Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL)

*Badieah Badieah  -  Universitas Islam Sultan Agung, Indonesia
Rachmat Gernowo  -  Universitas Diponegoro, Indonesia
Bayu Surarso  -  Universitas Diponegoro, Indonesia
Received: 27 Apr 2016; Published: 30 Nov 2016.
Open Access
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Article Info
Section: Research Articles
Language: EN
Statistics: 779 6897
Abstract

In order to improve academic quality in higher education, students’ performance evaluation is becoming important. To prevent increasing failure rate in the course, we need a system that is capable of predicting student’s performance in the end of the course. The research used several factors that are considered to affect students' performance on Problem Based Learning (PBL), such as students’ demography, students’ prior knowledge and group heterogeneity.  The method used in the study was Artificial Neural Network (ANN) with backpropagation training algorithm. Total 8 neurons were used as inputs for ANN which were obtained from gender variable (2 neurons), age variable (1 neuron), students’ average knowledge variable (1 neuron), students’ average skill variable (1 neuron) and group heterogeneity variable (3 neurons). Several different ANN architecture were tested in the study using 2, 7 and 12 hidden neurons respectively. Each architecture was trained using various different training parameters in order to find the best ANN architecture. Dataset used  in the research were obtained from Academic Information System in Faculty of Dentistry Unissula which contained Adult and Elderly Diseases Course’s participants from year 2009 to 2013. The ANN output were numeric values which represented students’ performance in Adult and Elderly Diseases Course. The output of this study is a system that is able to predict the student performance in block course. The result shows that using 7 hidden neurons in the network combining with 0.5 ,0.1 and  9000 for learning rate, momentum and epoch respectively, were the best ANN architechture and parameters in the study. The MSE obtained from validation test was 0,011926 with correlation coefficient (R) 0,796879. The prediction system are expected to help faculty and academic evaluation team to conduct actions to improve student’s academic performance and prevent them from failure in the course. 

Keywords: Artificial Neural Network; Problem Based Learning; Prediction

Article Metrics:

  1. Alajmi, N., 2014. Factors that influence performance in problem-based learning tutorial. Ph.D dissertation, Bond University Faculty of Health Science and Medicine
  2. Beskeni, R.D., Yousuf, M.I., Awang, M.M., Ranjha, A.N., 2011. The effect of prior knowledge in understanding chemistry concepts by senior secondary school students. International Journal of Academic Research, 3(2), 607-611.
  3. Bidokht, M.H., 2011. Life-long Learners Through Problem-Based and Self Directed Learning, Procedia Computer Science 3, 1446-1453
  4. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R., 2000. CRISP-DM 1.0 : Step-by-Step Data Mining Guide, SPSS.
  5. Das, M., Mpofu, D.F.S., Hasan, M.Y., Stewart, T.S., 2002. Student perceptions of tutor skill in problem-based learning tutorials. Medical Education, (36), 272-278
  6. Graf,S.,Bekele,R., 2006. Forming Heterogeneous Groups for Intelligent Collaborative Learning Systems with Ant Colony Optimization, In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), 217-226
  7. Hailikari,T., Katajavouri,N., Ylane,S.L., 2008. The Relevance of Prior Knowledge in Learning and Instructional Design, American Journal of Pharmaceutical,72(5) , 1-8
  8. Heaton, J., 2008. Introduction to Neural Networks for C, Second Edition, Heaton Research, St Louis
  9. Hermawan, 2006, Jaringan Syaraf Tiruan Teori dan Aplikasi, Penerbit Andi Yogyakarta
  10. Kardan,A.A.,Sadeghi,H.,Ghidary,S.S.,Sani,M.R.F., 2013. Prediction of Student Course Selection in Online Higher Education Institutes Using Neural Network, Computer & Education 65(2013), 1-11
  11. Karsoliya, S., 2012. Approximating Number of Hidden Layer Neurons in Multiple Hidden Layer BPNN Architecture, International Journal of Engineering Trends and Technology Vol.3 Issue.6, 714-717
  12. Khasei,M., Bijari,M., 2010. An Artificial Neural Network (p,d,q) Model for Timeseries Forecasting, Expert Systems with Applications 37(2010), 479-489
  13. Kovacic,Z.J., 2010. EarlyPrediction of Student Success : Mining Students Enrollment Data, Proceedings of Informing Science & IT Education Conference (InSITE) 2010, 647-665
  14. Laokietkul,J., Utakrit, N., Meesad, P., 2009. A Forecasting Model to Evaluate a Freshman's Ability to Succeed by Using Particular Full-Scaled Class Association Rules (PFSCARs), International Conference of Computer Science and Information Technology-Spring Conference, 40-44
  15. Larose,D.T., 2005. Discovering Knowledge in Data : An Introduction to Data Mining, Wiley-Interscience, Canada
  16. Liu,T.C., Lin,Y.C., Paas,F., 2014. Effects of Prior Knowledge on Learning from Different Compositions of Representations in a Mobile Learning Environment, Computer & Education, Vol.72, 328-338
  17. Moucary,C.E., Khair,M., Zakhem,W., 2006, Improving Student Performance Using Data Clustering and Neural Networks in Foreign-Language Based Higher Education, The Research Bulletin of Jordan ACM Vol II(III), 27-34
  18. Ogor, E.N., 2007. Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques, Forth Congress of Electronics, Robotics and Automotive Mechanics, 354-359
  19. Oladokun,V.O.,Adebanjo,A.T.,Owaba,O.E.C.,2008. Predicting Students Academic Performance using Artificial Neural Networks : A Case Study of an Engineering Course,The Pacific Journal of Science and Technology Vol.9, 72-79
  20. Puspitaningrum, 2006, Pengantar Jaringan Saraf Tiruan, Penerbit Andi, Yogyakarta
  21. Samsudin dan Sunarti, 2006. Cooperative Learning : Heterogenous vs Homogenous Grouping, Apera Conference 2006, 1-6
  22. Siang, J.J., 2009. Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan MATLAB, Penerbit Andi, Yogyakarta
  23. Sugiono, Wu,M.H., Oraifige,I., 2012. Employ the Taguchi Method to Optimize BPNN’s Architectures in Car Body Design System, American Journal of Computational and Applied Mathematics 2012 2(4), 140-151
  24. Wood,D.F.,2003. ABC of Learning and Teaching in medicine : Problem based learning, BMJ, Volume 326, 328-330