Group awareness and regulation in computer-supported collaborative learning

Group awareness is of critical relevance for collaborative learning and interaction and is thus often referred to in CSCL research. However, the concept is only vaguely defined as some kind of understanding or perception of characteristics of learning partners or the collaborating group. Most CSCL research activities concerned with group awareness aim at modifying learners' awareness using so-called group awareness tools. However, there are much less attempts to measure group awareness and to conceptualize its formation. Thus, building on existing group awareness research, this article derives a conceptualization with six defining aspects of group awareness: (1) group awareness is cognitive, (2) group awareness is conscious, (3) group awareness is current, (4) group awareness is individual, (5) group awareness is social, and (6) group awareness is perceived as valid. Additionally, while it is often assumed that group awareness builds on self-regulatory skills, its role in regulating behavior and cognition within a social context is seldom explored. Thus, this article aims at defining and analyzing the concept of group awareness, specifying its relation to regulatory processes, and sketching possible research paths whilst building on, complementing, and informing tool-driven research.

     

Visualization of agreement and discussion processes during computer-supported collaborative learning

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.     

计算机支持的协作学习的伙伴模型

伙伴模型的建立是计算机支持的协作学习（CSCL）中寻找学习伙伴的关键。伙伴模型对学生分组的关键因素进行了较全面的建模。对伙伴模型进行了形式化的描述,并重点对动态协作信息的表示，如伙伴学习进度、知识水平、认知能力和协作能力进行了研究。     

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multi-dimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.

Measurement and assessment in computer-supported collaborative learning

The overall goal of CSCL research is to design software tools and collaborative environments that facilitate social knowledge construction via a valuable assortment of methodologies, theoretical and operational definitions, and multiple structures [Hadwin, A. F., Gress, C. L. Z., & Page, J. (2006). Toward standards for reporting research: a review of the literature on computer-supported collaborative learning. In Paper presented at the 6th IEEE International Conference on Advanced Learning Technologies, Kerkrade, Netherlands; Lehtinen, E. (2003). Computer-supported collaborative learning: an approach to powerful learning environments. In E. De Corte, L. Verschaffel, N. Entwistle & J. Van Merriëboer (Eds.), Unravelling basic components and dimensions of powerful learning environments (pp. 35–53). Amsterdam, Netherlands: Elsevier]. Various CSCL tools attempt to support constructs associated with effective collaboration, such as awareness tools to support positive social interaction [Carroll, J. M., Neale, D. C., Isenhour, P. L., Rosson, M. B., & McCrickard, D. S. (2003). Notification and awareness: Synchronizing task-oriented collaborative activity. International Journal of Human–Computer Studies 58, 605] and negotiation tools to support group social skills and discussions [Beers, P. J., Boshuizen, H. P. A. E., Kirschner, P. A., & Gijselaers, W. H. (2005). Computer support for knowledge construction in collaborative learning environments. Computers in Human Behavior 21, 623–643], yet few studies developed or used pre-existing measures to evaluate these tools in relation to the above constructs. This paper describes a review of the measures used in CSCL to answer three fundamental questions: (a) What measures are utilized in CSCL research? (b) Do measures examine the effectiveness of attempts to facilitate, support, and sustain CSCL? And (c) When are the measures administered? Our review has six key findings: there is a plethora of self-report yet a paucity of baseline information above collaboration and collaborative activities, findings in the field are dominated by ‘after collaboration’ measurement, there is little replication and an over reliance on text-based measures, and an insufficient collection of tools and measures for examining processes involved in CSCL.     

