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

Fitra Abdurrachman Bachtiar | Zendy; Katsuari Kamei | Zendy; Eric W. Cooper | Zendy

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

Author(s) -

Fitra Abdurrachman Bachtiar,

Katsuari Kamei,

Eric W. Cooper

Publication year - 2012

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2012.p0375

Subject(s) - bridging (networking) , computer science , active listening , artificial neural network , estimation , reading (process) , artificial intelligence , machine learning , mathematics education , speech recognition , psychology , linguistics , philosophy , computer network , management , communication , economics

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.

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