JACIII Vol.16 No.3 pp. 375-380
doi: 10.20965/jaciii.2012.p0375


A Neural Network Model of Students’ English Abilities Based on Their Affective Factors in Learning

Fitra A. Bachtiar*, Katsuari Kamei**, and Eric W. Cooper**

*Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan

**College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan

September 15, 2011
November 15, 2011
May 20, 2012
neural network, affective factors, English ability, estimation model
The gap between teaching perspectives and students’ differences may impact negatively on teaching and learning effectiveness, indicating the need for a new approach for bridging this gap. The potentials of artificial neural networks for approximating extremely complex problems encouraged us to develop an estimation model of student English ability. The model was trained using a back propagation algorithm and tested using 154 samples from two universities. The model estimation rate related to student English ability demonstrated a high level of estimation by 93.34% for listening, 94.38% for reading, 94.90% for speaking, and 93.58% for writing.
Cite this article as:
F. Bachtiar, K. Kamei, and E. Cooper, “A Neural Network Model of Students’ English Abilities Based on Their Affective Factors in Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.3, pp. 375-380, 2012.
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