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A Model for the Efficient Representation and Management of Online Collaborative Learning Interactions

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This study aims to explore the importance of the efficient representation and management of the event information generated from group activity in online collaborative learning practices so that we can use it to provide awareness about individual and
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  A Model for the Efficient Representation and Management of Online Collaborative Learning Interactions Santi CABALLÉ 1 , Thanasis DARADOUMIS 1 , Fatos XHAFA 2 1 Open University of Catalonia, Rambla Poblenou 156, 08018 Barcelona, Spain Tel: +34 93 2533600, Fax: +34 93 253 3610, Email: {scaballe,adaradoumis}@uoc.edu 2 Polytechnic University of Catalonia, Jordi Girona Salgado 1-3, 08034 Barcelona, Spain Tel: +34 93 354400, Fax: +34 93 3544050, Email: fatos@lsi.upc.es Abstract: This study aims to explore the importance of the efficient representation and management of the event information generated from group activity in online collaborative learning practices so that we can use it to provide awareness about individual and group behaviour. The achievement of this goal first involves the design of a conceptual model of collaborative learning interaction that structures and classifies the information generated in an online collaborative learning situation at several levels of description. This model is then translated into a computational  platform that can be used for the systematic construction of collaborative learning applications endowed with enriched capabilities for providing more efficient knowledge management and scaffolding. To test our model we describe the construction of a specific application, a structured forum, to be used in real situations.   1. Introduction Online collaborative learning is an emerging paradigm for research in educational technology that focuses on the use of information and communications technology (ICT) as a mediation tool within collaborative methods of learning [1]. When designing and implementing environments that support online collaborative learning several issues must  be taken into account in order to ensure full support to the online learning activity. One such issue is the representation and analysis of group activity interaction. Interaction analysis is a core function for the support of coaching and evaluation in online environments. It relies on information captured from the actions performed by the  participants during the collaborative process [1]. The efficient embedding of this information and of the extracted knowledge into online applications sets the basis for greatly enhancing both awareness and feedback [2] as crucial aspects to achieve a successful learning process in collaborative environments. Applications for online collaborative learning involve a high degree of user-user and user-system interaction and hence generate a huge amount of event information. This information is an important data source for supporting group activity with relevant information as well as for understanding, explaining and predicting patterns of group  behaviour and thus needs to be easily collected, represented and automatically processed by computational models in order to extract essential knowledge [3] about the collaborative  process. As a consequence, a successful online system needs to distinguish and account for explicitly three stages of information management: collection of information , analysis and knowledge extraction , and  presentation  (for awareness and feedback). Given that the large  amount of information generated during online group activity may require much time to be  processed, an effective way of collecting, analyzing and presenting this information is required; the ultimate objective is to efficiently embed the information and knowledge obtained into online applications in order to constantly provide group participants with as much awareness and feedback as possible. For the collection of information, the objective is to collect as much information as  possible generated during group activity as a basis for the next two stages. To this end, a solution is proposed consisting of the classification of information by means of three generic group activity parameters, namely collaborative learning product  , group  functioning  and scaffolding , which represent three high-level collaborative learning  processes. As regards analysis and knowledge extraction, a statistical model is defined, together with further complementary qualitative and quantitative supporting evaluation means, in order to process and analyze the collected information. This model is applied to a significant volume of data from real online collaborative learning situations. Finally, as regards presentation, the interpretation of the analysis results is also guided by the three high-level collaborative learning processes to lead to the inference of knowledge about individual and group activity. This knowledge is communicated back to the members of the learning group and their tutor in appropriate formats, thus providing valuable awareness and feedback of group interaction and performance. The paper is organized as follows. Section 2 presents a process that builds a conceptual model that ideally captures and classifies three main types of information generated in the group activity by means of potential indicators of effective collaboration. Section 3 briefly describes how the conceptual model is translated into a computational model of collaborative learning interaction that constitutes a generic platform that can be used for the systematic construction of collaborative learning applications