Abstract. One of the main difficulties during the design of collaborative learning activities is adequate group formation. In any type of collaboration, group formation plays a critical role in the learners ’ acceptance of group activities, as well as the success of the collaborative learning process. Nevertheless, to propose both an effective and pedagogically sound group formation is a complex issue due to multiple factors that influence group arrangement. The current (and previous) learner’s knowledge and skills, the roles and strategies used by learners to interact among themselves, and the teacher’s preferences are some examples of factors to be considered while forming groups. To identify which factors are essential (or desired) in effective group formation, a well structured and formalized representation of collaborative learning processes, supported by a strong pedagogical basis, is desirable. Thus, the main goal of this paper is to present an ontology that works as a framework based on learning theories that facilitates group formation and collaborative learning design. The ontology provides the necessary formalization to represent collaborative learning and its processes, while learning theories provide support in making pedagogical decisions such as gathering learners in groups and planning the scenario where the collaboration will take place. Although the use of learning theories to support

We report on a recently funded project called Renaissance Computing, an initiative for promoting student participation in computing. We propose a radical re-thinking not only of our core curriculum in CS, but of the role of CS at the university level. In our conception, ―computational thinking ‖ is neither easily separated from other endeavors nor easily balkanized into a single department. We thus imagine a CS curriculum that is inextricably linked to other domains. Our proposed initiative covers introductory, depth, and capstone courses, targeting both CS majors and minors. It is also aimed to develop interdisciplinary CS courses in sciences, engineering, arts, and humanities. Furthermore, the framework embraces collaborative learning to help improve learning.

Abstract — Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, and design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free-riding, student assessment, forming student groups that improve student learning, etc.). We have used a multiagent-based system to develop a CSCL application and multiagent-frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and ClassroomWiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that, our lessons would allow (1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help improve the design of such systems, and (2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms.

Abstract. Two critical issues of the typical computer-supported collaborative learning (CSCL) systems are inappropriate selection of student groups and inaccurate assessment of individual contributions of the group members. Inappropriate selection of student groups often leads to ineffective and inefficient collaboration, while inaccurate assessment of individual contributions of the group members (1) hinders healthy working relationships among members and (2) prevents teachers from providing precise interventions to specific students. To address these issues, our proposed iHUCOFS framework forms student groups by balancing the students ’ competence (what the students know) and compatibility (whom they like as peers) for each group. The competence and compatibility are calculated using the assessment of student contributions derived from a newly implemented asynchronous collaborative writing module’s detailed tracking information. Results suggest that: (1) the use iHUCOFS framework may improve: (a) the effectiveness and efficiency of the groups, (b) the perception of the students of their peers and their groups, and (c) the collaboration among students with low and high competence and (2) the teacher can use the detailed information tracked by the collaborative writing module to: (a) improve the design of the CSCL tools and (b) provide precise intervention to improve collaboration among the students.