Design a personalized e-learning system based on item response theory and artificial neural network approach. Ahmad Baylari, Gh.A. Montazer
Author: Juan José Calderón Amador
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Hoy traemos a este espacio este artículo de Expert System with Applications 36 (2009) titulado : “Design a personalized e-learning system based on item response theory and artificial neural network approach. Ahmad Baylari, Gh.A. Montazer *IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran”
In web-based educational systems the structure of learning domain and content are usually presented inthe static way, without taking into account the learners’ goals, their experiences, their existing knowl-edge, their ability (known as insufficient flexibility), and without interactivity (means there is less oppor-tunity for receiving instant responses or feedbacks from the instructor when learners need support).Therefore, considering personalization and interactivity will increase the quality of learning. In the otherside, among numerous components of e-learning, assessment is an important part. Generally, the processof instruction completes with the assessment and it is used to evaluate learners’ learning efficiency, skilland knowledge. But in web-based educational systems there is less attention on adaptive and personal-ized assessment. Having considered the importance of tests, this paper proposes a personalized multi-agent e-learning system based on item response theory (IRT) and artificial neural network (ANN) whichpresents adaptive tests (based on IRT) and personalized recommendations (based on ANN). These agentsadd adaptivity and interactivity to the learning environment and act as a human instructor which guidesthe learners in a friendly and personalized teaching environment.
Fuente: [ slideshare vía Expert System with Applications ]