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
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.
-  D. Perkins and G. Salomon, “Transfer of Learning,” Int. Encyclopedia of Education, Second Edition, pp. 1-13, 1992.
-  D. Pratt and J. Collins, “The Teaching Perspective Inventory,” In Proc. of the 41st Adult Education Research Conf., 2000.
-  R. Felder and R. Brent, “Understanding Student Differences,” J. of Engineering Education, Vol.94, No.1, 2005.
-  R. Ellis, “Understanding Second Language Acquisition,” New York: Oxford University Press, 1995.
-  D. H. Brown, “Principles of Language Learning and Teaching,” New York: Pearson Education Inc., 5th edition edition, 2007.
-  M. Williams and R. Burden, “Psychology for Language Teachers: A Social Constructivist Approach,” Cambridge: Cambridge University Press, 1997.
-  M. Immordino-Yang and A. Damasio, “We feel, therefore we learn: The relevance of affective and social neuroscience to education,” J. Compilation, Vol.1, No.1, 2007.
-  M. Obeidat, “Attitudes and Motivation in Second Language Learning,” J. of Faculty Education UAEU, Vol.18, No.22, 2005.
-  M. Wei, “The Interrelatedness of Affective Factors in EFL Learning: An Examination of Motivational Patterns in Relation to Anxiety in China,” TESL-EJ, Vol.11, No.1, 2007.
-  C. Halpern, “An Investigation of Linguistic, Cognitive, and Affective Factors That Impact English Language Learners’ Performance on A State Standardized Reading Achievement Test,” Ph.D. thesis, College of Education at the University of Central Florida, 2009.
-  N. A. Kumar and G. Uma, “Improving Academic Performance of Student by Applying Data Mining Technique,” European J. of Scientific Research, Vol.34, No.4, 2009.
-  Z. Karamouzis and A. Vrettos, “An Artificial Neural Network for Predicting Student Graduation Outcome,” In Proc. of the World Congress on Engineering and Computer Science 2008 (WCECS 2008), 2008.
-  Z. Ibrahim and D. Rusli, “Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision Tree, and Linear Regression,” In 21st Annual SAS Malaysia Forum, 2007.
-  R. Lippman, “An Introduction to Computing with Neural Nets,” Vol.4, IEEE Trans. ASSP Magazine, 1987.
-  B. Bloom, M. Engelhart, J. Edward, and H.W. D. Krathwohl, “Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain,” Longmans, Green and Co. Ltd., London, 1956.
-  E. Horwitz, M. Horwitz, and J. Cope, “Foreign Classroom Anxiety,” The Modern Language Journal, Vol.70, No.2, 1986.
-  C. Bishop, “Neural Network for Pattern Recognition,” Oxford University Press, 1995.
-  D. George and P. Mallery, “SPSS for Windows step by step: a simple guide and reference 4th edition,” Allyn and Beacon, 2003.
-  D. Rumelhart, J. McClelland, and P. D. P Research Group, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition,” Vols.1 and 2, MIT Press, 1986.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.