Genetic Algorithm Based Adaptive Learning Scheme Generation For Context Aware E-Learning

نویسندگان

  • Manju Bhaskar
  • Minu M Das
چکیده

Context aware e-learning system helps to provide elearning contents which are customized according to the learner’s context. For generating context aware contents many adaptation parameters have to be considered. Customized learning path is one such adaptation parameter. In the existing elearning systems, learning paths is generated using several approaches. But in order to generate context aware contents, the profile context, infrastructure context, preference and learning context of learner have to be considered in addition the learning path. These context parameter values together constitute for the learning scheme of a learner. Hence learning path generation has to evolve into a learning scheme generation which accommodates the entire learner’s context. There are no learning scheme generation algorithms reported in the literature. In this paper a genetic algorithm based adaptive learning scheme for context aware e-learning has been described. KeywordsE-learning, Context Aware E-learning, Adaptive Learning Path generation, Genetic Algorithm

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Optimal Placement and Sizing of DGs and Shunt Capacitor Banks Simultaneously in Distribution Networks using Particle Swarm Optimization Algorithm Based on Adaptive Learning Strategy

Abstract: Optimization of DG and capacitors is a nonlinear objective optimization problem with equal and unequal constraints, and the efficiency of meta-heuristic methods for solving optimization problems has been proven to any degree of complex it. As the population grows and then electricity consumption increases, the need for generation increases, which further reduces voltage, increases los...

متن کامل

Adaptive Neuro Fuzzy Sliding Mode Based Genetic Algorithm Control System to Control of a pH Neutralization Process

In this paper, an adaptive neuro fuzzy sliding mode based genetic algorithm (ANFSGA) controlsystem is proposed for a pH neutralization system. In pH reactors, determination and control of pH isa common problem concerning chemical-based industrial processes due to the non-linearity observedin the titration curve. An ANFSGA control system is designed to overcome the complexity of precisecontrol o...

متن کامل

Adaptive Approximation-Based Control for Uncertain Nonlinear Systems With Unknown Dead-Zone Using Minimal Learning Parameter Algorithm

This paper proposes an adaptive approximation-based controller for uncertain strict-feedback nonlinear systems with unknown dead-zone nonlinearity. Dead-zone constraint is represented as a combination of a linear system with a disturbance-like term. This work invokes neural networks (NNs) as a linear-in-parameter approximator to model uncertain nonlinear functions that appear in virtual and act...

متن کامل

ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF PERTURBED NONLINEARLY PARAMETERIZED SYSTEMS USING MINIMAL LEARNING PARAMETERS ALGORITHM

In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learnin...

متن کامل

Optimization of e-Learning Model Using Fuzzy Genetic Algorithm

E-learning model is examined of three major dimensions. And each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. If any of these undetermined events be considered in the optimization process, t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010