Embedded the mobile learning agent into intelligent system

Lo, Steven K.C.; Keh, Huan‐Chao; Lin, Yi-Hung

doi:10.1109/icmlc.2009.5212596

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…19 [9], [10], [13], [15], [16], [17], [18] [19], [20], [14], [16], [28], [29] Others 4 [11], [12], [13], [14] Also, from our review six personalization trends emerged as key aspects when deploying agents technology in mobile learning for personalization, Table 2 illustrate those aspects and there related studies. Moreover, we can see from earlier sections that most of the existing literatures concentrate on one Aspect in personalization; those issues can be categorized as: 1.…”

Section: Resultsmentioning

confidence: 98%

“…No. Articles [1], [2], [3], [4], [5], [6] [7], [8], [9], [10], [15], [16] Mobile learning 25 [17], [18], [19], [20], [21], [22] [23], [24], [25], [26], [27], [28], [29] [1], [2], [3], [4], [5], [6] Personalization 19 [7], [8], [9], [10], [15], [16], [17] [18], [19], [21], [24], [27], [29] [2], [3], [5], [6], [7], [8] Agents Tech.…”

Section: Resultsmentioning

confidence: 99%

“…At the same time the system should keep track with the users operations and advancement. Since the mobile portal constructs the ubiquitous mobile intelligent learning environment, which should provide the user(s) with more flexible learning method at anytime and anywhere [20].…”

Section: Agent Technologymentioning

confidence: 99%

See 2 more Smart Citations

A Review on Personalization and Agents Technology in Mobile Learning

Qoussini

Jusoh

2014

2014 International Conference on Intelligent Environments

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Mobile Learning (ML), nowadays gains more attention technically and pedagogically. This review of literature deals with the personalization issue in mobile learning, and how agents can be used to support solving this issue, the main objective of this study is to review recent and up to date studies on personalization in mobile learning and the use of agent technology to help in solving this issue. Also to find if there are any gaps in the existing literature. The review process started with a primary search which, then preparing a checklist(Aims, Research Design,framework, and Justification of the findings), after that selecting the most relevant articles (27) according to some general questions, then the analysis process started and resulted some gaps in the existing literature. Results shows that most of the studies concentrate on one aspect of the personalization such as (Device Capabilities, Student Level, Student's Preferences, Network Issues, Course "Subject", Device Operating System, and Location), student level and students preferences gained more attention of investigating than other aspects. On the other hand, there are some issues not investigated at all (emotional state and disabilities). also most of them assure that agents are a solution for personalization in mobile learning. So there is a need for more investigating on how to deploy agents more effectively to support more personalization in mobile learning or deploying multi-agent architecture in mobile learning.

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