Aplicación De Métodos De Diseño Centrado en El Usuario Y Minería De Datos Para Definir Recomendaciones Que Promuevan El Uso Del Foro en Una Experiencia Virtual De Aprendizaje

Valdiviezo, Priscila; Santos, Olga C.; Boticario, Jesús G.

doi:10.5944/ried.2.13.824

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…In virtual learning environments (VLEs), Valdiviezo, Santos, and Boticario (2010) have expounded the application of unsupervised learning techniques to identify common patterns of interaction with the forums available on an OpenACS/dotLRN course, promoting the definition of recommendations that help to improve the students' learning experience. The cases presented above are just a small sample of the breadth of research work conducted on the application of artificial intelligence to educational resources and processes.…”

Section: Artificial Intelligence: Recommender Systemsmentioning

confidence: 99%

Mobile learning: perspectives

Díaz

Torres-Carrión

Moro

2015

RUSC. Univ. and Know. Soc.

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From a technical perspective, the future of learning is defined by four axes around which technological and methodological efforts revolve. These axes are mobility, interaction, artificial intelligence and technologybased resources such as augmented reality and games applied to learning. Combining them means creating a model of mobile, interactive and intelligent scenarios that take advantage of the spaces and times available to the learner. The various technologies are already available yet used separately in different educational experiences. It is therefore crucial to combine and integrate them into didactic models wherein the learning attained by students is significant. This article discusses these technologies and proposes an integrative model that enables a framework of reference for didactic work to be established. It concludes by highlighting the need to experiment with technologies and to apply the results to teaching-learning models using alternative interaction schema, and the urgency of having intelligent tutoring systems to make tutoring available on a massive scale. part to the increase in mobile access plans and the development of mobile learning within reach of all students. KeywordsThe growth in rates of access to the Internet via mobile devices is setting a trend that actually runs counter to the classic disadvantages experienced by certain social and ethnic groups.Mobile devices offer the advantage of integrating several technologies into a single unit; in the field of education, this represents a set of possibilities that have had a major impact (Low, 2006). They offer the possibility of having tools and resources available anytime, anywhere, thereby creating a combination of possibilities that, properly exploited, can improve learning results. Applying the concept of mobility to education has the power to make processes ubiquitous and to combine formal learning that takes place in a classroom with informal learning that occurs in social networks, thus breaking structures and concepts and paving the way for a series of innovations, the effects of which need to be experienced.In addition to academic applications is the Web 2.0 phenomenon, which has caused the mobile wave to spread across the globe; Internet users upload information to share with others in their social networks, they interact with each other in those social networks and they collaboratively construct knowledge as constituent parts of a collective intelligence in which leisure and entertainment take up a considerable amount of time.According to Low (2006), learning via mobile devices facilitates the students' activities, which the author divides into four categories:1. Students are capable of creating and capturing their own content. Students can access educational resources.3. Students use digital devices to process learning stimuli.4. Students communicate with their peers and tutors, establishing relationships that are useful to learning.The fourth category refers to the interaction that occurs among members of a lear...

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Section: Artificial Intelligence: Recommender Systemsmentioning

confidence: 99%

Mobile learning: perspectives

Díaz

Torres-Carrión

Moro

2015

RUSC. Univ. and Know. Soc.

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“…o Identify troublesome or promising situations, which helps the educator to think of appropriate recommendation needs [38]. For instance, interaction data from previous courses can be analyzed to: (i) identify learners with some shared features that are not performing well in the course; (ii) course activities that have become a hindrance for a subset of learners, or (iii) LMS functionalities that are being misused in certain activities.…”

Section: Designing the Recommendations With Tormes Methodologymentioning

confidence: 99%

Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

Santos

Boticario

2011

Algorithms

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This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations

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“…In this case, data from previous ALPE courses were mined, but no relevant outcomes were identified to refine the values for the conditions because it was difficult for the educators to associate the data mined to the scenarios elicited. However, there was a related experiment where the clustering approach was used to identify user features to be considered as applicability conditions in the recommendations (Valdiviezo et al, ). The experiment carried out is outlined here to illustrate the potential of this approach.…”

Section: Scenario ‘Discovering the Platform’mentioning

confidence: 99%

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Santos

Boticario

2013

Expert Systems

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Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.

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Aplicación De Métodos De Diseño Centrado en El Usuario Y Minería De Datos Para Definir Recomendaciones Que Promuevan El Uso Del Foro en Una Experiencia Virtual De Aprendizaje

Cited by 3 publications

References 6 publications

Mobile learning: perspectives

Mobile learning: perspectives

Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

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