Personalization for Location-Based E-Learning

Zhou, Rui; Rechert, Klaus

doi:10.1109/ngmast.2008.20

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…Seven systems sense the location of the user and nearby objects to support situated or collaborative learning activities. For instance, Rogers et al [89] and Zhou and Rechert [128] use location to suggest learning resources during outdoor learning activities. In MOBIlearn [65], location is detected for generating recommendations of both relevant learning resources and peers who are nearby in a museum.…”

Section: Survey Of Context-aware Tel Recom-menders 41 Context Dimensionsmentioning

confidence: 99%

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Verbert

Manouselis

Ochôa

et al. 2012

IEEE Trans. Learning Technol.

475

262

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Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

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Section: Survey Of Context-aware Tel Recom-menders 41 Context Dimensionsmentioning

confidence: 99%

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Verbert

Manouselis

Ochôa

et al. 2012

IEEE Trans. Learning Technol.

475

262

View full text Add to dashboard Cite

show abstract

“…The learner's mobile context characterizes the situation of the learner. In e-learning, it is common to use a user profile containing information about the learner (such as personal information, goals, knowledge, interests, preferences, learning history, and possibly also the information about the user's context (such as location, time and device)) (Zhou & Rechert, 2008). We prefer to use the term Mobile User Context because in a MPLE, the mobile aspect is very important.…”

Section: The Main Features a Mplementioning

confidence: 99%

A Reference Model for Mobile Playful Learning Environments

Maushagen

Troyer²

2021

Proceedings of the International Conferences Mobile Learning 2021 (ML 2021) and Educational Technologies 2021 (ICEduTech 2021)

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We propose a novel model for learning environments to target the characteristics of digital natives and to improve informal learning practices, i.e. the exploration, discovery and self-monitoring of learning content and the reflection on learning activities. The Mobile Playful Learning Environment (MPLE) model is defined as consisting of seven main features (mobile user context, data collection and analysis, learner visualization, self-monitoring, persuasion, playfulness, and micro-learning) intended to encourage continuous usage and self-reflection. As a case study we developed TICKLE, an application that implements the MPLE model to re-activate and re-motivate youngsters at risk for school dropout, for learning by stimulating curiosity and engagement through the recognition of non-formal learning opportunities.

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“…Similarly, the PALLAS system, designed for language learners' interest-based informal learning around a city uses GPS to identify position and recommend relevant places of potential interest [8]. Zhou and Rechert's prototype personalized e-learning system for use in a botanical garden draws on both WiFi and GPS positioning to establish the learner's location, and position of nearby plants of probable interest [9]. In the MASELTOV app, we identify location through the "Android Location API" which uses a combination of GPS, WiFi fingerprinting and cell tower positioning.…”

Section: Location Identified Through Automated Inputmentioning

confidence: 99%

Challenges in Context-Aware Mobile Language Learning: The MASELTOV Approach

Gaved

Luley

Efremidis

et al. 2014

Mobile as a Mainstream – Towards Future Challenges in Mobile Learning

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Abstract. Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is engaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project's use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment.

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Personalization for Location-Based E-Learning

Cited by 16 publications

References 17 publications

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

A Reference Model for Mobile Playful Learning Environments

Challenges in Context-Aware Mobile Language Learning: The MASELTOV Approach

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