An evaluation of website-based education system in Language C
With the advent of internet, most of the day to day activities have been influenced by it. Learning is one area where internet has been able to bring in a significant change in the way of delivery and management of learning. It is now possible to provide training to people in different parts of the world and across time zones. Internet also introduced greater flexibility in the learning system. It is now possible to reach a wider audience within a smaller time frame. In most aspects, e-learning still retains most of the disadvantages of a traditional distance education system.
It has provided more flexibility to deliver but has not provided greater visibility for the learning centres and the coaches till now. One of the issues with traditional distant learning methods has been that the interaction between the coach who designs and delivers the learning module and the learner. This is because of the distance and the method of delivery where it is usually based on paper. The delay in training material reaching the learner also meant that the coach has limited influence on how the material is used and it is nearly impossible to continuously monitor and provide support.
(Jhonston, 1997). E-learning provided a platform for providing higher degree of interaction by allowing learners to login and access the material at the convenience of the learner. It also allowed greater interaction between the learners, between themselves as well as between the learners and the coach. Even with e-learning facilities, the level of interaction between the coach and the learner was limited due to the limited delivery options. The delivery of the coaching material was usually through multimedia files and documents.
This provided limited emotional interaction between the learners and the coach. An agent provides this missing link, bringing the coach closer to the learner. The learner can now interact with an artificial image that can provide animated responses to the learner. There are now possibilities that artificial intelligence provides capability to introduce more human element into the agent. The agent can now learn, gauge the behaviour and adapt to the new scenarios and behavioural patterns of the learner. (McIsaac et al)
Even with advancement of technologies, the e-learning technologies have not progressed in line to reduce the gap between the traditional proximity based learning system and the distance education. This has resulted in a large number of e-learning solutions still providing the same or just marginally higher learning experience compared to the traditional distance education. (Nasseh, 1997) E-learning has now the opportunity to bring in more aspects of complexities associated with education, thereby extending the scope of distance education which has been limited by the option available.
This research focuses on tapping the new possibilities available and applying them to get better results from educational systems. This research looks at improving the level of interaction of the e-learning systems by introducing a artificial intelligence based agent. The agent is designed to provide human element in delivering of the content, learn continuously to react and answer the questions that the learners pose and reduce human intervention in the process of learning. This is beneficial to both the learner and the learning centre by providing greater personalisation for learning without additional cost and dependency on coaches.
1. 2 Aim The aim of the research is to design, develop and evaluate an agent based e-learning system that can be effectively deployed for C-programming. The agent is designed in such a way that it can be plugged in or interfaced to a e-learning system. 1. 3 Objective The research focuses on the reduction of gap in learning experience in e-learning compared to the traditional learning methods and systems. Although the focus is to identify technologies and solutions specifically for C-language programming learning, a larger objective is kept in mind during the research.
The research looks at general aspects of e-learning and attempts to use C-language as a case study for the general solution. The research is aimed to develop an agent based e-learning system that can be used by learning centres. The primary question that this research focuses is: – Identify the applicability of agent based e-learning system for C programming language. The research looks into the detailed aspects of this question by looking at a set of sub-questions. The research question can be divided into different sub-questions: 1. Is agent based e-learning tool feasible?
The objective of this question is to look at how agent based e-learning systems are functioning and to evaluate whether such systems are feasible to develop. 2. Is agent based e-learning tool satisfy all the requirements for learning of programming languages such as C language. This question puts the agent in perspective of software languages and evaluates the application in that environment. 3. Can the agent based system be designed based on e-learning standards to ensure interoperability? Standardisation helps the industry to provide solutions that are compatible and make use of the common aspects.
It can lower the cost and provide greater effectiveness on a longer run. This questions attempts to identify any possibility of using standards for e-learning system. The research focuses on identifying the result of these questions through design of the system using agent based e-learning. 1. 4 Project Overview The project studies the existing implementations, systems and standards available that are used for e-learning purposes. Further it looks at the current e-learning technologies for C-language. The analysis is done generically to ensure that it can be applied to any software language training.
