COLEG: Collaborative Learning Environment within Grid

The principal function of the CSCL environments is to provide to the various users (students, teachers, tutors…), the best activities with the best tools at the best time according to their needs. If a CSCL system is a collection of activities or learning process, we can cut out its functionalities in a certain number of autonomous functions which can then be carried out separately in the form of autonomous applications by using the technology of the Web/Grid services. The emerging technologies based on the Grid are increasingly being adopted to improve education and provide better services for learning. These services are offered to students who, regardless of their computer systems, can collaborate to improve their cognitive and social skills. This article presents COLEG (COllaborative Learning Environment within Grid), which aims to employ the capacities offered by the Grid to give the various actors, all the power of learning, collaboration and communication in an adaptable, heterogeneous and dynamic sight

A K-complementarity Technique for Forming Groups of Tutors in Intelligent Learning Environments

In e-learning environments, tutors perform the main function of tutoring. They follow up learners and answer their assistance requests which require different skills. These requests may not belong to the tutors’ skills and competencies, so collaboration among other tutors is expected. In fact, this collaboration can improve tutors’ skills and provide learners with effective monitoring. It can be permanent and adaptive, according to learners’ needs and requests. Grouping tutors with different skills is the aim of this research. In this paper, a new technique for grouping tutors is presented. The proposed technique is called K-complementarity. It is based on the complementarity of roles that are assigned to tutors. K-complementarity is based on tutor model and aims at obtaining K groups of tutors who have the most of the roles and skills. The proposed technique has been used by a Computer-Supported Collaborative Tutoring (CSCTT) system. This system had been tested in an Algerian University. The obtained results showed that the groups’ members are heterogeneous and the groups are homogeneous. Furthermore, each group had more than 80% of the roles by combining those of its members. So, these results can be considered as acceptable and very encouraging

Trace-based Collaborative Learning System

The users of any educational software may leave traces which concern all their activities. In collaborative learning context, these traces are very voluminous and very heterogeneous. They are the results of various interactions between the actors themselves, and between the actors and the system. Hence, first, they must be collected and filtered. Then, these traces must be analyzed to help or support these actors (the tutor in his task of monitoring learners and the teacher-author in his task of creating educational courses). It is this context that defines our research work, which is focused on implementing a collaborative learning system based on traces called SYCATA (SYstème pour la Collecte et l’Analyse des Traces d’Apprentissage collaboratif). SYCATA collects all traces of actors’ activities (especially learners) and groups them into five categories. It offers a multitude of forms (graphical, numerical or mixed) to show these traces to the tutors and the authors. Some traces may be viewed by learners to promote their pedagogical activities and raise their awareness. SYCATA was implemented and experimented with a sample of university students where good results have been obtained

A new approach to identify dropout learners based on their performance-based behavior

Distance learning environments are increasingly offering more comfort to both learners and teachers, allowing them to carry out their academic tasks remotely, especially in critical times where it is difficult, or even dangerous, to bring these actors together in one physical place. Nevertheless, These same environments are complaining about the massive dropout numbers among their learners. Therefore, designing new intelligent systems capable of reducing these numbers becomes imperative. This paper proposes a new approach capable of identifying and assisting endangered learners experiencing difficulties by monitoring and analyzing their behavior inside the e-learning environment. By building dynamic models to follow the learners’ current situation, the proposed approach could intervene autonomously to save learners identified as struggling. Relying on distributed artificial intelligence instead of humans to closely monitor learners within distance learning environments can be very effective when identifying struggling learners. Furthermore, targeting these learners with early enough and carefully designed interventions can reduce the number of dropouts

CLAS: a Collaborative learning awareness system

AbstractBy interacting with the environment, a person generates a multitude of signals. In the case of collaborative learning, these signals allowed a learner to have some knowledge of the actions and the intentions of his colleagues. The knowledge of others, which is resulting from his interaction with the environment, is often mentioned in literature by the term awareness’. The latter can be translated in this context by be aware’ or be aware of what’ it happens. The awareness allows both learners to adjust and plan their behavior based on what they know each other. In this paper, we present CLAS, a collaborative learning system that takes into account the “awareness process”. For doing this, it provides its learners with a set of tools that facilitate the visualization of the traces of their teammates. These learners are grouped into small groups. These social groups can contain four learners at most. This work is a part of research project supported by Guelma University. The task dedicated to our team is to study the impacts of “group awareness” on the instructional level of learners. In other words, the aim is to study the effects of showing all the traces (i.e. actions) of learners by theirs peers. This visualization can be synchronous or asynchronous. In order to facilitate this task, several tools are provided to each learner. These tools are used for supporting the visualization of information about the actions of the other learners belonging to the same group. Some indicators can be calculated from these actions. Most of these indicators are taken from the Social Network Analysis (SNA). Among these indicators, we can cite task realization percentage, percentage of contribution of each group member in global task, cohesion degree, etc. At the end of the paper, we present some screen-shots of CLAS (Collaborative Learning Awareness System), which was implemented at computer science department of Guelma University

Learning Behavior Analysis to Identify Learner’s Learning Style based on Machine Learning Techniques

Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%

Improving soft skills based on students' traces in problem-based learning environments

To assume the productivity of students in the workplace, the higher educational institutions would be faced by a challenging reality that of how to keep focus on technicalities while improving the set of soft skills. Therefore, the main aim of this research is improving students' soft skills and thus their cognitive skills in parallel, to prepare them professionally, where they are put in a problem-based learning environment that is based on developing a software project. In this humble study, students undergo an experimental process where they are asked to develop a software project. The latter is defined by a teacher-set deadline period in which students' performed actions would be recorded to be used in the assessment process. To demonstrate the effectiveness of the developed system and the proposed approach for improving the soft skills, the prospective experiment was conducted at the level of a higher education institution. Over and above, the obtained results were highly satisfying and very encouraging. They also showed that the cognitive profiles and soft skills of most students were improved