The week's pick
Reseach Article
A Neuro-Fuzzy Model for Predicting Students Performance in Object-Oriented Programming Courses
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 21 |
| Year of Publication: 2019 |
| Authors: Olusola Olajide Ajayi, Temitope Clement Akindele |
10.5120/ijais2019451805
|
Olusola Olajide Ajayi, Temitope Clement Akindele . A Neuro-Fuzzy Model for Predicting Students Performance in Object-Oriented Programming Courses. International Journal of Applied Information Systems. 12, 21 ( June 2019), 26-32. DOI=10.5120/ijais2019451805
Abstract
Failure trend in object-oriented programming courses is mostly on the increase side, student’s performance in other courses are most times better than in programming courses. One of the ways to improve the student performance is for the instructors to identify the group of students who might not perform well at the later stage of learning. From there the instructor can focus on the students in order to help them to improve their performance. Thus, in this case making the prediction of student performance a major step in identifying the potential students that needs further help such as extra classes or special tutorials and assignments. Therefore, the need for performance prediction in programming courses becomes imperative. The study will use neuro-fuzzy model to evaluate the current performance of students and then predict the students’ performances in subsequent object oriented programming courses.
References
- Chapel, V., Ronald C. A., and Mark, A., (2013). “Understanding System Architecture – A Business Brief” A White Paper for Business and Technology Professionals.
- Christopher, W. and Li, Frederick W. B. and Godwin, J. L. (2013), “Predicting performance in an Introductory Programming Course by Logging and Analysing Student Programming Behaviour”, in Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT2013). Piscataway, NJ: IEEE Computer Society, pp. 319-323.
- Czogala, E. and Leski, J. (2000), “Neuro-Fuzzy Intelligent Systems, Studies in Fuzziness and Self Computing”. Springer Verlag, Germany.
- Fantuzzi, C., and Rovatti, R., (1996). “On the Approximation Capabilities of Homogeneous Takagi-Sugeno Model”. Proceeding of the Fifth IEEE International Conference on Fuzzy System, Pages 1067 – 1072.
- Hamam, A., and Georganas, N. D., “A Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Evaluating the Quality of Experience of Hapto-Audio-Visual Applications”. HAVE 2008 – IEEE International Workshop on Haptic Audio Visual Environments and their Applications, 2008.
- Han. J and Kamber M. (2006), “Data Mining: Concepts and Techniques”. 2nd ed. San Francisco, California: Morgan Kaufman.
- Jie A., Li Y., Chen B., Ye J. and Zou J. (2010), “The Education Reform and Innovation of Object Oriented Programming Courses in Normal”. The 5th International Conference on Computer Science & Education, Hefei. 978-1-4244.
- Jose, V., Fernando, M. D. and Alexander, M., (2004) “Neuro-Fuzzy Systems – A Survey”. 5th WSEAS NNA Interactions Conference.
- Kamyar, M., (2008). “Takagi-Sugeno Fuzzy Modeling for Processing Control, Industrial Automation, Robotics and Artificial Intelligence”. IEEE 8005.
- Mohd H. R., and Abdullah, E., (2013). “Predicting Student Performance in Object Oriented Programming Using Decision Tree: A Case at Kolej Poly-Tech Mara, Kuantan”. 3rd International Conference on Software Engineering & Computer Systems (ICSECS - 2013), 20-22 August 2013, Universiti Malaysia Pahang. pp. 11-8.
- Mohsen, H. and Zahra, M., (2007). “Application of Artificial Neural Network for Temperature Forecasting”, World Academy of Science, Engineering and Technology, 2007.
- Nauk, D., Klawon, F. and Kruse R. (1997), “Foundations of Neuro-Fuzzy Systems” J. Wiley & Sons. Robert, F., (2001). “Neuro Fuzzy Methods for Modeling and Fault Diagnosis”, Eotuos Lorand University, Buderpest, September, 2001.
- Rojas R. (1996), “Neural Networks”. Springer Verlag, Berlin. Santhosh, B., and Kadar, S. I., (2011), “An Effective Temperature Prediction System Using BPN Neural Network”. IJES, Vol. 2, No. 1, February, 2011. ISSN 2010-0264.
- Sirwan, A. M., and Sattar B. S., (2013). “A Comparison of Mamdani and Sugeno Fuzzy Inference Systems Based on Block Cipher Evaluation”. International Journal of Science & Engineering Research. Vol. 4, Issue 12, December, 2013, ISSN 2229-5518.
- Somerville, I., (2011). “Software Engineering – Ninth Edition”. Pearson Education Incorporation (2011).
- Takagi, T., and Sugeno, M., (1985). “Fuzzy Identification of Systems and Its Application to Modeling and Control.” IEEE Transactions on Systems, Man, and Cybernetics, 15, (1985):116-132.
- Xinghuo, Y., Onder, M. E., and Okyay, K., (2001). “A Backpropagation Learning Framework for Feedforward Neural Network”. IEEE Transactions on Neural Networks – No. 0-7803-6685-9101, 2001.
Index Terms
Keywords