The project further identifies the requirements for the e-learning system components specific to this project, i. e. agents. The design of the agent is then done based on the requirements and re-using the existing components and standards available. A GUI design is also designed for the purpose. The project also identifies the areas that require further work and how this work can be used effectively in a larger environment. CHAPTER 2 Literature Review The literature review looked at various available systems that were developed and the techniques used in the systems.
The following technologies and systems were found to be relevant for the purpose of this research ? E-learning standards based on IEEE LTSA ? User profiling and storing of user profiles using Learner Information Systems ? Adaptive learning ? Classification of emotions The following sections provide an overview of the key aspects of the above aspects. 2. 1 Agent Based E-Learning System An agent is a software entity simulated in an environment, autonomous, reactive to the changes in its environment and also proactive in attaining its goals as well as social. (Harbouche et al, 2007)
Need for agent based e-learning systems An agent-based learning environment to overcome the ineffectiveness of the e-learning centres for applications: ? Problems due to different forms of delivery of content; ? Problems due to isolation of the learner and the loss of motivation and the learner independence; ? Lack of contact between the learner and the coach; ? Lack of coordination and structure for the traditional e-learning methods; ? Cost considerations of putting human members for each of the courses and the dependency on them to cater to learners in different time zones and locations.
An agent based e-learning system provides solution to the above problems using an agent that can conduct the learning activity on behalf of the coach. This re-usable machine based model can provide dependent and predictable behaviour as well as provide more responsiveness and interaction to the learners. The advantages of having an agent for e-learning purposes are: ? It can help to take into account problems underlined by different forms of distance learning such as sociological isolation of the learner, the loss of motivation and the learner independence;
? It creates a means to make distance education closer to the face-to-face environment while allowing more flexibility in terms of time, presence and space; ? It provides an assistance to learners and replaces teachers/coach during a working session; ? Helps to regroup learners in a productive direction who are left out of the correlation; ? Augment and favour correlation between different agents such as human and artificial; ? Coordinate cooperative and collaborative tasks between the learners; (Harbouche et al, 2007)
Agents can be applied to the e-learning realm for providing enhances level of interaction to the learner even if machine interfaces are used. Adaptive e-learning systems are used for implementation of intelligent systems. This section deals with the artificial intelligence, adaptive learning and how artificial intelligence can be effectively used for adaptive learning systems. The agents that are required for e-learning purpose are: Curriculum agent This agent saves the history of the progression of the learner in the exercise.
The agent analyses the profile of the learner and draws out the sessions of activities for the learner based on the models defined. The agent keeps information of: ? History of the progression of lessons by the user; ? History of progression of learner in the exercises; ? Stores the model of the learner throughout the learning such as programming practices; ? Update of important information that changes the model. This information is presented to the coach to before updating or automatically updated to the model. Coach agent The coach agent performs the following activities: ? Ensure follow-up of the training of each learner;
? Support learner in the activities such as programming exercises; ? Support human relations and contacts between the learners; ? Help with the training methods; ? Help learner with more information on the ways, preferences, and difficulties in going through the training. ? Evaluate the tests answered by the learners; ? Manage access and updating of the persistent information such as the leaner database, knowledge database; ? Ensure synchronous and asynchronous communication between the members; ? To manage the meeting time taking account of the availability of the various actors; (Neji et al, 2007)
Adaptive learning Froschl describes the use of adaptive learning in e-learning agents. The adaptive learning is based on user profiling and modelling of the user from the profiles. The profiles are set of information about the learner that describes the characteristic of the learner. A model is derived from this profile information based on the most common functions required for the e-learning purposes. Adaptive learning involves constant updating of the the profile and the refinement of the models. The user models can be applied effectively for the instructional part of the e-learning systems. (Froschl, 2005)
Adaptive e-learning systems employ model of the user. A user model is used as an internal representation of the user’s property. The model relies on information collected about the user and subsequently generates a model to represent the user. The system adapts itself to the different circumstances and user characteristics to provide high degree of interaction. The adaptive learning system works on the user profiling and user modelling. A user profile is a collection of personal information that can be used for identifying the characteristics that affect the behaviour of the agent. The information is stored without interpretation.
