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This study investigated how university students (n = 28) coordinated their collaborative online writing and what kinds of coordination profiles were found among the students. Further, the study examined the quality of the essays produced by groups of students varying in their combinations of coordination profiles. Students' written comments on their writing processes (n = 583) were divided into episodes focusing on coordination. Eight different categories of collaborative activities during online collaboration were found. The students' joint essays (n = 9) were evaluated as high, moderate and low according to the number of topics, key concepts, and integration of Internet sources in the essays. Coordination profiles were identified by K-means cluster analysis. The students mainly coordinated their collaborative writing through text-related activities, task-related activities as well as social activities. Four distinct coordination profiles were found, showing that the students coordinated their collaborative writing process in different ways. Technical problems seemed to have a negative effect on essay quality.

Teacher orchestration of the classroom is a demanding task because of the multitude of activities and the rapid pace at which these activities occur. Teachers must constantly be aware of the activities students engage in and the progress students are making, in order to be able to make informed decisions. Recent years have seen the development of learning analytics (LA) to support teachers, in the form of interfaces that capture and visualise data about learner activities. LA are hypothesised to deliver actionable knowledge, because the provided information would enable teachers to immediately translate the information into concrete intervention of some kind. This paper provides a short discussion of the hypothesised affordances of LA for supporting teachers, and subsequently, a case study is presented in which a teacher's sense making strategies of LA were investigated within a digital environment, which enabled the teacher to monitor and regulate the activities of five collaborating groups of students. Besides automatically logged teacher behaviour, eye tracking was used to examine eye movements, and cued retrospective reporting was used to evaluate the choices and decisions made by the teacher. The case study is used to discuss directions for future research concerning learning analytics to support teachers.

This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a single solution. Each phase was foreseen with a part-task-specific representational tool facilitating visualization of the domain-content (i.e., a conceptual, causal and simulation tool respectively for the subsequent phases). Whereas all teams of learners (N = 17) were scripted to carry out the part-tasks in the predefined order, teams were instructed to (1) inspect expert visualizations (n = 8) or (2) construct their own domain-specific visualizations (n = 9). Results indicate that constructing visualizations, in comparison to inspecting them, evokes more meaningful discussion of the domain-content beneficially affecting team complex learning-task performance and individual learning gains (i.e., higher post-test score). (Contains 8 tables and 5 figures.)

This study investigated how students collaborate in a CSCL environment and how this collaboration affects group performance. To answer these questions, the collaborative process of 101 groups of secondary education students when working on a historical inquiry task was analyzed. Our analyses show that group members devote most of their efforts to regulation of task-related activities. For example, by formulating plans or strategies or monitoring task progress. Group members also engaged in social activities often (e.g., disclosing personal information, joking). Less attention was paid to exchange of task-related information (e.g., asking task-related questions) and regulation of social activities (e.g., planning and monitoring the collaboration). Exploratory factor analysis was used to identify the interrelationships between the different collaborative activities. This analysis showed that collaborative activities can be grouped in four broad categories: discussion of information, regulation of task-related activities, regulation of social activities, and social activities. These activities were then used to predict group performance using multiple regression analysis. No effect of discussion of information and regulation of task-related activities on group performance were found. Regulation of social activities positively affected group performance, whereas social interaction negatively affected group performance. As in this study no inferences could be made about the causal relation between collaboration and performance, future research should attempt to focus on this relationship, for example by investigating more closely how different individual and group factors affect collaboration and group performance. (Contains 8 tables.)

Traditional research on collaborative learning employs a "black box" approach that makes it difficult to gain a deeper understanding of the differential effects of collaborative learning. To make the black box transparent, researchers have studied the process of collaboration, in order to establish which interaction features are likely to make learning more effective and efficient for group members. Although cognitive load theory has been developed in the context of individual learning situations, it may provide a promising new way of looking inside the black box, assuming that students working in groups have more processing capacity than students working individually. The aim of this article is to provide an overview of the process-oriented and cognitive-load approaches to conducting collaborative learning research, to highlight their respective advantages and disadvantages, and to suggest how they can be combined in order to address new research questions.

This article reports on a qualitative study of the construct of shared epistemic agency, investigated in the context of collaborative instructional design activities of university students. The aim of the study is to shed light on the notion of shared epistemic agency and to create empirical grounding for its theoretical description. The current study provides an account of the construct, based on a review of several theoretical conceptualizations and on the analysis of empirical material from 2 case studies from university education. We investigate shared epistemic agency within the specific framework of the knowledge creation perspective on learning, viewing it as the capacity that enables deliberate collaborative efforts of groups to create shared knowledge objects. Our study identifies and describes 2 core dimensions of shared epistemic agency: the epistemic and the regulative dimensions. We identify actions within each dimension that indicate the manifestation of shared epistemic agency in the practice of collaborative creation of shared knowledge objects. This study also distinguishes patterns of action that provide indications of how shared epistemic agency is differently articulated in the 2 groups' collaborative object-oriented activities. (Contains 6 footnotes and 7 tables.)

This research investigates the role of representational guidance by comparing the effects of two different representational tools. We used a design with two different groups defined by the type of argumentative diagram students co-constructed while working in a computer-supported collaborative learning (CSCL) environment. The Graphical Debate-tool offered different representational guidance than the Textual Debate-tool. The results show that groups that worked with the Graphical Debate-tool constructed representations of higher quality and wrote essays that were better in terms of grounds quality. Furthermore, working with the Graphical Debate-tool was found to have a positive effect on students' learning as measured by a knowledge post-test. In contrast to our expectations however, there was little difference between the two conditions regarding the online collaboration process. It can be concluded that representational guidance has an impact on group and individual performance and should therefore be taken into account during instructional design.

