A Group Formation Tool in an E-Learning Context

Fonooni, Benjamin

doi:10.1109/ictai.2007.155

Cited by 60 publications

(61 citation statements)

References 6 publications

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“…They proved the scalability of the used strategy by iterative experiments with different group sizes Graf and Bekele (2006). In Christodoulopoulos and Papanikolaou (2007), the authors implemented a web-based group formation tool. This tool has the ability of grouping members homogenously and heterogeneously.…”

Section: Related Literaturementioning

confidence: 97%

“…In almost all the work reviewed, the group formation process is discussed in various circumstances. Some researches depended on either single attribute to form a collaborative group such as Abnar et al (2012); Mujkanovic et al (2012); Srba and Bielikova (2015); Sukstrienwong (2012); Yannibelli and Amandi (2011);Zheng and Pinkwart (2014) or multiple attributes as in Brauer and Schmidt (2012); Christodoulopoulos and Papanikolaou (2007); Graf and Bekele (2006);Ho et al (2009);Ounnas et al (2008a). It is obvious that the more attributes of group members, the more complex is the process of group formation.…”

Section: Taxonomy Of Group Formation Techniquesmentioning

confidence: 99%

“…Many researchers merged more than one goal, for instance, a research may conclude with applying suitable attributes with iterative experiments to obtain the optimal solution Abnar et al (2012);Tien et al (2013);Zheng and Pinkwart (2014). Looking more deeply into previous work on group formation, it becomes apparent that there are two approaches of formation which compare the similarity of the group members characteristics: either clustering the homogeneous characteristics or constrained optimization for heterogeneous and mixed characteristics Christodoulopoulos and Papanikolaou (2007);Hubscher (2010). Figure 6 presents a proposed taxonomy on classification of the techniques, which are reviewed and discussed previously.…”

Section: Taxonomy Of Group Formation Techniquesmentioning

confidence: 99%

“…Approximately, four studies used these algorithms Amara et al (2016); Christodoulopoulos and Papanikolaou (2007); Mujkanovic et al (2012); Srba and Bielikova (2015). While other studies varied in their techniques because of their aims (e.g.…”

Section: Techniques Used In Group Formationmentioning

confidence: 99%

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Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

2017

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Section: Related Literaturementioning

confidence: 97%

Section: Taxonomy Of Group Formation Techniquesmentioning

confidence: 99%

Section: Taxonomy Of Group Formation Techniquesmentioning

confidence: 99%

Section: Techniques Used In Group Formationmentioning

confidence: 99%

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Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

2017

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“…Alternative strategies for creating groups often are limited by how many student attributes can be incorporated (Christodoulopoulos and Papanikolaou 2007) or by how much time is required to incorporate more attributes. Conversely, our technique incorporates many factors just as easily as a few.…”

Section: Designs With More Continuous Measuresmentioning

confidence: 99%

Overcoming Barriers to Heterogeneous-Group Learning in the Political Science Classroom

Moore

2014

APSC

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Despite positive fi ndings, small-group activities continue to lag behind lectures in political science classrooms. This article argues that one barrier to wider adoption of more innovative activities is uncertainty about how to effi ciently and fairly create teams that each are heterogeneous and as a set are balanced across relevant characteristics. We fi rst describe recent fi ndings and strategies for creating teams; we then detail our concrete, general approach for incorporating several student characteristics into team creation. We then describe implementations of this approach using freely available software in two undergraduate political science courses-one in American politics and one in political methodology. In these applications and in a variety of simulated data, we demonstrate that teams created using our method are better balanced than those created by randomly allocating students to teams. R ecent educational research argues that students learn more during class time that is spent working actively rather than listening more passively to an instructor-delivered lecture. However, despite these positive fi ndings, political science classrooms tend to rely on teacher-centered rather than student-centered activities that promote active learning (Archer and Miller 2011 ). In our experience, this is partly due to instructors' uncertainty about how to form active-learning teams in ways that are fair, fast, and generate balance on student characteristics across the groups. Heterogeneity within groups has been found to improve student outcomes, and it enables each team to draw on a more diverse set of skills, experiences, and knowledge from its members.Here, we seek to lower instructors' costs of implementing group activities, enabling them to focus on structuring the content of activities with confi dence that their procedure scales to large classes, large groups, or classes that change rapidly as students join or drop out during the term. We accomplish this by deploying research tools that already may be familiar to many instructors and that allow us to incorporate substantial information about students. For those unfamiliar with our approach, this article describes the necessary steps and off ers supplementary support. Our method for quickly creating heterogeneous groups easily can incorporate almost as many student attributes as there are students.Below, we describe recent pedagogical fi ndings about smallgroup learning and detail common strategies for creating such groups. We then introduce our approach in the context of an actual project that we assigned in class, an exercise applying a policymaking model (Kingdon 2003 ) to the Patient Protection and Aff ordable Care Act (ACA) legislation in a medium-sized undergraduate course in American politics. Here, we explicate the three lines of R code that create heterogeneous groups. We then demonstrate that our approach generates teams that are considerably more heterogeneous than if we allocate students purely at random-but with virtually no additio...

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Peer assessment and groups formation in massive open online courses

Haddadi

Bouarab-Dahmani

Guin

et al. 2018

Comp Applic In Engineering

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One of the major challenges facing Massive Open Online Courses (MOOCs) is assessing the learner performances beyond traditional automated assessment methods. This leads to a bottleneck problem due to the massiveness of course participants, especially in the context of problem solving. To tackle this issue, peer assessment has been proposed as an effective method. However, the validity of this process is still under discussion, suffers from a lack of credibility and has many weaknesses, particularly with regards to group formation. This paper develops a new method of peer assessment for MOOCs to improve the accuracy and exactitude of the learner grade. Our proposition is based on three main steps: the formation of learner groups, the assessment and synthesis of the results. First, the group definition process can use different elements of the learner model and enables to build heterogeneous groups. After, each learner is required to grade a small number of peer productions. Finally, a synthesis of the various grades is proposed using both data about the ability to assess of each learner and complexity of problems. To evaluate the proposed peer assessment process, we conducted an experimentation devoted to teaching Software Quality Assurance to beginners with computer science during the first university cycle.

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A Group Formation Tool in an E-Learning Context

Abstract: Abstract

Cited by 60 publications

References 6 publications

Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

Overcoming Barriers to Heterogeneous-Group Learning in the Political Science Classroom

Peer assessment and groups formation in massive open online courses

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