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Darrel (2009),. Enrico et al.(2004),. Srimathi & Srivatsa,. IMS (2003),. Jeongwoo et al. (2006),. Jose (2008),. Thyagharajan & Nayak (2007),. Peng et al. (2007),.
Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346

STATIC CONTEXT MODEL FOR CONTEXT AWARE E-LEARNING Minu. M. Das* Department of Computer Science and Engineering, Pondicherry University Pondicherry, India [email protected]

Dr. T. Chithralekha Department of Banking Technology, Pondicherry University Pondicherry, India [email protected]

Dr. S. SivaSathya Department of Computer Science and Engineering, Pondicherry University Pondicherry, India [email protected]

Abstract: Context aware E-Learning systems provide learning content according to a learner’s context. In order to determine a learner’s context, the parameters that constitute the context and the values of these parameters in the current learner’s situation have to be found. There are several existing context aware E-Learning systems and each of these are taking care of some of the context parameters - like learning styles, learner preferences etc. But, a standardized static context model that helps to capture a learner’s context in its entirety is not available. This paper proposes a static context model that helps to capture a learner’s context. The static context model is developed by consolidating the various context parameters used in the existing context aware E-Learning systems and organizing them into an appropriate structure. The structure of the static context model along with the parameters that constitute the context is explained in the paper Keywords: E-Learning; personalized E-Learning; context aware E-Learning; context models. 1. Introduction In traditional teaching, the tutor will give information and resources such as text books to the learner and the learner should follow that particular sequence to learn the materials. But nowadays, E-Learning is becoming popular. In E-Learning a learner can study the materials according to his/her interest. Most of the E-Learning systems are not personalized. Through personalized E-Learning systems the learner can get the information according to his/ her pre knowledge, skill, interests etc. Learner’s context should be taken care for personalizing the E-Learning system. According to the free online dictionary Howe (2006) context is defined as “Context is that which surrounds, and gives some meaning to, something else”. In Dey (2001) context is defined as “any information that is used to characterize the situation of an entity”. Context awareness in E-Learning system is to select or filter the learning resources in such a way that learner will get the relevant result or information according to the learner’s need, interest, situation etc. This would help a learner to obtain a personalized learning experience. There are several existing context-aware E-Learning systems. But, in each of them only a certain subset of context parameters are considered. This subset of parameters would not help to capture a learner’s context completely. Hence, in order to develop a comprehensively context aware E-Learning system, a static context model that helps to accommodate all the details of a learner’s context is essential.

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Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346 This paper proposes a static context model which could be used to develop a context aware E-Learning system. The static context model has been developed by compiling the various parameters proposed in the existing context-aware E-Learning systems. These parameters are organized into three sub-contexts namely the Situation Context, Abstraction Contexts and Personal Context. Using this static context model, an evaluation of the context models of the existing context-aware E-Learning systems has been carried out. This evaluation helps to evince the inadequacies of the existing context models in capturing a learner’s context. Section 2 describes the context-aware E-Learning system. Section 3 provides an overview of the existing works in context aware E-Learning systems. Section 4 explains the newly proposed static context model for context aware E-Learning system. Section 5 provides the evaluation of existing context models against the static context model described in this paper. Section 6 gives the conclusion. 2. Context Aware E-Learning Systems This section describes about E-Learning, personalized E-Learning and context aware E-Learning. 2.1. E-Learning By Derek (2003) E-Learning is defined as “the delivery of a learning, training or education program by electronic means. E-Learning involves the use of a computer or electronic device (e.g. a mobile phone) in some way to provide training, educational or learning material”. E-Learning can involve a greater variety of equipment than online training or education, for as the name implies, online involves using the Internet or an Intranet. CD-ROM and DVD can be used to provide learning materials. Distance education provided the base for E-Learning development. E-Learning can be on demand. It overcomes timing, attendance and travel difficulties. Plain E-Learning systems cannot adapt to a learner’s learning requirements. Hence, E-Learning systems evolved and personalized E-Learning systems which enable customized E-Learning for every learner came into existence 2.2. Personalized E-Learning Thyagharajan & Nayak (2007) described personalized E-Learning as a unique, blended educational model that is tailored to the individual learner’s needs and interests. Personalized learning can be used for developing the individual learning programs and also engage these learners into the learning process so that learner’s learning potentials and success can be optimized. Personalized learning should not be restricted by time, place and learner’s other requirements. Personalized E-Learning is mostly focusing on learner’s preferences and current state of a learner to provide the learning content correctly. It does not focus on the learner’s situation. Hence, context- aware E-Learning systems which consider a learner’s situation also were developed. These came to be called as context-aware E-Learning systems. 2.3. Context Aware E-Learning A context aware E-Learning system considers many parameters that contribute for a learner’s contexts. By using these context parameters, the system will give customized information to the user. The definition of context given by ubiquitous computing community as “Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.” Context aware E-Learning systems select or filter the learning resources in order to make the E-Learning content more relevant and suitable for the learner in his/her situation. The selection or filtering of the ELearning resources is done by considering the learner’s personal information, learning style preferred by him, learner’s situation, etc. These parameters constitute for the learner’s context. There are many works done on context aware E-Learning systems and in each of them certain subset of parameters are considered for the context. In the subsequent section the context parameters used in existing context aware E-Learning systems are described. 3. Context Parameters in Existing Context-Aware E-Learning This section gives an overview of the context parameters used in the various context-aware E-Learning systems. The following are the various context parameters considered.

