Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 375-380 |
| Number of pages | 6 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 16 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - May 2012 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
Keywords
- Affective factors
- English ability
- Estimation model
- Neural network
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