Exploring regulatory processes during a computer-supported collaborative learning task using process mining

The purpose of this study was to explore sequences of social regulatory processes during a computer-supported collaborative learning task and their relationship to group performance. Analogous to self-regulation during individual learning, we conceptualized social regulation both as individual and as collaborative activities of analyzing, planning, monitoring and evaluating cognitive and motivational aspects during collaborative learning. We analyzed the data of 42 participants working together in dyads. They had 90 min to develop a common handout on a statistical topic while communicating only via chat and common editor. The log files of chat and editor were coded regarding activities of social regulation. Results show that participants in dyads with higher group performance (N = 20) did not differ from participants with lower group performance (N = 22) in the frequencies of regulatory activities. In an exploratory way, we used process mining to identify process patterns for high versus low group performance dyads. The resulting models show clear parallels between high and low achieving dyads in a double loop of working on the task, monitoring, and coordinating. Moreover, there are no major differences in the process of high versus low achieving dyads. Both results are discussed with regard to theoretical and empirical issues. Furthermore, the method of process mining is discussed.     

Gender-related differences in computer-mediated communication and computer-supported collaborative learning

Abstract   A question associated with the introduction of computer-supported collaborative learning (CSCL) is whether all participants profit equally from working in CSCL environments. This article reports on a review study into gender-related differences in participation in CSCL. As many of the processes in CSCL are similar to those in computer-mediated communication (CMC), studies into CMC are also included in the review. Male dominance is found to play a role in many CMC settings. A learning culture with an explicit focus on participation by all students seems to be related to a more gender-balanced participation in CMC, however. A tendency for boys to be more active participants than girls is also present in CSCL environments, but it is less pronounced than in CMC. This may be explained by the fact that participation is explicitly promoted in most CSCL environments. Gender differences in the character of students' contributions are found in both CMC and CSCL. It is concluded that in order to avoid gender-stereotyped participation and communication patterns, it is necessary to explicitly address inclusiveness as an aspect of a collaborative classroom culture. A plea is made for further research into differential participation by students in CSCL, and the effects thereof on cognitive and affective learning outcomes. Research should also focus on the question how classroom cultures can be promoted that support active participation of all students aimed at collaborative knowledge construction.     

Exploring gender and gender pairing in the knowledge elaboration processes of students using computer-supported collaborative learning

The aim of the study is to investigate the influence of gender and gender pairing on students’ learning performances and knowledge elaboration processes in Computer-Supported Collaborative Learning (CSCL). A sample of ninety-six secondary school students, participated in a two-week experiment. Students were randomly paired and asked to solve several moderately structured problems concerning Newtonian mechanics. Students’ pretest and posttest performances were analyzed to see whether students’ gender and the gender pairing (mixed or single-gender) were significant factors in their problem solving learning in CSCL. Students’ online interactions were also analyzed to unravel the dynamic process of individual knowledge elaboration. The multilevel analyses revealed that a divergent pattern of knowledge elaboration was a significant predictor for students’ learning achievement, and in mixed-gender dyads students’ knowledge elaboration processes were more inclined to diverge from each other. Moreover, females in single-gender dyads significantly outperformed females in mixed-gender dyads. But this was not the case for male students.     

Measuring perceived sociability of computer-supported collaborative learning environments

Most asynchronous computer-supported collaborative learning (CSCL) environments can be characterized as functional environments because they focus on functional, task-specific support, often disregarding explicit support for the social (emotional) aspects of learning in groups which are acknowledged by many educational researchers to be essential for effective collaborative learning. In contrast, sociable CSCL environments emphasize the social (emotional) aspects of group learning. We define sociability as the extent to which a CSCL environment is perceived to be able to facilitate the emergence of a sound social space with attributes as trust and belonging, a strong sense of community, and good working relationships. Specific environmental characteristics, which we have designated social affordances, determine sociability.     

Heterogeneity of learning material in synchronous computer-supported collaborative modelling

This paper examines the effect of heterogeneous resources, available to students, during computer-supported collaborative problem solving. A study of collaborative modeling has been conducted in the frame of an authentic educational activity in a secondary school. The students involved were provided with sets of primitive resources of varying degrees of heterogeneity to be used during synchronous computer-mediated modeling activities. Analysis of students' peer interaction and of the produced solutions revealed that, contrary to our expectations, the group with heterogeneous resources produced solutions of similar quality to those of the reference group, although they were more active, they exchanged more messages, they were involved in deeper discussions and collaborated more for building the constituent parts of the solution.     