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Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346         

Learner personal profile Level of Expertise Learning Style Learner Preferences or Learner Approach Learner Intention Learner Situation Quality of Learning Service (QoLS) Network Device

3.1. Learner personal profile Learner’s personal profile contains learner’s personal details such as name, ID, Date of Birth, Knowledge of the learner etc. 3.2. Learner’s Level of Expertise Level of expertise is used to indicate whether the person is a beginner or that learner has some preknowledge about a topic or the learner is an expert in that topic. 3.3. Learning Style Learning Style corresponds to media used by the learner for learning his/her lessons. The media can be video, audio, textual, animation etc. 3.4. Learner’s Preferences Most of the existing systems are focusing on Learner’s preferences. Learner’s preferences described in these papers correspond to the conceptual, example-oriented, case study or problem-oriented, demonstration, simulation approaches preferred by the learner for learning the e-content. 3.5. Learner’s Intention Learner’s intention means in what intention the learner is coming for E-Learning site. The learner can come for research purpose or survey purpose or interview purpose or just to learn the concept etc. 3.6. Learner’s Situation Learner’s situation defines the situation of the learner. The learner might be driving some vehicle or he/she might be in private place or in public place etc. The learner’s location details are also included in learner’s situation. 3.7. Quality of Learning Service (QoLS) QoLS contains functional and non functional quality requirements. The functional requirements are network bandwidth and response time. Non- functional requirements are reliability, availability and cost. 3.8. Network Network can be wired network or wireless network. 3.9. Device The device used by learner can be mobile, PC, Laptop, PDA etc. Table 1 shows the list of context parameters which are considered in existing context aware E-Learning systems.

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Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346 Table 1. List of context parameters.

Context Parameters considered

Learner Profile

Level of Expertise

Learning Style

Learning Preference

Learning Intention

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Compiled set of Sub Context Parameters Name ID DOB Gender Address E -mail ID Phone Number Technologies Known Knowledge Level OS Experience Internet Usage

Beginner Practitioner Expert

Video Audio Text Animation Slides

Conceptual Example-Oriented Case Study Simulation Demonstration

Research Survey/ Overview

References

Carla et al.(2008), Enrico et al. (2004), Srimathi & Srivatsa, IMS (2003), Jeongwoo et al. (2006), Jovanovic et al. (2007), Kawanish et al. (2006), Thyagharajan (2007), Maria (2009), Mingfei et al. (2007), Peng et al. (2007), Stefan et al. (2007), Sun Microsystems (2003), Tzone et al. (2008), Xinyou et al. (2008), Yang (2006), Yevgen et al., (2009) Andreas & Claudia (2004), Srimathi & Srivatsa, IMS (2003), Jeongwoo et al. (2006), Koun & Hsin (2008), Mingfei et al. (2007) ADLI (2003), Adriana & Francisco, Andreas & Claudia (2004), Darrel (2009), Enrico et al.(2004), Srimathi & Srivatsa, IMS (2003), Jeongwoo et al. (2006), Jose (2008), Thyagharajan & Nayak (2007), Peng et al. (2007), Sun Microsystems (2003), Yuan, Capus & Nicole (2007) Adriana & Francisco, Carla et al. (2008), IMS (2003), Jeongwoo et al. (2006), Jovanovic et al. (2007), Kawanishi et al. (2006), Thyagharajan (2007), Koun & Hsin (2008), Maria (2009), Mianxiong et al. (2007), Mingfei et al. (2007), Stefan et al. (2007), Sun Microsystems (2003), Tzone et al. (2008), Xinyou et al. (2008), Yang (2006), Yevgen et al. (2009) Enrico et al. (2004), IMS (2003),

