Abstract
The Personalized Education System (PES) provides appropriate counseling pro-gram as per the different demands and the natures of learners. Its education quali-ty depends on the individuality to a great extent. The Brain Neural Network (BNN) can automatically analyze the learners’ profiles from their feedback data. In light of the above, this paper analyzes the forgetting curve of the learners in the system by building the brain neural network. Take the word memory in English learning as a study case. This curve will help customize the learning content for those learners precisely to hit their strides with a new high-rise personalized edu-cation. Experiment bears out that the forgetting curve generated by the BNN more adapts to the learner's memory law than the traditional universal Ebbinghaus memory curve. The new memory curve makes it possible to improve the effect of PES more effectively and the teaching principle more scientifically.
Highlights
The information era has witnessed the springing-up of new knowledge and the growth of knowledge-based economy
Upon receiving a learner’s request to learn/preview English words, the English word memorization system (EWMS) will transfer the current time and word library selected by the learner into the neural network, which will return a set of labels corresponding to the word library
With a forward structure, the multilayer perceptron (MLP) is an artificial neural network (ANN) that maps a set of input vectors to a set of output vectors
Summary
The information era has witnessed the springing-up of new knowledge and the growth of knowledge-based economy. The traditional education models, despite their popularity and scalability, cannot adapt to the disparity in learners’ intellectual levels, cognitive competence and curriculums [1,2]. The PES refers to a set of teaching programs tailored according to the potentials, education backgrounds, qualities and occupations of individual learners. In other words, this education model teaches students in accordance of their aptitude. A number of artificial neural network models have been developed, mimicking BNNs [7,8,9] These network models can automatically analyse the learners’ profiles from their feedback data, and sheds new light on reducing the education cost in the PES. The learning content was customized according to the curve, aiming to achieve the goal of the PES
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