In this paper, we propose the E-learning product search system (ELSS) which can provides high quality information fitted to the learners' demands. ELSS can also provide the accurate information that doesn't contain unnecessary information in mass e-learning products on the internet. Through multiple agents, this system extracts the optimal output based on input data such as learner's demands and interests. In addition, BP (Back Propagation) algorithm is applied to the multiple agents to obtain optimal results. Input variables are designed by Extended TAM (Technology Acceptance Model) for the high reliability of ELSS. The factors that learners consider when choosing E-learning products are analyzed by extended TAM and the factors are extracted by SEM (Structural Equation Model) analysis.
After the appearance of the internet, our life style has been remarkably changed. Shopping, financial payment and commercial transaction on the Web are revitalized and the circulation of personal knowledge and creating information has become major flow on the Web. In this new area, the number of people offering or receiving information and knowledge has been increased.
[...] Table 1 Summary of measurement variables Measurement variables Perceived usefulness Perceived ease of use Perceived enjoy Reputation Individual preference Functional service Price Economy of time Marketing Behavioral intention Real Behavior Choose a e-learning product Definition The degree of intention to adopt e-learning will be The convenience of e-learning contents and service The extent to which the activity of using the e-learning is perceived to be enjoyable Opinion or view one about e-learning Contents visual impressions, recommend lecturer Service and contents speed, service efficiency The package price/rate Free from time, economy of moving time Discounts, events, and marketing The behavior intention toward e-learning A learner's real behavior on e-learning The existent e-learning product (Output data of BP) Scale The rating of input variables was based on a scale of being best and 1 worst Multiple-Choice 3.3 Data collection We surveyed people who can easily access the internet and have experience in using E-learning service questionnaires were distributed and 176 questionnaires were analyzed except 24 questionnaires which were incomplete and un-filled Reliability and Factor analysis Before studying SEM, we carried reliability and factor analysis out for internal consistency and validation. [...]
[...] E-learning product search system (ELSS) ELSS is a system which analyzes demands from learners and recommend proper E -learning product. ELSS is designed for the multiple agent which consists of 6 agents. ELSS has a same construction with Fig Personal Agent A personal agent conveys data received from learners to the integrated data store agent. UI consists of facts from SEM analysis using Extended TAM. For receiving learner's exact tastes and demands, this agent design s UI of categories of E-learning and a question form. [...]
[...] It is a unit for achieving an aim with mutual cooperation. And usually it is originated in AI. Because a single agent can work only based on the installed program, it has difficulty processing complex data. The other side, the multiple agent system can communicate with other agents. Thus, it can have united-processing capacity through the cooperation of each agent which has different functions. Because new agents can be added as needed, extension of system is easy. Single agents of multiple agent system must have some abilities like self-control system, communication ability, cooperation ability, adaptation ability, and confidence. [...]
[...] This ELSS is expected to be useful contents to the learners. The contributions through this study can be listed as follows. First, while established studies have been devoted to the technique of e-learning contents and standardization in position of an offer, this study tried to make a new access to search e-learning products in position of learners. In this way, we can provide the learners with detail information about e-learning and suited information. Second, we designed ELSS based on multiple agent applied BP algorithm. [...]
[...] The limitation of this study is the lack of information about e-learning search systems. Thus, we could not have enough comparison analysis in this study. This study needs more studies through practical implementation of system in next time. References Fishbein, M. & Ajzen, I.; Belief, attitude, intention, and behavior: an introduction to theory and research, Reading, MA: Addison-Wesley Venkatesh, Viswanath; Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model, Information Systems Research, Vol No pp. [...]
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