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Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346 Quick Reference Basic Introduction Project Assignment Seminar Private Learner Situation

Public Driving

QoLS

Functional Requirement Non- Functional Requirement Wired

Network

Device

Wireless

Mobile PC Laptop PDA

Thyagharajan (2007), Yang (2006)

Bill et al. (1994), Carla et al. (2008), Jovanovic et al. (2007), Kawanishi et al. (2006), Koun & Hsin (2008), Maria (2009), Mingfei et al. (2007) Bill et al. (1994) Carla et al. (2008)

Bill et al. (1994), Carla et al. (2008), Jovanovic et al. (2007), Kawanishi et al. (2006), Mianxiong et al. (2007), Mingfei et al. (2007), Yevgen et al. (2009), Yuan et al. (2007), Zhu (2009) Bill et al. (1994), Carla et al. (2008), Jovanovic et al. (2007), Kawanishi et al. (2006), Koun & Hsin (2008), Maria (2009), Mianxiong et al. (2007), Mingfei et al. (2007), Yuan et al. (2007), Zhu (2009)

From an analytical perspective, the existing works helps to obtain a complete overview of the various aspects to be considered for providing context-aware E-Learning content to the user. But, considering from a critical perspective, the following are the limitations observed.   

Static context model is not followed. In certain works, only some of the contexts parameters are considered. In certain works, only some of the Sub context parameters are taken into consideration.

Since a static model which encompasses the entire set of context parameters that will not change for ELearning course session is yet to be formulated, the same has been taken up as the objective of this work. The details of the context model are described in the subsequent section. 4. Static Context Model for Context Aware E-Learning Context aware E-Learning systems enable to facilitate flexible, adaptive, personalized, and situation aware learning experience for every user. The said features of context-aware E-Learning system could be achieved by the ability to capture a learner’s context by using a set of parameters. These set of parameters and their values in a particular instance constitute for the context. In different E-Learning systems, different subsets of context parameters have been used for capturing a learner’s context. Since the subset is not complete, it cannot help to accommodate all the details of a user’s context completely. Static contexts are constant; it will not change in the entire E-Learning course structure. Hence, it is necessary to derive a static context model that helps to establish

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Minu.M.Das et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2337-2346 a learner’s context comprehensively. This is achieved by consolidating the various context parameters and organizing them into appropriate sub-contexts. This process is explained below. The context parameters described in Table 1 has been segregated into the following sub-contexts   

Personal Context Abstraction Context Situation Context

4.1. Personal Context Personal context is giving the information about the learner’s personal details, personality type and level of expertise of the learner. 4.2. Abstraction Context Abstraction context contains the information about learning approach or preferences, learner’s intention and learning style. 4.3. Situation Context The situation context describes the information about the learner’s situation, network and device used by the user. The organized set of context parameters classified under appropriate sub-contexts constitute for the static model. This is given in Table 2. The context model has been represented in a formalized manner in Figure 1 of representing the context model is given below. Table 2. Structured set of contexts

Sub Context Name

Parameters

Personal Information

Personal Context Personality Type

Level of Expertise

Sub- Parameters Name ID DOB Gender Address Email-id Phone Number Technologies Known Knowledge Level OS Experience Internet Usage Extrovert Sensory Thinkers Judgers Beginner Practitioner Expert Conceptual

Learner Preference Abstraction Context

Learner Intention

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Example-Oriented Case Study Simulation Demonstration Research Survey or Overview Quick Reference

