Gamification of Collaborative Learning Scenarios: Structuring Persuasive Strategies Using Game Elements and Ontologies

2016 ◽  
pp. 12-28 ◽  
Author(s):  
Geiser Chalco Challco ◽  
Riichiro Mizoguchi ◽  
Ig Ibert Bittencourt ◽  
Seiji Isotani
Keyword(s):  
Collaborative Learning ◽  
Persuasive Strategies ◽  
Learning Scenarios ◽  
Game Elements

Scripts for Facilitating Computer Supported Collaborative Learning

2011 ◽  
pp. 745-750
Author(s):  
Bernhard Ertl ◽  
Heinz Mandl
Keyword(s):  
Distance Learning ◽  
Collaborative Learning ◽  
Learning Strategies ◽  
Text Comprehension ◽  
Comprehension Strategies ◽  
Computer Supported Collaborative Learning ◽  
Collaboration Scripts ◽  
Collaboration Skills ◽  
Learning Scenarios ◽  
Collaboration Process

Many distance learning scenarios, for example, virtual seminars, use collaborative arrangements for learning. By applying them, they offer learners the chance to construct knowledge collaboratively. However, learners often do not possess the skills necessary for a beneficial collaboration. It is therefore important that learners are offered support in these learning scenarios. Scripts for collaborative learning can provide support. They can guide learners through their collaboration process (Ertl, Kopp, & Mandl, 2007b) and help them to acquire collaboration skills (Rummel & Spada, 2005). Scripts for collaboration were originally developed in order to support text comprehension. They facilitate two or more learners—who are similar as far as their existing knowledge and learning strategies are concerned— in their efforts to understand contents provided by theory texts. Collaboration scripts split this process into a sequence of smaller steps, assign each learner to a particular role, and offer a number of comprehension strategies, such as questions, feedback, and elaboration. Each one of these learners has a defined role to play, which in turn is associated with certain strategies and varies within the different phases.

Interface creation and redesign techniques in collaborative learning scenarios

2011 ◽  
Vol 27 (1) ◽  
pp. 127-138 ◽  
Author(s):  
Benjamin Weyers ◽  
Wolfram Luther ◽  
Nelson Baloian
Keyword(s):  
Collaborative Learning ◽  
Learning Scenarios

The Integration of Aspects of Geo-Tagging and Microblogging in m-Learning

2011 ◽  
pp. 95-110
Author(s):  
Christian Safran ◽  
Victor Manuel Garcia-Barrios ◽  
Martin Ebner
Keyword(s):  
Higher Education ◽  
Collaborative Learning ◽  
Web 2.0 ◽  
Collaborative Work ◽  
Solution Approach ◽  
Textual Data ◽  
E Learning ◽  
Learning Scenarios ◽  
Mobile Clients ◽  
Potential Use

The recent years have shown the remarkable potential use of Web 2.0 technologies in education, especially within the context of informal learning. The use of Wikis for collaborative work is one example for the application of this theory. Further, the support of learning in fields of education, which are strongly based on location-dependent information, may also benefit from Web 2.0 techniques, such as Geo-Tagging and m-Learning, allowing in turn learning in-the-field. This chapter presents first developments on the combination of these three concepts into a geospatial Wiki for higher education, TUGeoWiki. Our solution proposal supports mobile scenarios where textual data and images are managed and retrieved in-the-field as well as some desktop scenarios in the context of collaborative e-Learning. Within this scope, technical restrictions might arise while adding and updating textual data via the collaborative interface, and this can be cumbersome in mobile scenarios. To solve this bottleneck, we integrated another popular Web 2.0 technique into our solution approach, Microblogging. Thus, the information pushed via short messages from mobile clients or microblogging tools to our m-Learning environment enables the creation of Wiki-Micropages as basis for subsequent collaborative learning scenarios.

Visual Modelling of Collaborative Learning Processes

2008 ◽  
pp. 280-297 ◽  
Author(s):  
Andreas Harrer ◽  
H. Ulrich Hoppe
Keyword(s):  
Collaborative Learning ◽  
Visual Learning ◽  
Learning Processes ◽  
Learning Activity ◽  
Collaboration Scripts ◽  
Web Based ◽  
Tutoring Systems ◽  
Modelling Languages ◽  
Research Fields ◽  
Learning Scenarios

The modelling of learning processes and its use in computer-supported learning scenarios attracted attention in a wide variety of research fields in the last years, e.g. in web based education, computer supported collaboration scripts, and intelligent tutoring systems (ITS). Most of the discussion is either focused on the conceptual level of instructional design for exchange between designers or on the automated execution of predefined designs and learning scripts. In this chapter we will elaborate on the whole spectrum of different uses that visual learning models provide for teachers, learners, and researchers. Based on our discussions in an international research project on computer-supported collaboration scripts we identify desired properties for such modelling languages especially considering the needs of the practitioners. Finally we propose MoCoLADe (MOdel for COllaborative Learning Activity Design), an exemplary approach of a visual language for collaborative learning processes that was designed according to the presented principles.

Steps Towards the Gamification of Collaborative Learning Scenarios Supported by Ontologies

2015 ◽  
pp. 554-557
Author(s):  
Geiser Chalco Challco ◽  
Riichiro Mizoguchi ◽  
Ig Ibert Bittencourt ◽  
Seiji Isotani
Keyword(s):  
Collaborative Learning ◽  
Learning Scenarios

scholarly journals Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1578
Author(s):  
Oscar Revelo Sánchez ◽  
César A. Collazos ◽  
Miguel A. Redondo
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Learning ◽  
Big Five ◽  
Big Five Personality ◽  
Empirical Validation ◽  
Psychometric Instrument ◽  
Big Five Model ◽  
Specific Objective ◽  
Learning Scenarios ◽  
Two Stages

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.

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