Assessing Collaboration Quality in Synchronous CSCL Problem-Solving Activities: Adaptation and Empirical Evaluation of a Rating Scheme

Kahrimanis, Georgios; Meier, Anne; Chounta, Irene‐Angelica; Voyiatzaki, Eleni; Spada, Hans; Rummel, Nikol; Avouris, Nikolaos

doi:10.1007/978-3-642-04636-0_25

Cited by 23 publications

(14 citation statements)

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“…An expert-based oriented analysis takes into account specific features from the given context, which minimizes the transferability of the model. For instance, Meier et al (2007) and Kahrimanis et al (2009) used a similar approach to analyze collaboration, but not the same indicators. Kahrimanis et al (2009) used the indicators: Collaboration Flow, Knowledge Exchange, Argumentation, Structuring the Problem Solving Process, Cooperative Orientation, Sustaining Mutual Understanding and Individual Task Orientation.…”

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 98%

“…For instance, Meier et al (2007) and Kahrimanis et al (2009) used a similar approach to analyze collaboration, but not the same indicators. Kahrimanis et al (2009) used the indicators: Collaboration Flow, Knowledge Exchange, Argumentation, Structuring the Problem Solving Process, Cooperative Orientation, Sustaining Mutual Understanding and Individual Task Orientation. Only the last two indicators were also used by Meier et al (2007).…”

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 98%

“…This "collaborativity" represented the group's activities to achieve the team's learning objectives. Meier et al (2007) and Kahrimanis et al (2009) attempted to find general indicators to identify collaboration. They conducted a literature review and proposed five aspects of the collaboration process to model collaboration (Communication, Joint information processing, Coordination, Interpersonal relationship and Motivation).…”

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 99%

“…On the one hand, students and teachers were asked for information on student work and features in questionnaires or reports (Collazos et al 2007;Soller 2001;Park and Hyun 2006;Meier et al 2007;Kahrimanis et al 2009). On the other hand, student interactions were stored to model their collaboration.…”

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

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: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 98%

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

confidence: 99%

Section: Key Modeling Issues In Collaborative Learning Systemsmentioning

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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“…The three steps are monitoring tools, metacognitive tools and guide systems. 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].…”

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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Collaborative Learning and Patterns of Practice

Chounta

2020

Encyclopedia of Education and Information Technologies

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Assessing Collaboration Quality in Synchronous CSCL Problem-Solving Activities: Adaptation and Empirical Evaluation of a Rating Scheme

Cited by 23 publications

References 5 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

Collaborative Learning and Patterns of Practice

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