Comulang: towards a collaborative e-learning system that supports student group modeling

Abstract

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called “Comulang” while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system’s basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.

RETRACTED: Factors affecting the e-learning outcomes: An integration of TAM and IS success model

“…As Xu et al (2014) note, e-learning systems provide learners with self-evaluation which allows them to assess their learning performance and distinct their learning weaknesses; therefore, learners using online learning platforms typically show higher perceived learning performance than those who do not. Troussas et al (2013) who put collaboration among learners into consideration in a computer assisted learning environment, found that effective collaborative learning results when students appropriately perceive the significance of working actively with others in order to learn and act in ways which improve the educational procedure and emphasize the value of cooperation. Furthermore, As Islam (2013) noted, some studies concluded that individuals interact more effectively when a social structure enables them to access a large number of contacts and interactions and makes the information sharing faster (Cho et al, 2007;Ortiz et al, 2004).…”

Combining Learner’s Preference and Similar Peers’ Experience in Adaptive Learning

“…Personalised recommendation mechanisms which take into account peers' experience have been proposed in a number of previous study (Troussas et al, 2013;Wang et al, 2007). The method proposed by Tzone I Wang et al (Wang et al, 2007) implements two algorithms, the preference-based algorithm and the correlation-based algorithm, to rank the recommended results to advise a learner concerning the most suitable learning objects.…”

Information Theoretic Clustering for an Intelligent Multilingual Tutoring System

“…Further insight is gained by contrasting the conditional entropy to the criterion minimized in the K-means [15]: (8) where µ k is the centroid of the cluster k. Both H(Y |X) and J(X, Y) measure how tight the clusters are. However, the conditional entropy uses the logarithm of the distances, which grows slower than the linear function used by the K-means and showed in past research of the authors [1], [5] and [6]. Thus, arguably the conditional entropy tends to be less sensitive and more robust to outliers.…”

“…The authors of this paper have used the k-means algorithm in order to cluster the students in a multiple language learning environment and ameliorate the learning process in [1], [5], and [6]. Selecting an appropriate distance measure (or metric) is fundamental to many learning algorithms such as k-means.…”