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A Rich Context Model: Design and Implementation
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0001-9062-1609
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The latest developments of mobile devices include a variety of hardware features that allow for more rich data collection and services. Numerous sensors, Internet connectivity, low energy Bluetooth connectivity to other devices (e.g., smart watches, activity tracker, health data monitoring devices) are just some examples of hardware that helps to provide additional information that can be beneficially used for many application domains. Among others, they could be utilized in mobile learning scenarios (for data collection in science education, field trips), in mobile health scenarios (for health data collection and monitoring the health state of patients, changes in health conditions and/or detection of emergency situations), and in personalized recommender systems. This information captures the current context situation of the user that could help to make mobile applications more personalized and deliver a better user experience. Moreover, the context related information collected by the mobile device and the different applications can be enriched by using additional external information sources (e.g., Web Service APIs), which help to describe the user’s context situation in more details.

The main challenge in context modeling is the lack of generalization at the core of the model, as most of the existing context models depend on particular application domains or scenarios. We tackle this challenge by conceptualizing and designing a rich generic context model. In this thesis, we present the state of the art of recent approaches used for context modeling and introduce a rich context model as an approach for modeling context in a domain-independent way. Additionally, we investigate whether context information can enhance existing mobile applications by making them sensible to the user’s current situation. We demonstrate the reusability and flexibility of the rich context model in a several case studies. The main contributions of this thesis are: (1) an overview of recent, existing research in context modeling for different application domains; (2) a theoretical foundation of the proposed approach for modeling context in a domain-independent way; (3) several case studies in different mobile application domains.

Place, publisher, year, edition, pages
Växjö: Faculty of Technology, Linnaeus University , 2017. , p. 103
Series
Reports: Linnaeus University, Faculty of Technology ; 48
Keywords [en]
Context modeling, rich context model, mobile users, current context of the user, mobile sensors, multidimensional vector space model, contextualization
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-60850Libris ID: 20100123ISBN: 978-91-88357-62-5 (print)OAI: oai:DiVA.org:lnu-60850DiVA, id: diva2:1076331
Presentation
2017-02-17, C1202, Växjö, 09:15 (English)
Opponent
Supervisors
Available from: 2017-02-24 Created: 2017-02-22 Last updated: 2017-09-01Bibliographically approved
List of papers
1. Implementing and Validating a Mobile Learning Scenario Using Contextualized Learning Objects
Open this publication in new window or tab >>Implementing and Validating a Mobile Learning Scenario Using Contextualized Learning Objects
2014 (English)In: Proceedings of the 22nd International Conference on Computers in Education ICCE 2014: November 30, 2014 - December 4, 2014, Nara, Japan, Japan: Asia-Pacific Society for Computers in Education, 2014, p. 522-527Conference paper, Published paper (Refereed)
Abstract [en]

Substantial research in the field of mobile learning has explored aspects related to contextualized learning scenarios. Nevertheless, the current context of a mobile learner has been often limited to his/her current position, neglecting the possibilities offered by modern mobile devices of providing a much richer representation of the current learner´s context. In this paper, we show that a detailed contextualization of the learner may provide benefits in mobile learning scenarios. In order to validate this claim, we implemented a mobile learning scenario based on an approach that allows for a very rich and detailed contextualization of the mobile learner. The scenario that we implemented allowed exchange students to be guided at Linnaeus University in Växjö, Sweden in order to get familiar with the campus and prominent institutions on it. We carried out a study including two groups; one that performed learning activities with contextualization support and one other without it. The results of our evaluation showed significantly better results for the contextualized approach, especially with respect to the acceptance of the Perceived Ease of Use.

Place, publisher, year, edition, pages
Japan: Asia-Pacific Society for Computers in Education, 2014
Keywords
mobile learning scenarios; learning objects; contextualization; cross-platform development
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
urn:nbn:se:lnu:diva-38612 (URN)2-s2.0-84923923652 (Scopus ID)978-4-9908014-1-0 (ISBN)
Conference
The 22nd International Conference on Computers in Education (ICCE), November 30, 2014 to December 4, 2014, Nara, Japan
Available from: 2014-12-15 Created: 2014-12-15 Last updated: 2018-01-11Bibliographically approved
2. Flexible and Contextualized Cloud Applications for Mobile Learning Scenarios
Open this publication in new window or tab >>Flexible and Contextualized Cloud Applications for Mobile Learning Scenarios
2016 (English)In: Mobile, Ubiquitous, and Pervasive Learning: Fundaments, Applications, and Trends / [ed] Alejandro Peña-Ayala, Springer Publishing Company, 2016, p. 167-192Chapter in book (Refereed)
Abstract [en]

This chapter describes our research efforts related to the design of mobile learning (m-learning) applications in cloud-computing (CC) environments. Many cloud-based services can be used/integrated in m-learning scenarios, hence, there is a rich source of applications that could easily be applied to design and deploy those within the context of cloud-based services. Here, we present two cloud-based approaches—a flexible framework for an easy generation and deployment of mobile learning applications for teachers, and a flexible contextualization service to support personalized learning environment for mobile learners. The framework provides a flexible approach that supports teachers in designing mobile applications and automatically deploys those in order to allow teachers to create their own m-learning activities supported by mobile devices. The contextualization service is proposed to improve the content delivery of learning objects (LOs). This service allows adapting the learning content and the mobile user interface (UI) to the current context of the user. Together, this leads to a powerful and flexible framework for the provisioning of potentially ad hoc mobile learning scenarios. We provide a description of the design and implementation of two proposed cloud-based approaches together with scenario examples. Furthermore, we discuss the benefits of using flexible and contextualized cloud applications in mobile learning scenarios. Hereby, we contribute to this growing field of research by exploring new ways for designing and using flexible and contextualized cloud-based applications that support m-learning.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 406
Keywords
Mobile learning, Contextualization, Contextualized service, Cloud computing, Cloud-based services, Context modeling
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
urn:nbn:se:lnu:diva-49569 (URN)10.1007/978-3-319-26518-6_7 (DOI)2-s2.0-84966270917 (Scopus ID)978-3-319-26516-2 (ISBN)
External cooperation:
Available from: 2016-02-04 Created: 2016-02-04 Last updated: 2017-02-22Bibliographically approved
3. Using a Rich Context Model for People-to-People Recommendation
Open this publication in new window or tab >>Using a Rich Context Model for People-to-People Recommendation
2015 (English)In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 24-26 Aug. 2015, Rome, IEEE, 2015, p. 703-708Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an approach for People- to-People recommendations based on a Rich Context Model (RCM). We consider personal user information as contextual information used for our recommendations. The evaluation of our recommendation approach was performed on a social network of students. The obtained results do show a significant increase in performance while, at the same time, a slight increase in quality in comparison to a manual matching process. The proposed approach is flexible enough to handle different data types of contextual information and easy adaptable to other recommendation domains. 

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
rich context model, recommendation, matching
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-46067 (URN)10.1109/FiCloud.2015.68 (DOI)000378639200105 ()2-s2.0-84959061042 (Scopus ID)978-1-4673-8103-1 (ISBN)
Conference
3rd International Conference on Future Internet of Things and Cloud, 24-26 Aug. 2015, Rome
Available from: 2015-09-04 Created: 2015-09-04 Last updated: 2017-04-24Bibliographically approved
4. Using a Rich Context Model for a News Recommender System for Mobile Users
Open this publication in new window or tab >>Using a Rich Context Model for a News Recommender System for Mobile Users
2014 (English)In: UMAP 2014 Extended Proceedings: Posters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014) Aalborg, Denmark, July 7-11, 2014. / [ed] Iván Cantador, Min Chi, Rosta Farzan, Robert Jäschke, CEUR , 2014, Vol. 1181, p. 13-16Conference paper, Published paper (Refereed)
Abstract [en]

Recommender systems have become an important application domain related to the development of personalized mobile services. Thus, various recommender mechanisms have been developed for filtering and delivering relevant information to mobile users. This paper presents a rich context model to provide the relevant content of news to the current context of mobile users. The proposed rich context model allows not only providing relevant news with respect to the user’s current context but, at the same time, also determines a convenient representation format of news suitable for mobile devices.

Place, publisher, year, edition, pages
CEUR, 2014
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; Vol 1181
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
urn:nbn:se:lnu:diva-34511 (URN)2-s2.0-84925259973 (Scopus ID)
Conference
2nd International Workshop on News Recommendation and Analytics (NRA) in conjunction with 22nd Conference on User Modelling, Adaptation and Personalization (UMAP 2014), July 11, 2014, Åalberg
Available from: 2014-05-31 Created: 2014-05-31 Last updated: 2018-01-11Bibliographically approved

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