Academic journal article Educational Technology & Society

A Framework for Semantic Group Formation in Education

Academic journal article Educational Technology & Society

A Framework for Semantic Group Formation in Education

Article excerpt

Introduction

Many approaches to learning and teaching rely upon students working in groups. Research in many disciplines has shown that learning within groups improves the students' learning experience by enabling peers to learn from each other. To form groups, students can be either allocated to groups randomly, self-select each other, or be appointed to a group by the teacher based on some criteria related to the collaboration goals. These criteria are usually expressed as a set of conditions, typically referred to as constraints, such as restricting the groups to be mixed in gender or skills (Ounnas, 2007b).

For the teacher, forming groups manually can be both difficult and time consuming. For this, researchers have been investigating several techniques for automating this process through the use of computer-supported group formation (CSGF). Similar to manual group formation, the challenges of CSGF lie in modeling the students' data, the teacher's constraints; and negotiating the allocation of students to groups to satisfy these constraints. However, existing tools often fail in allocating all students to groups, leaving some students unassigned to any group after the formation (Redmond, 2001), (Tobar, 2007). This problem is usually referred to as the orphan students problem.

In previous work (Ounnas, 2007b), we discussed some of the existing CSGF techniques in terms of the constraints and the selection of members in the groups. We also discussed the potential of using Semantic Web technologies (Berners-Lee et al., 2001) in providing meanings to the students' descriptions and constraints. In this paper, we propose a framework that is capable of efficiently automating the formation of students' groups by reasoning over the students' semantic data and the list of constraints specified by the teacher. We use the efficiency of both Semantic Web technologies and logic programming in modeling the problem of group formation as a constraint satisfaction problem (Kumar, 1992). The next section of the paper describes our motivation behind the research based on existing literature and the results obtained from a case study. We then describe the structure of the proposed framework and explain its components. Afterward, we discuss the evaluation of the proposed framework. Finally, we describe our future work for improving the performance of the framework and discuss some of the relevant issues with its evaluation.

Motivation

In order to understand the issues rising with forming efficient groups of learners, we analyzed the existing applications and efforts to automate this process. We identified the limitations of the existing tools and the need to design a framework that enables the delivery of well-formed groups based on constraints from a multidimensional space. To understand the nature of the possible constraints we carried an observational study with a class of undergraduate students at the University of Southampton.

Existing applications

In (Hoppe, 1995) Hoppe introduced an intelligent tutoring system that allows the learners to initiate a group formation when they have a problem (a learner-helper group). Based on the learners' models, the system displays a list of all potential peer learners that can help; the learner then selects a helper from the list, and the latter can accept or reject the invitation to help the learner. Parameters here are based on learning experience and competency criteria in the subject of the collaboration. In Muhlenbrock (2005) and (2006), context information such as the learner's geographical location from PCs, Phones, and PDAs were added to the model. Unfortunately, no evaluation of the application was provided by the authors.

A team from Osaka University in Japan (Ikeda et al., 1997) and (Inaba et al., 2000) introduce Opportunistic Group Formation (OGF) where an intelligent system detects the appropriate situation to start a collaborative learning session and sets up a learning goal for the learner. …

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