endowed with enriched capabilities for providing more efficient knowledge management and scaffolding. Then, Section 4 discusses the design of a specific application, a structured forum, which is used as an example in real situations to validate the possibilities offered by this model as regards data analysis and management. We conclude in Section 5 with some comments and point out ongoing and further work. 2. A conceptual model for data analysis and management In the context of an asynchronous (and synchronous) online collaborative learning practice, we propose a conceptual model aiming at modelling different aspects of interaction and thus at helping all the actors involved understand the outcomes of the collaborative process. To this end, based on [4] and [5], we classified group activity information into three generic categories of activity: the outcome of collaboration  (the members’ contributing behaviour to the task), the  functioning of the group  (the management and organizational processes underlying the collaborative learning activities, such as participation behaviour, role  playing, etc.), and individual and group scaffolding  (social support and task- or group functioning-oriented help). Indeed, the specification of three high-level collaborative learning processes provides a  principled and effective manner to represent and manage the information generated from group interaction. On the one hand, it provides a classification of the many different variables that characterize collaborative interaction as well as the identification and measurement of these variables in terms of the user and system specific events (or actions). On the other hand, it facilitates the processing and analysis of this information and knowledge extraction This conceptualization enables the construction of a computational model to gather information in a structured manner and consequently to provide an easier and more efficient further processing and analysis of this information through different techniques (such as statistical and data mining, social network analysis etc.).   Next, we briefly describe each of these three processes. We employ a similar terminology to the one used in the Basic Support for Cooperative Work (BSCW) system [6] to refer to the actions that can be carried out in an asynchronous groupware platform. However, they are general enough to be abstracted and represent all the typical and basic actions encountered in any asynchronous groupware platform. 2.1 Collaborative Learning Outcome (or Task Performance) This is the first top-level activity parameter featuring the production function and task  performance of online groups. This parameter is characterized by the type of actions (events) that capture and describe the functional knowledge, cognitive processes and skills of the students and the group as a whole in solving problems and producing learning outcomes in a collaborative learning practice. The aim is to analyze and evaluate the individual and group effectiveness as far as task achievement concerns. To this end, this  parameter can be measured both qualitatively and quantitatively by the type of user task- based actions that represent contributions which express basic and supporting active learning skills as well as perception skills. Table 1 shows the mid- and low-level indicators in the form of the skills and sub-skills that should characterize the students who participate in a learning collaborative situation in order to achieve effective group and individual performance of the task and thus obtain a successful learning outcome. To measure each indicator (or skill), we associate it with the actions that students perform in an asynchronous environment. Table 1: Indicators (skills) that model task performance Skills Sub-skills (Learning outcome contribution) Actions (&objects) involved Basic active learning skills Information generation Create doc/note Information refinement Edit doc Information elaboration Version/Replace doc Information revision Revise/Branch doc Supporting active learning skills Information reinforcement Create_Noteboard doc/URL /Notes (as an attachment) Information  processing (perception) skills Information acknowledgement Read event 2.2 Group functioning This is the second top-level activity parameter which is made up of the type of events that represent and are used to measure and analyze the individual and group effectiveness regarding participation and interaction behaviour that facilitate the group’s well-being function [4]. Table 2 shows the mid- and low-level indicators in the form of skills and sub-skills that students should exhibit in order to enhance participation, accomplish well-balanced contributions, promote better communication and coordination, as well as adequate task management and workspace organization, and thus achieve an effective group interaction and functioning in a collaborative learning situation. To measure each indicator, we associate it with specific student action and contribution types that best describe each skill to be accomplished  Table 2: Indicators (skills) that model group functioning Skills Sub-skills (Group functioning contribution) Actions (&objects) involved Active participation  behaviour and peer involvement skills Participation in managing (generating, expanding and  processing) info Create Event, Change Event, Read Event Social grounding skills Well-balanced contributions, adequate reaction attitudes, and role playing Create Event, Change Event, Read Event, Move Event Task planning Create/Link Appointment Create/ChangeAccess WSCalendar Task processing skills Task (and knowledge) management Create Folder Create Notes (create a debate space) Workspace  processing skills Workspace organisation and maintenance Move event (cut, drop, copy, delete, forget) Clarification Change Description/ Change Event doc Change Description url Evaluation Rate document/url Description (illustration) Edit/Change Description Folder Change Description Notes Communication improvement Edit Note Chvinfo/Chvno/Checkin/Check out doc Rename Folder/Notes/doc/url/ Appointment/WSCalendar Communication  processing skills Meeting accommodation ChangeDesc/ChangeDate /ChangeLocation Appointment 2.3 Scaffolding This last top-level activity parameter is specified by the type of events that refer to social support among members as well as to task- or group functioning-oriented help provided to a  participant who is not quite able or ready to achieve a task on his or her own. As for the former, it is related to the event information that includes actions which support and  promote group cohesion, such as motivational and emotional support, conflict resolution, etc. As for the latter, it must be focused on those specific actions designated to provide effective help to the peers when they need it during the collaborative learning activities. Table 3: Indicators that model scaffolding Social support Members’ commitment toward collaboration, joint learning and accomplishment of the common group goal Level of peer involvement and their influential contribution to the involvement of the others Members’ contribution to the achievement of mutual trust Members’ motivational and emotional support to their peers Participation and contribution to conflict resolution Help Services  Help is timely Help is relevant to the student’s needs Help is qualitative Help is understood by the student Help can readily be applied by the student   The participants' actions aiming at getting or providing help are classified and measured according to whether they refer to the task or group functioning. Table 3 shows the different types of social support and help services [7] that have been identified and accounted for in this model. To sum up, this model shows how the information generated in collaborative learning activities can be captured and classified at several description levels in an efficient manner. This fact can significantly improve the way a collaborative system, used for learning and instruction, can effectively collect all the necessary and useful information produced from user-user and user-system interaction. Next section briefly describes how the conceptual model is translated into a computational model of collaborative learning interaction. 3. A generic platform for the systematic construction of collaborative learning applications We propose a generic, robust, reusable component-based Collaborative Learning Purpose Library (CLPL) that implements the conceptual model of information management described above whereby it enable a complete and effective reutilization of its generic components for the construction of specific CSCL applications. Many specific Web-based platforms for the construction of collaborative applications such as WebCT [8], PhpBB [9], Moodle [10], and MetaWiz [11] have been given in the literature. Although most of these support many aspects of collaborative applications, they do not contemplate the users’ fundamental needs for groupware environments such as dynamic support to group awareness, specific components for awareness and feedback management, and interoperability between different applications to support collaborative work. We try to remediate this lack from a generic view with our CLPL platform. The CLPL is made up of five components related to user management, administration, security, knowledge management, and functionality mapping the essential needs in which any CSCL application is involved. Special attention has been paid to address the complex issues of data analysis and management identified in the previous sections. This is mainly  performed by two components, namely CSCL Knowledge Management and CSCL Functionality components, which form the core of the CLPL in the construction of CSCL applications. Due to their importance, they are briefly described below. The CSCL Knowledge Management component is made up of two subsystems, namely CSCL Activity Management and CSCL Knowledge Processing so as to support the first two stages of the information and knowledge management. The first subsystem manages the system log files made up of all the events occurring in a certain workspace over a given  period of time. This event information is then correctly classified according to a complete and tight hierarchy of events based on the mentioned three types of collaborative activity  proposed in the previous section. The second subsystem performs the statistical analysis event information as well as the management and maintenance of the knowledge extracted  by that analysis. The CSCL Functionality component implements the last stage of the information and knowledge management process, that is the presentation of the knowledge generated to users in terms of immediate awareness and constant feedback of what is going on in the system. In order to provide the essential awareness information to support collaboration, communication and coordination effectively, this subsystem defines three generic entities respectively, namely resource state, user status  and group memory . Each of these abstractions acts as a vehicle so that awareness information can be classified and presented to users in the correct form depending on the type of activity involved. Finally, feedback information is achieved by defining certain generic entities such as history ,  pool  and diagram and functions such as sorting . Based on these abstractions it is possible to dynamically gather and store a great amount of history data and statistical results from
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