Investigating the complexity of computer-supported collaborative learning in action

International Journal of Computer-Supported Collaborative Learning -     

Developing argumentation processing agents for computer-supported collaborative learning

In this study, an intelligent argumentation processing agent for computer-supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. The proposed argumentation processing agent then scrutinizes each learner’s learning portfolio on e-learning platform and automatically issues feedback messages in case devious argument or abnormal behavior that is unfitted to the learners’ learning style is detected. The Moodle (http://moodle.org), an open source software e-learning platform, is used to establish the cooperative learning environment for this study. The experimental results revealed that the learners benefited by the argumentation activity with the assistance of the proposed learning style aware argumentation processing agent.     

Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups

This study reports the impact of high sensitivity to early exchange in 11th-grade, CSCL triads solving well- and ill-structured problems in Newtonian Kinematics. A mixed-method analysis of the evolution of participation inequity (PI) in group discussions suggested that participation levels tended to get locked-in relatively early on in the discussion. Similarly, high (low) quality member contributions made earlier in a discussion did more good (harm) than those made later on. Both PI and differential impact of member contributions suggest a high sensitivity to early exchange; both significantly predicting the eventual group performance, as measured by solution quality. Consequently, eventual group performance could be predicted based on what happened in the first 30–40% of a discussion. In addition to drawing theoretical and methodological implications, implications for scaffolding CSCL groups are also discussed.     

Learning technologies: Affective and social issues in computer-supported collaborative learning

This paper is concerned with affective issues in learning technologies in a collaborative context. Traditionally in learning there has been a division between cognition and affect: where cognition is concerned with skills and processes such as thinking and problem-solving and affect with emotional areas such as motivation, attitudes, feelings. Affective issues have been viewed as somewhat problematic in studying learning, so although it is well known that learner attitude, motivation, and emotional state are very important, they have often been excluded from the frame of research, or studied separately from cognitive learning. This position is gradually changing and this paper considers what previous research has been conducted in these areas. It discusses the role of affective factors in three main areas of collaboration: in settings where learners are co-located, in on-line communities and to support and develop socio-emotional skills. It considers relevant developments in these areas, what the outcomes have been and suggests important directions for future research.     

Differences in learning processes between successful and less successful students in computer-supported collaborative learning in the field of human nutrition and health

This study aimed to investigate the differences in learning processes between successful and less successful pairs of students in computer-supported collaborative learning (CSCL) in the field of human nutrition and health. As part of their regular MSc (and optional BSc) course “Exposure assessment in nutrition and health research” at Wageningen University, 44 students were asked (as an individual pretest) to design and analyze a study which evaluates a certain dietary assessment method. Subsequently, they were asked to discuss their evaluation studies in randomized pairs using a CSCL platform. As an individual posttest, students had to re-design and re-analyze the same evaluation study. The quality of students’ knowledge construction in both tests and characteristics of their learning processes in the CSCL environment were assessed through two coding schemes. Based on their learning outcomes (quality of knowledge construction), pairs of students were divided into two subgroups: successful and less successful students. Next, the learning processes of these subgroups were compared. This study revealed that the learning processes of successful and less successful students in the CSCL environment differed in terms of relevance, width and depth of discussion and justification and reasoning. Based on these findings, recommendations for further research and educational practice are formulated.     

Experiences with CLARE: a computer-supported collaborative learning environment

Current collaborative learning systems focus on maximizing shared information. However, "meaningful learning" is not simply information sharing but also knowledge construction. CLARE is a computer-supported learning environment that facilitates meaningful learning through collaborative knowledge construction. It provides a semi-formal representation language called RESRA and an explicit process model called SECAI. Experimental evaluation through 300 hours of classroom usage indicates that CLARE does support meaningful learning. It also shows that a major bottleneck to computer-mediated knowledge construction is summarization. Lessons learned through the design and evaluation of CLARE provide new insights into both collaborative learning systems and collaborative learning theories.