The model of the user is based on the user profile information. Hence this is only a small part of the real user. The applicability of this model depends on how close the models can represent the users in the particular context in which it is done. User models can be effectively used for instructional part of e-learning systems. The system can not only consider the positive attributes of the user such as knowledge, preferences, goals, learning capacity, it can also look at the limitations such as disabilities, lower learning capacities, limitation on time etc. Application Agent design is based on the following characteristics of e-learning
? E-learning systems are open, dynamic and complex; ? Agents are a natural metaphor of human acts and behave in a way that is controlled by a pre-defined boundary; ? The agent has a high-level representation of behaviour of humans. A goal based approach is found to be suitable for e-learning the courses that are offered by the learning centres have pre-defined objectives and desired outcomes. The adaptation process can be divided into three stages: ? Retrieving information about the user; ? Processing information to initialize and update the user model; ? Use the user model to provide the adaptation. (Froschl, 2005)
Emotional states of software agent Masum, et al des describes the mapping of emotions to the e-learning system by representing them as characteristics of software. It describes various emotions such as blank, motivated, curios, hesitated, disappointed, failed, anxious, contented. These emotional roles can be used to represent the different behavioural aspects of the agent. The emotions can be used to provide the right emotions to the learner upon finding the emotional status of the learner. One example is when the learner’s emotion is blank, the agent can be a promoter by providing a friendly elaborative, hopeful and welcoming response.
While collecting of these emotions from the learner is out of scope of this research the principle can be effectively used for selecting and displaying the agent’s emotions. The literatures reviewed provide information on the existing work done on agents, standardisation, artificial intelligence, adaptive learning and representation of emotions. These principles are put to use in the design of the intelligent agent. (Harbouche et al, 2007). To correspond effectively with the different types of e-learners, the different roles of software agents have to carry out different emotional characteristics of the software…
The different emotional roles and the traits are: ? Blank – The traits of this emotional role are desire, uncertainty, hope, imagination, dull ? Motivated – Interest, comfort, motivation, approaching, encouragement ? Curious – Thrill, trusting, anticipatory, expecting, curiosity ? Hesitated – Discomfort, confusions, dissatisfaction, hesitation ? Disappointed – Shame, embarrassment, pessimism, worry, disappointment, anger ? Failed – Boredom, tired, exhausted, inattentive, inactive, drowsy, sad, disgust ? Anxious – Fear, anxiety, enthusiasm, excitement
? Contented – Pride, confidence, calm, satisfaction, lively, happy, contentment. (Masum, et al, 2005) When different emotions are played by the learner the agent can adopt a definite set of responsive emotions that can help to cement a relationship and motivate a learner. The different emotions for the software agents are: ? When the leaner’s emotion is blank, the role of the agent is to be a promoter. This involves characteristics such as friendly, elaborative, hopeful and welcoming; ? When the leaner is motivated, the role of the agent is to be parental by simulation, encouraging, being wishful and approaching;
? When the leaner is curios, the role is pedagogue, in the mode of lesson and teaching where the characteristics are informative, pedagogy, loving, polite, calm and rationale; ? When the learner is hesitated, the role of the agent is advisor/counsellor where the action is motivation and the characteristics are cheering, confident, enthusiastic and caring; ? When the leaner is disappointed, the role is buddy where the action is inspiration characterized by amicable, cordial, inspiring, optimistic, approving and agreeable;
? When the learner is failed, the role is entertainer with the action of amusement characterized by energetic, flexible, funny and animated; ? When the learner is anxious, the role of the agent is coordinator with the action of explanation and characteristic as eloquent, skilful, agile, helpful and expressive; When the learner is contented, the role of the agent is admirer with the action of praise characterized by excited, proud, happy, suggestive and satisfied. (Masum, et al, 2005)
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