four teachers were observed, interviewed, and answered surveys in three different conditions altogether: with no extra information about the situation, using a dashboard mirroring low-level data about the collaboration, and additionally an AI assistant indicating problems in pedagogical terms and potential solutions (i.e., a guiding dashboard). The results show that the situational awareness of the teachers increased with the introduction of a mirroring dashboard. The workload of the participating teachers dropped more with the introduction of an alerting & guiding dashboard, helping teachers feel less frustrated and more accomplished.

The individual problem-solving (IPS) and collaborative problem-solving (CPS) have received a lot of attention, yet little research has been conducted to investigate whether CPS and IPS are equally effective in improving students’ understanding of physics concepts, problem-solving abilities, and minimizing achievement gaps. Therefore, the present study developed two types of online electromagnetism problem solving programs with simulation—IPS and CPS—for 8th grade students over five class sessions. Students in the CPS group significantly outperformed those in the IPS group on their performance of physics problem solving test and online problem-solving solution, while IPS and CPS both affected their physics concept test performance to the same degree. The CPS group allocated more time to the online problem-solving solution, evidence-based reasoning, simulation and data reporting than the IPS group. Both CPS and IPS affected high-achievers' problem-solving performance to the same extent. Nonetheless, CPS was more effective in maximizing low-achievers' problem-solving performance and minimizing the discrepancy between high- and low-achievers than IPS, possibly because low-achievers in CPS group requested and received more support from high-achieving students. Regression analysis indicated that students' online problem-solving solution significantly predict their posttest performance in the physics concept test and physics problem-solving test.