Content-free collaborative learning modeling using data mining

Abstract

Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous humanbased and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features 123 182 A. R. Anaya, J. G. Boticario and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future.

A visual recommender tool in a collaborative learning experience

“…We have also proposed two DM approaches, based on the statistical indicators, to assess student collaboration with a clustering approach and a metric approach (Anaya and Boticario, 2011b). The clustering approach groups student interactions into three clusters using an EM algorithm and compares the clusters with the expert-based analysis; the cluster algorithms have been shown to group students according to their collaboration (Anaya and Boticario, 2011b).…”

“…The clustering approach groups student interactions into three clusters using an EM algorithm and compares the clusters with the expert-based analysis; the cluster algorithms have been shown to group students according to their collaboration (Anaya and Boticario, 2011b). The metric approach is an ML-based method that provides a set of student collaboration indicators.…”

“…It is divided into two phases: a first phase to do work individually for a few days; and a second phase to develop collaborative work in three-member teams for around two months (Anaya and Boticario, 2011b). During the first phase students become familiar with the problem that they have to solve and the environment that they have to face in the second phase.…”

Information Management System based on Principles of Adaptability and Personalization

“…It is a component of the organizational information system, developed in order to enable KPI management. Organization performance management requires comprehensive and timely information about KPIs [12,13].…”

Activity sequence modelling and dynamic clustering for personalized e-learning

“…The statistical indicators were used as a basis for clustering through which information about learners' collaborative behaviour was extracted. The paper by the same authors in this issue (Anaya and Boticario 2011) presents an updated and more comprehensive view of their approach, introducing metrics based on the statistical indicators, which are shown to have superior performance to clustering in characterising the collaboration behaviour of learners.…”