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Learning Style

Learner Situation

Situation Context

Network

Device QoLS

Video Audio Text Animation Slides Private Public Driving Wired Wireless Mobile PDA Laptop PC Functional requirements Non- functional requirements

Context Ontology – {Personal, Abstraction, Situation} Personal – {Personal Information, Personality Type, Level of Expertise} Personal Information

{Name, ID, DOB, Gender, Address, Email-id, Phone Number, Technologies Known, Knowledge Level, OS Experience, Internet Usage}

Personality Type

{Extrovert, Sensory, Thinkers, Judges}

Level of Expertise

{Beginner, Practitioner, Expert}

Abstraction - {Learner’s Preference, Learner’s Intension, Learning Style} Learner’s Preference

{Conceptual, Example- Oriented, Case Study, Demonstration, Simulation}

Learner’s Intension

{Research, Survey, Quick Reference, Basic Introduction, Project, Assignment, Seminar}

Learning Style

{Video, Audio, Text, Animation}

Situation – {Learner’s Situation, QoLS, Network, Device} Learner’s Situation

{Public, Private, Driving}

QoLS

{Functional Requirements, Non-Functional Requirements} Functional Requirements

{Bandwidth, Response Time}

Non-Functional Requirements

{Reliability, Availability, Cost}

Network

{Wired, Wireless}

Device

{Mobile, PDA, Laptop, PC} Fig. 1. Formalized representation of the static context model

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The static context model is a standardized one helping to establish a learner’s context comprehensively. Thus, it provides a reference structure which could be used to develop context-aware E-Learning systems. 5. Evaluations In this section, the evaluation of the static context model is performed by considering the requirements to be fulfilled by context aware E-Learning systems. This is given in Table 3. Table 3. Fulfillment of context aware requirements by the proposed system

Requirements of Context Aware E-Learning Learner’s Ability

Fulfillment in the Static Context Model Fulfilled

Learner’s Preferences

Fulfilled

Learner’s Background Knowledge Learner’s Interest

Fulfilled

Learner’s Skills

Fulfilled

Learner’s Requirements

Fulfilled

Fulfilled

How it is fulfilled Learners abilities are Described in the Personal Context Learner’s preferences are Described in the Abstraction Context Described in the Personal Context Described in the Personal Context Described in the Personal Context Described in Personal as well as Situation Context

Subsequently, an evaluation of the context models of the existing E-Learning systems against the static context model has been carried out. This is given in Table 4. Table 4. shows the whether the existing systems are satisfying the contexts parameters of the static context which is described in this paper. Table 4: Comparison of standard context model with existing context models

Existing Systems ADLI, 2003 Adriana & Francisco Andreas, 2007 Andreas, 2004 Bill et al., 1994 Carla et al., 2008 Darrel, 2009 Enrico et al.,2004 Srimathi & Srivatsa IMS, 2003 Jeongwoo et al., 2006 Jose et al., 2008 Jovanovic et al., 2007 Kawanishi et al., 2006 Thyagharajan, 2007 Koun & Hsin, 2008 Lanzilotti et al., 2006 Maria, 2009 Mianxiong et al., 2007 Mingfei et al.,2007

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Personal Awareness                    

Abstraction Awareness                    

Situation Awareness                    

Total Score 2 1 2 1 1 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1

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          

          

          

2 1 2 1 2 2 2 2 2 2 3

The total score is given based on the context parameters fulfilled. Figure 2 shows the graphical representation of the evaluation. The X coordinate represents the E-Learning systems and Y coordinate represents the Total value that each system obtained. Most of the existing systems are satisfying only 2 context parameters described in the proposed context model. But proposed static context model contains value 3 which is shown in black shade satisfies all the context parameters.

Fig 2. Graphical representation of evaluation of Context models in existing e-learning systems against static context model

6. Conclusion This paper describes a static context model for context aware E-Learning systems. The context is organized into three sub-contexts namely personal context, abstraction context and situation context that will not change in the entire learning course session. In context aware E-Learning systems, the system will give highly customized content to the learner based on the context. However, this paper does not consider about the psychological aspects and user’s cognitive level and also the dynamic contexts. In future psychological aspects, user’s cognitive level details and dynamic contexts can also be included in the context model. References [1] [2] [3] [4]

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