Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

Systemic intervention for computer-supported collaborative learning

This paper presents a systemic intervention approach as a means to overcome the methodological challenges involved in research into computer-supported collaborative learning applied to the promotion of mathematical problem-solving (CSCL-MPS) skills in schools. These challenges include how to develop an integrated analysis of several aspects of the learning process; and how to reflect on learning purposes, the context of application and participants' identities. The focus of systemic intervention is on processes for thinking through whose views and what issues and values should be considered pertinent in an analysis. Systemic intervention also advocates mixing methods from different traditions to address the purposes of multiple stakeholders. Consequently, a design for CSCL-MPS research is presented that includes several methods. This methodological design is used to analyse and reflect upon both a CSCL-MPS project with Colombian schools, and the identities of the participants in that project

From multiple perspectives to shared understanding

The aim of this study was to explore how learners operating in a small group reach shared understanding as they work out joint research questions and build a theoretical framework and to identify the resources and tools they used in the process. The learners’ own interpretations of their group activities and learning were also taken into account. The data, consisting of group discussions and the documents produced by the group, were subjected to a qualitative content analysis. The group members employed a variety of resources and tools to exchange their individual perspectives and achieve shared understanding. Summaries of relevant literature laid a foundation for the group’s theoretical discussions. Reflective comparisons between their book knowledge and their personal experiences of online interaction and collaboration were frequent, suggesting that such juxtapositions may have enhanced their learning by intertwining the content to be mastered and the activities entailed by this particular content

Content analysis: What are they talking about?

Quantitative content analysis is increasingly used to surpass surface level analyses in Computer-Supported Collaborative Learning (e.g., counting messages), but critical reflection on accepted practice has generally not been reported. A review of CSCL conference proceedings revealed a general vagueness in definitions of units of analysis. In general, arguments for choosing a unit were lacking and decisions made while developing the content analysis procedures were not made explicit. In this article, it will be illustrated that the currently accepted practices concerning the ‘unit of meaning’ are not generally applicable to quantitative content analysis of electronic communication. Such analysis is affected by ‘unit boundary overlap’ and contextual constraints having to do with the technology used. The analysis of e-mail communication required a different unit of analysis and segmentation procedure. This procedure proved to be reliable, and the subsequent coding of these units for quantitative analysis yielded satisfactory reliabilities. These findings have implications and recommendations for current content analysis practice in CSCL research

Analyzing collaborative learning processes automatically

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 multidimensional 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

The usage of computer integrated classroom (cic) technology tools in the study of interactions of knowledge construction among esl pre-service teacher

This paper takes a glimpse at the possible tools for collecting data on interactions of knowledge construction among ESL pre-service teacher. The main tool identified to compile the data collection of the study is a customized of computer integrated classroom (CiC) system. For that purpose, a pilot study on computer support face to face peer response using CiC was trialed with a group of students enrolled in a Microteaching course at the Faculty of Education, University Technology Malaysia. CiC was explored to see whether the system could facilitate both modes of synchronous interactions: text-based reporting and verbal interaction. With the assistance of software and hardware integrated in CIC, many computer supported collaborative learning activities could be carried out by ESL pre-service teachers such as recording, storing, retrieving, and monitoring of user profiles’ activities, learning materials and interactions

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers