ICWL 2009

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Ahmed Al-Hmouz

International Conference on Web-based Learning 2009. A Machine Learning based Framework for Adaptive Mobile Learning

Business Technology

[object Object],[object Object],[object Object],[object Object],A Machine Learning based Framework for Adaptive Mobile Learning International Conference on Web-based Learning (ICWL): August 19th, 2009 RWTH Aachen University, Aachen, Germany

Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Introduction (1) ,[object Object],[object Object],[object Object]

Introduction (2) ,[object Object],[object Object],[object Object]

Introduction (3) ,[object Object],[object Object]

Related Work (1) ,[object Object],[object Object],[object Object],[object Object]

Related Work (2) ,[object Object],[object Object],[object Object],[object Object]

Framework Foundation (1) ,[object Object],[object Object],[object Object]

Framework Foundation (2) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Basic Elements of a User Adaptive System

Layer Model Components ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

1- Context Acquisition Layer ,[object Object],[object Object],[object Object],[object Object]

2- Information Classification Layer ,[object Object],[object Object],[object Object],[object Object]

3- Learner Model Layer ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

4- Information Extraction Layer ,[object Object],[object Object],[object Object]

6- Reasoning Layer ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

7- Interface Layer (1) ,[object Object],[object Object],[object Object],[object Object]

7- Interface Layer (2) ,[object Object],[object Object],[object Object]

Learner Control over the M-learning Application

Learner Control over the M-learning Application ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusion & Future Work (1) ,[object Object],[object Object],[object Object]

Conclusion & Future Work (2) ,[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What's hot

The adequacy of user models depends mainly on the accuracy and precision of information that is retrieved to the user. The real challenge in user modelling studies is due to the inadequacy of data, improper use of techniques, noise within the data and imprecise nature of human behavior. For the best results of user modelling, one should choose an appropriate way to do it i.e. by selecting the best suitable approach for the desired domain. Machine learning and Soft computing Techniques have the ability to handle the uncertainty and are extensively being used for user modeling purpose. This paper reviews various approaches of user modeling and critically analyzes the machine learning and soft computing techniques that have successfully captured and formally modelled the human behavior.

Review and analysis of machine learning and soft computing approaches for use...

IJwest

IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.

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Learner Model's Utilization in the e-Learning Environments Vija VAGALE and Laila NIEDRITE Faculty of Computing, University of Latvia, Raina boulv. 19, Riga, Latvia Abstract. In the field of personalized systems big role is granted to the adaptive elearning environments. The task of these systems is very important and complicated. With their participation the learner gains exactly the knowledge he needs most, and the system adapts to user needs, expectations and his individual features. In this kind of systems information about learner is saved in the learner model also known as the user model and student model. For the system to be able to perceive and analyze user activities correctly, is necessary to define what kind of information about the learner has to be saved. The article gives an overview about already existing learner models, their utilization methods and also it offers their comparison according to various criteria.

Learner Model's Utilization in the e-Learning Environments

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One of the most effective applications of Information and Communication Technology (ICT) is the emergence of E-Learning. Considering the importance and need of E-Learning, recent years have seen a drastic change of learning methodologies in Higher Education. Undoubtedly ,the three main entities of E-Learning system can be considered as Student, Teacher & Controlling Authority and there will be different level, but a good E-Learning system needs total integrity among all entities in every level. Apart from integrity enforcement, security enforcement in the whole system is the other crucial way to organize the it. As internet is the backbone of the entire system which is inherently insecure , during transaction of message in E-Learning system, hackers attack by utilising different loopholes of technology. So different security measures are required to be imposed on the system. In this paper, emphasis is given on different risks called e-risks and their remedies called e-remedies to build trust in the minds of all participants of E-Learning system.

RISKS AND REMEDIES IN E-LEARNING SYSTEM

IJNSA Journal

An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user

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E-learning is currently taking the shape of a Web Service in various applications i.e. learners can search for suitable content, book it, pay for it and consume it. This paper shows how the search aspects for e-learning content can technically be combined with the recent standardization efforts that aim at content exchangeability and efficient reuse. A repository for learning object publication and search is proposed that essentially adapts the UDDI framework used in commercial Web Services to the e-learning context. To adopt Web Services technology towards the reusability and aggregation of e-learning services, the conceptual Web Services architecture and its building blocks need to be augmented. The objective of this research is to design broker based registry architecture for e- Web services which facilitates effective elearning content/service discovery for the consumption or composition. The implementation followed by experimentation showed that, the proposed e-learning discovery architecture facilitates effective discovery with moderate performance in terms of overall response.

A REGISTRY BASED DISCOVERY MECHANISM FOR E-LEARNING WEB SERVICES

cscpconf

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.

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E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems.

Investigating a theoretical framework for e-learning technology acceptance

IJECEIAES

Transcript is an inventory system holding student academic record. This system has been implemented conventional, electronically or implemented using web services approaches which have been devoid of mobile computing approach and system classification rules. The proposed architecture provides an assiduous pathway for the implementation of the aforementioned issues. This architecture has the propensity for cutting down operational cost and hardcopy documentation while handling organizational procedures and processes

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Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended.

ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES

ijsc

Nabila__proposal4.doc

butest

Although of the semantic web technologies utilization in the learning development field is a new research area, some authors have already proposed their idea of how an effective that operate. Specifically, from analysis of the literature in the field, we have identified three different types of existing applications that actually employ these technologies to support learning. These applications aim at: Enhancing the learning objects reusability by linking them to an ontological description of the domain, or, more generally, describe relevant dimension of the learning process in an ontology, then; providing a comprehensive authoring system to retrieve and organize web material into a learning course, and constructing advanced strategies to present annotated resources to the user, in the form of browsing facilities, narrative generation and final rendering of a course. On difference with the approaches cited above, here we propose an approach that is modeled on narrative studies and on their transposition in the digital world. In the rest of the paper, we present the theoretical basis that inspires this approach, and show some examples that are guiding our implementation and testing of these ideas within e-learning. By emerging the idea of the ontologies are recognized as the most important component in achieving semantic interoperability of e-learning resources. The benefits of their use have already been recognized in the learning technology community. In order to better define different aspects of ontology applications in e-learning, researchers have given several classifications of ontologies. We refer to a general one given in that differentiates between three dimensions ontologies can describe: content, context, and structure. Most of the present research has been dedicated to the first group of ontologies. A well-known example of such an ontology is based on the ACM Computer Classification System (ACM CCS) and defined by Resource Description Framework Schema (RDFS). It’s used in the MOODLE to classify learning objects with a goal to improve searching. The chapter will cover the terms of the semantic web and e-learning systems design and management in e-learning (MOODLE) and some of studies depend on e-learning and semantic web, thus the tools will be used in this paper, and lastly we shall discuss the expected contribution. The special attention will be putted on the above topics.

Applying Semantic Web Technologies to Services of e-learning System

International Journal of Engineering Inventions www.ijeijournal.com

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Bluetooth is one of the most prevalent technologies available on mobile phones. One of the key questions how to harness this technology in an educational manner in universities and schools. This paper is about a Bluetooth quizzing system which will be used to administer quizzes to students of a university. The Bluetooth quizzing application consists of a server and client mobile Android application. It will utilize a queuing system to allow many clients to connect simultaneously to the server. When clients connect, they can register or choose the option to complete a quiz that the lecturer selected. Results are automatically sent when quiz is done from the client application. Data analysis can then be done to review the progress of students.

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ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIES

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Mobile Learning (M-Learning) is an emerging discipline in the area of education and educational technology. So researchers are trying to optimize and expanding its application in the field of education. The aim of this paper is to investigate the role of mobile devices and expert systems in disseminating and supporting the knowledge gained by intelligent tutors and to propose a system based on integration of intelligent M-Learning with expert systems. It acts as an intelligent tutor which can perform three processes - pre-test, learning concept and post-test - according to characteristic of the learner. The proposed system can improves the education efficiency highly as well as decreases costs. As a result, every time and everywhere (ETEW) simple and cheap learning would be provided via SMS, MMS and so on in this system. The global intention of M-Learning is to make learning “a way of being”.

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This paper presents recent research into methods used in Australian Indigenous Knowledge sharing and looks at how these can support the creation of suitable collaborative environments for timely organisational learning. The protocols and practices as used today and in the past by Indigenous communities are presented and discussed in relation to their relevance to a personalised system of knowledge sharing in modern organisational cultures. This research focuses on user models, knowledge acquisition and integration of data for constructivist learning in a networked repository of organisational knowledge. The data collected in the repository is searched to provide collections of up-to-date and relevant material for training in a work environment. The aim is to improve knowledge collection and sharing in a team environment. This knowledge can then be collated into a story or workflow that represents the present knowledge in the organisation.

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This paper propose an enhanced E-Learning Social Network Exploiting Approach focused around chart model and clustering algorithm, which can consequently gathering dispersed e-learners with comparative premiums and make fitting suggestions, which can at last upgrade the collective learning among comparable e-learners. Through closeness revelation, trust weights overhaul and potential companions change, the algorithm actualized a programmed adjusted trust association with progressively upgraded fulfillments.

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Nowadays, a huge amount of ambiguous e-learning materials are available in World Wide Web irrespective of various objectives. These digital educational resources can be reused and shared from centralized online repository and it will avoid the redundant learning material. The main goal is to design consistent adaptable e-learning course material for web-based education system with emphasis on the quality of learning. This can be done by organizing learning object in a prescribed manner and it can be reused in feature. Such reusable learning objects are enhanced further to become adaptive reusable learning objects that are virtually cognitive and responsive towards the specific needs of the user/customer. This paper proposes the cognitive architecture to offer an adaptive reusable objects (RLO) based on individual profile of e-learner besides their cognitive behaviour while learning.

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The advent of asynchronous web based learning systems has helped the learner in a self paced, personalized and flexible learning style. It can be even more useful with a supportive synchronous tutorial (question-answer) session. The challenge is to provide sufficient information to the instructor about the learner’s experience in that particular course at run time. Online analytical processing (OLAP) is a very useful technique in producing such run time information in the form of reports. In this paper we have designed an automated scaffolding technique to hold this vital information about the learner which we have obtained by OLAP techniques on the log data of the LMS users. We have also proposed an overall architecture of the scaffolding where this information can be easily accessed and used by the instructor in the synchronous tutorial session to make the system more adaptive.

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The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.

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ICWL 2009

3. Introduction

7. Related Work

10. Framework Foundation

11.

12.

13. Basic Elements of a User Adaptive System

14. Basic Elements of a User Adaptive System

15. Layer Model Components

16.

17. Context Acquisition Layer

18.

19. Information Classification Layer

20.

21. Learner Model Layer

22.

23. Information Extraction Layer

24.

25. Learner Profile Representation Layer

26.

27. Reasoning Layer

28.

29. Interface Layer

30.

31.

32. Learner Control over the M-learning Application

33.

34. Conclusion & Future Work

35.

36.

37.

38.

ICWL 2009

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