Supporting teachers in collaborative student modeling: A framework and an implementation

Gaudioso, Elena; Montero, Manuel; Talavera, Luis; Hernández-del-Olmo, Félix

doi:10.1016/j.eswa.2007.12.035

Cited by 34 publications

(23 citation statements)

References 10 publications

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“…Other researchers have considered the collaboration context or circumstances as significant features (Durán 2006;Muehlenbrock 2005), because the collaboration context informs other students about the aptitude and capacity of their fellow students to collaborate. Student interactions play an important role in monitoring and analyzing SC (Steffens 2001;Talavera and Gaudioso 2004;Perera et al 2007;Bratitsis et al 2008;Martínez et al 2006;Daradoumis et al 2006;Gaudioso et al 2009;Gómez-Sánchez et al 2009). …”

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 98%

“…These tools were offered to identify and assess the most useful type of information and displaying strategy to achieve the objectives. Other researchers have provided tools that used the modeling approach (Gaudioso et al 2009), and the tool even had scrutability features (Bull et al 2009). These researchers, nevertheless, did not compare the scrutable strategy with other strategies in the same context.…”

Section: Introductionmentioning

confidence: 98%

“…Other researchers analyzed SC using DM techniques and considered student interactions as a sufficient data source (Talavera and Gaudioso 2004;Perera et al 2007;Gaudioso et al 2009). As comparative studies are scant, we investigated different alternatives and finally proposed two different DM approaches to analyze SC, and a simple method to compare both approaches (Anaya and Boticario 2011).…”

Section: Introductionmentioning

confidence: 98%

“…Data, derived from active student interaction during their collaboration, were used to model and analyze SC (Gaudioso et al 2009). We used student interactions in the forums of a collaborative learning environment.…”

Section: Introductionmentioning

confidence: 99%

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Content-free collaborative learning modeling using data mining

Anaya

Boticario

2011

User Model User-Adap Inter

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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.

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Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Content-free collaborative learning modeling using data mining

Anaya

Boticario

2011

User Model User-Adap Inter

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“…Monitoring tools are used to show or monitor the behaviour of the student to the student and the teacher based on their approach [20]. Metacognitive tools analyse the student behaviour based on desired outcome [21]. The above figure ( Fig.…”

Section: Related Workmentioning

confidence: 99%

A Web Based Recommendation System for Personal Learning Environments Using Hybrid Collaborative Filtering Approach

Anandakumar¹,

K.Rathipriya²,

Bharathi³

2014

IJIRSET

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ABSTRACT:The large growth of Web has influenced the generation of huge e-learning resources. This work is focused to devise a personal recommendation system that will address the sparsity and cold-start problems and that will provide a have a more diverse recommendation list for each learner. Here Improved Neighborhood-based Collaborative filtering and Hybrid Genetic algorithm with Particle Swarm Optimization (PSO) method is implemented. These techniques are employed for improving the diversity, and the convergence towards the preferred solution taking into account the preferences of users. The results obtained from the experiments show that the proposed method outperforms current algorithms in terms of accuracy measures and can alleviate cold-start and sparsity problems and generate a more diverse recommendation list as well.

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Data mining in education

Romero

Ventura

2012

WIREs Data Min & Knowl

641

377

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Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Business and Industry

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Supporting teachers in collaborative student modeling: A framework and an implementation

Cited by 34 publications

References 10 publications

Content-free collaborative learning modeling using data mining

Content-free collaborative learning modeling using data mining

A Web Based Recommendation System for Personal Learning Environments Using Hybrid Collaborative Filtering Approach

Data mining in education

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