How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Abstract

Abstract-Computer Supported Collaborative Learning (CSCL) is a new mode of teaching and one of the popular approaches for learning process. It allows virtual interactions between groups by providing tools such as: chat, internal email and discussion forums. One of the major problems caused by this learning process is the neglect and isolation of learners in groups, and usually is the cause of a heterogeneous group through social, cognitive or emotional ways. The method used is based on the exploitation of traces left on the online learning platform by learners and groups. The data collected from the environment can be observed and exploited in order to build social and cognitive indicators. Our approach is to design a model which assists the tutor to rebuild groups who are not homogeneous in order to prevent their isolation and abandonment. Our model offers the tutor the opportunity to rebuild the groups in an automatic way and based on the characteristics of quantitative indicators of all learners. Our work allowed us to test our algorithm from a functional and technical point of view and also identifies real variables from a collaborative online learning. It also allowed us to evaluate six different indicators proposed for this experiment, showing that they may assist the tutor to rebuild many groups again. The results show us that after the rebuilding groups, there has been a lot of participation in the forum and a considerable number of shares and documents deposited to the forum for each group. This high frequency of interaction between learners, lead them to a fruitful collaboration, and a good quality work at the end.

Revisão Sistemática da Utilização de Fóruns Inteligentes para a Promoção da Colaboração na Aprendizagem Virtual

“…• Formação de grupos -Neste tipo de estratégia, os usuários são alocados em grupos, designados para que seus integrantes sejam mais compatíveis e/ou complementares, facilitando as interações. Um mecanismo de formação automática de grupos em um sistema tutor inteligente, baseado no algoritmo de busca local 2-opt, foi abordado por Salihoun, Guerouate, Berbiche & Sbihi (2017), obtendo sucesso no incremento da participação no fórum. Maina et al (2017) desenvolveu uma solução de agrupamento com base no grau de colaboração, utilizando aprendizado de máquina e algoritmos kmédias e Expectation-maximization.…”

Implementation of Group Formation Algorithms in the ELARS Recommender System

“…In [4], authors gave an overview of proposed and implemented solutions showing that, in most cases, automatic grouping relies on probabilistic algorithms which employ a degree of randomness. In such approaches, the goal is not to find the best possible solution, but to find the approximation of the best solution [6], [7]. Examples are genetic algorithms [8] and those based on swarm intelligence [9].…”

“…Grouping algorithms are usually employed at the beginning of the e-tivity, but there are also approaches where they are used to rebuild groups during the e-tivity. For example, authors in [6] use the descent algorithm with the neighborhood of 2-opts to reconstruct the groups that are not homogeneous, according to the activity traces collected during the first part of the forum discussion.…”

“…Homogeneous groups consist of the students with the same or similar characteristics. It can be useful to form homogeneous groups when the design of e tivity is tailored to certain knowledge level, learning style or similar [6].…”

A three-dimensional model that helps tutors to monitor groups during a collaborative online work

“…The constitution was done in an automatic manner according to our algorithm [33]. Each group will have an average following quantitative indicators that we have exploited.…”