Many studies have been conducted in the field of benefits of using information and communication technology (ICT) in teaching and learning by researchers in various courses. Learners, who apply ICT in their studies, enjoy open environment in learning. They become responsible for their own learning and are better able to make their own choice in courses and modules (
Online educational tools should not only present content but also should be capable of interacting with users based on their level of knowledge and methods, and this can be done through intelligent agents. Intelligent agents play an important role in personalization of e-learning environments. In the personalization process, agents provide behavior of the intelligent system, and intelligent agents work together to achieve personalization (
2). This research examined the designing dimensions of personalized e-learning environment scale based on intelligent agents and presentation of an integrated model from 11 intelligent agents and communications related to these agents.
Personalization ensures that educational system considers weakness and strength points of each individual who participate in educational careers (
3). Personalization of curriculum requires different levels of application due to reasons like background knowledge, experiences, motivation, and learners’ different behavioral abilities. Intelligent electronic learning systems are capable of analyzing learners’ behavior to create a proper learning model and provide oriented feedbacks to improve educational environment and adapting it with learners’ desires ( 4). Personalization has a wide range and includes custom systems, proprietary, and adapted websites. However, personalization is when a user clicks on the web page and only observes the requested content ( 5).
In other hands, research indicates that personalized learning environment has increasingly led to appearance of intelligent agents whose most notable reason is to determine learners’ ability of continuous monitoring and achieving. In addition, agents’ characteristics such as autonomy, involvement, reactivity, and their participation play an effective role in monitoring the learning process and ultimately presenting the content, tests, and personalized recommendations (
6). For this reason, constructing the proposed system in the research depends on multiple agents.
Intelligent agent is an entity with a level of intelligence and autonomy, which is able to activate and interact with its environment in to access determined purposes (
7). More recent studies in the field of e-learning have concentrated on applying multi-agent systems in electronic education environment. In general, intelligent agents are able to monitor behavior, evaluate learner’s performance and importance of the way of transmitting recommendations, and improve quality of learning. Therefore, designing and implementing e-learning environment has increased ( 8). Finally, this study aimed at investigating a model for designing personalized e-learning environment based on intelligent agents.
Thakare et al. conducted a research (intelligent online e-learning systems: a comparative study). They surveyed the various online e- learning designs and then made a comparison among them. Then, they developed an effective online e- learning system requiring 9 fold agents based on the proposed model such as analyst profile agent, resource agent, recommended fuzzy agent, data mining agent, test agent, adaptive queries agent, user interface agent, trainer agent and classification agent (
Xu et al. in a study applied an intelligent agent-supported personalized virtual learning environment to enhance e-learning effectiveness. According to these researchers, virtual learning environments (VLES) were grounded in constructivist learning theory, and consequently, they improved personalized functions of learning since virtual learning environments are able to meet learner’s various preferences. For this reason, the effectiveness of e-learning will be increased. An empirical field experiment involving 228 university students was conducted. The findings suggested that personalized e-learning facilities enhance online learning effectiveness in examination, satisfaction, and self-efficacy criteria (
This research is a model composed of 11 intelligent agents and 4 infrastructure layers (
Figure 1) as follow:
Figure 1. Proposed Prototype Model
The first layer or the common layer includes 3 basic components, which are the manager, the user, and the instructor in the e-learning environment.
The second layer or the user layer includes 5 intelligent agents, which are recording activity, personalization, interaction, the user, and accessibility.
The third layer or the middle layer includes 6 intelligent agents, which are educational agent, diagnosis and recommendation of style, planning agent, e- content agent, resources location agent, and posttest agent.
The fourth dimension or supporting layer includes instructors’ accessibility and responding, advising and supporting, transparency about the structure of the course and its purposes, and compatibility of supporting and services with learners’ needs and features of the courses.
The fifth layer or database includes learner’s profile, teaching-learning theories, personalized recommendations of learning style, e-content, and personalized tests.
A brief explanation of the 11 agents has been provided in this study as follows:
Accessibility agent: This agent makes possible the feasible accessibility to available resources in the database to satisfy specific needs of the learners and it activates an appropriate learning environment (
Interactive agent: The interactive agent creates a platform, where goals and contents of learning change for participation of the users. In addition, this agent changes, sets, and manipulates content according to the user’s preferences (
Personalization agent: Personalization agents of function make possible learning programs, materials, and exams. These agents make an interaction between different learners with the purpose of sending and receiving immediate messages. Moreover, it manages the learning content, the model of learners, the learning program, and the corresponding interaction in system (
User agent: This agent follows user’s orders, schedules, meetings, screens e-mails, news, and selects good books. The purpose of this agent is to reduce users’ workload through personalizing agents that process personal affairs efficiently (
E-content agent: This agent receives requests from users, retrieves relevant information from the e- content data source, and provides information for users through the agent source.
Planning agent: The intended agent receives questions and results of exams from learning interface and recommends appropriate lessons to learners.
Recording activity agent: It records learners’ interactive activities during the learning process and creates user's profile. According to this profile, the agent of modeling reviews the model of learner in a certain time sequence (
Educational agent: It is a human simulated agent that is activated on the computer of users, and it interacts with learners as they work on web-based educational materials (
Diagnosis and recommendation of style agent: It offers proper recommendations to the learner, trainer, and the designer of the educational environment via receiving information from the learner’s file and output of the system of the learning style (
Posttest agent: It estimates the learner’s capability and selects an examination form the database of quizzes, whose difficulty complies with the learner’s capability. Also, the agent records the ability of the learner in exams (
Resources location agent: It acts as a facilitator and provider that makes possible placement and uses learning resources for learners and educators. The resources location agent includes a wide range of learning materials that support and facilitate active learning (
Questions in the survey are as follow:
General question: What kind of model can be proposed for personalized designing e-learning environment based on intelligent agents?
1. What is the model proposed for personalized e-learning environment based on intelligent agents?
2. Is the proposed model of personalized e-learning environment based on the intelligent agents valid and reliable?
3. What are the elements of personalized e-learning environment based on intelligent agents?