**7. References**


**0**

**8**

*Japan*

**Intelligent Tutoring System with Associative**

In self-directed learning such as e-learning, it is significant for learner to recongnize his understanding level. Wrong judgment would provide inefficient learning, and cause a lack of motivation. Test is widely used as an effective method evaluating learner's understanding level, and we often judge it based only on its score (accuracy rate). For easy system construction, many e-learning systems use a multiple-choice test, which often provides a right

Studies on estimating learner's understanding level by using learning history and test have been reported. One of them is construction of learner model based on accuracy rate and answer time in test (Sobue et al., 2004). However, it requires an enormous amount of data and

Another solution is to use heuristic model. It is not appropriate to classify learners into at most two groups (either comprehend or not comprehend) due to effective instruction. Then, we add answer time as an objective criterion. Their combination can provide various understanding levels and more precise estimation. The problem that estimates leaner's understanding level based on objective information can be related to ambiguous classification. Heuristic model is

Many intelligent tutoring system (ITS) (Gunel, 2010; Nkambou & Bourdeau, 2008) has been developed in recent years. Constructing an accurate learner model is an important element in ITS. The method that generates fuzzy rule and uses classification has been proposed (Takashi et al., 2006). However, it requires enormous data. The methods that use Bayesian Network which utilizes statistical approximation and Support Vector Machine (SVM) which has a high generalization ability have been reported (Okamoto & Kayama, 2008; Sumada et al., 2007). The models have significant problems with computational amount and

This study focuses on Cellular Neural Network (CNN) (Chua & Yang, 1988) which is one of

2. Calculation efficiency of CNN is better than that of full-connected neural network as

3. Behavior of CNN is expressed by first-order differential equations, and the input-output relation is represented by a linear function with saturated regions. The function provides

neural network models because it has the following remarkable characteristics: 1. CNN can be easily implemented by arranging a simple analog circuit called cell.

represented by Hopfield model because CNN has a local connectivity.

**1. Introduction**

effective for it.

learning time.

a high computation accuracy.

answer with no comprehension.

consideration of learner's uncertain elements.

**Cellular Neural Network**

*Department of Humanities, Yamanashi Eiwa College*

Michihiro Namba

Pereira, J. R, & Pinho, P. (2010). Using Modern Tools to Explain the Use of the Smith Chart, *IEEE Antennas and Propagation Magazine*, Vol. 52, No. 2, (April 2010), pp. 145-150, ISSN 1045-9243