Although protocol analysis can be an important tool for researchers to investigate the process of collaboration and communication, the use of this method of analysis can be time consuming. Hence, an automatic coding procedure for coding dialogue acts was developed. This procedure helps to determine the communicative function of messages in online discussions by recognizing discourse markers and cue phrases in the utterances. Five main communicative functions are distinguished: "argumentative," "responsive," "informative," "elicitative," and "imperative." A total of 29 different dialogue acts are specified and recognized automatically in collaboration protocols. The reliability of the automatic coding procedure was determined by comparing automatically coded dialogue acts to hand-coded dialogue acts by a human rater. The validity of the automatic coding procedure was examined using three different types of analyses. First, an examination of group differences was used (dialogue acts used by female versus male students). Ideally, the coding procedure should be able to distinguish between groups who are likely to communicate differently. Second, to examine the validity of the automatic coding procedure through examination of experimental intervention, the results of the automatic coding procedure of students, with access to a tool that visualizes the degree of participation of each student, were compared to students who did not have access to this tool. Finally, the validity of the automatic coding procedure of dialogue acts was examined using correlation analyses. Results of the automatic coding procedure of dialogue acts of utterances (form) were related to results of a manual coding procedure of the collaborative activities to which the utterances refer (content). The analyses presented in this paper indicate promising results concerning the reliability and validity of the automatic coding procedure for dialogue acts. However, limitations of the procedure were also found and discussed.

This study investigated the effects of visualization of participation during computer-supported collaborative learning (CSCL). It is hypothesized that visualization of participation could contribute to successful CSCL. A CSCL-environment was augmented with the Participation Tool (PT). The PT visualizes how much each group member contributes to his or her group's online communication. Using a posttest-only design with a treatment (N=52) and a control group (N=17), it was examined whether students with access to the PT participated more and more equally during collaboration, reported higher awareness of group processes and activities, collaborated differently, and performed better than students without access to the PT. The results show that students used the PT quite intensively. Furthermore, compared to control group students, treatment group students participated more and engaged more in coordination and regulation of social activities during collaboration by sending more statements that addressed the planning of social activities. However, equality of participation, awareness of group processes and quality of the group products was not higher in the treatment condition. Still, the results of this study demonstrate that visualization of participation can contribute to successful CSCL.

When a computer-based tool or application is used to carry out a specific task in a learning situation--that is, it is used for learning--more effectively or efficiently one speaks of learning "with" the tool or application. When, possibly, that same tool or application is used to enhance the way a learner works and thinks, and as such has effects that reach further than the learning situation in which it is used, then one speaks of learning "from" the tool or application. This article concentrates on the latter. It zooms in on the use of mindtools in education--computer programs and applications that facilitate meaningful professional thinking and working--because this is the epitome of learning "from" ICT. Mindtools and cognitive tools help users represent what they know as they transform information into knowledge and are used to engage in, and facilitate, critical thinking and higher order learning. These tools can be as simple as email and or discussion lists and as complicated as argument mapping and visualization systems. Even more specifically, it deals with one category of such tools, namely conversation tools; tools used to create and facilitate the establishment of technology-supported discourse communities--communities of practice--where collaboration can flourish. (Contains 1 footnote.)

In this article the authors focus on how features of a computer-supported collaborative learning (CSCL) environment can elicit and support domain-specific reasoning and more specifically historical reasoning. The CSCL environment enables students to collaborate on a historical inquiry task and in writing an argumentative essay. In order to support historical reasoning the authors compared two representational tools: a graphical representation (argumentative diagram) and a linear representation (argument list). As it is assumed that an argumentative diagram can support both cognitive and interaction processes, it was expected that using this tool would result in more qualitative historical reasoning, in the chat as well as in the essay. However, the results of this study did not show a significant difference in the amount of historical reasoning between the two conditions. A possible explanation can be found in the way the students make use of the representational tool while executing the task. The tool does not only function as a cognitive tool that can elicit elaborate activities, but also as a tool through which students communicate. (Contains 7 tables and 3 figures.)

Presents four Dutch teachers' experiences with concept mapping, identifying features of the concept mapping task that helps it provoke and support a productive student discourse and explaining that the design of the task can affect the quality of student interaction. The paper describes the concept mapping task, student articulation of feelings, elaboration of conceptual knowledge, and co-construction of meaning. (SM)

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.chb.2006.10.005 Byline: Jeroen Janssen, Gijsbert Erkens, Gellof Kanselaar Keywords: Computer-supported collaborative learning; Collaboration; Computer-mediated communication; Visualizations; Secondary education Abstract: This study examined the effects of the shared space (SS) on students' behaviors in a computer-supported collaborative learning (CSCL) environment. The SS visualizes discussion and agreement during online discussions. It was hypothesized the SS would increase the media richness of the CSCL-environment, would stimulate critical and exploratory group-norms, would lead to more positive perceptions of online collaboration, and would have an impact on students' collaborative activities. In total, 59 students working in 20 groups had access to the SS visualization, while 58 students working in 20 groups did not. The results show that students with access to the SS visualization: (a) perceived higher media richness; (b) had a more exploratory group-norm perception; (b) perceived more positive group behavior; (c) perceived their group's task strategies to be more effective; (d) engaged in different collaborative activities and (e) performed better on one part of the group task. These results demonstrate the potential benefits of visualizing agreement and discussion during CSCL. Author Affiliation: Research Centre Learning in Interaction, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands