MODELLING SUPPORT SYSTEMS FOR SELECTING PROFESSIONS FOR APPLICANTS IN THE CONTENT OF PERSONALIZATION OF EDUCATION

Keywords: artificial neural network, intellectual system, fuzzy sets, personalization of education, fuzzy logic

Abstract

Various methods are currently being used in examining the initial “START” knowledge of applicants and their placement for specialties. Studies show that applicants are placed on the decreasing principle in terms of their overall scores at universities. In this case, applicants with a high level of knowledge are placed in the prestigious specialties as medicine and law as they require high results. Though, while applying for other professions, the applicants do not perform enough results on the key disciplines for the profession, they are placed in those professions when the general results enable it. This causes them to face a number of problems while working both in education process and in the industry.

To avoid this problem and to place applicants in a specialty that is more relevant to their level of knowledge, the introduction of an individual approach to the evaluation of initial level of knowledge may be more promising.

This article presents a modeling of the "evaluation – placement" support system for the individual approach to assessing applicants' knowledge and positioning them in relevant specialties. The main goal of the system is to give each applicant the opportunity to choose and study the specialty that is more relevant to their knowledge and skills, as well as to analyze the results for each discipline along with the overall results. The system is implemented using fuzzy logic based artificial neural networks.

The network consists of 100 neurons in the input layer, two hidden layers and one output layer. The number of neurons at the output is the same as the number of specialties taught at university.

Downloads

Download data is not yet available.

References

Varghese, N. V. (2013). Globalization and higher education: Changing trends in cross border education. Analytical reports in international education, 5 (1), 7–20.

Reber, R., Canning, E. A., Harackiewicz, J. M. (2018). Personalized Education to Increase Interest. Current Directions in Psychological Science, 27 (6), 449–454. doi: http://doi.org/10.1177/0963721418793140

Mammadova, M., Gasimov, H. (2017). E-UNIVERSITY: CONCEPTUAL, TECHNOLOGICAL AND ARCHITECTURAL APPROACHES. Problems of Information Technology, 8 (2), 51–62. doi: http://doi.org/10.25045/jpit.v08.i2.06

Özcan, B., Güler, E., Yerlikaya, Z. (2017). Kocaeli Mühendislik Fakültesi Mezunlarının Akademik Başarılarının İncelenmesi. Kocaeli Üniversitesi Sosyal Bilimler Dergisi, 34, 143–168. Available at: https://dergipark.org.tr/en/pub/kosbed/issue/43233/524969

Meenakshi, N., Pankaj, N. (2015). Application of Fuzzy Logic for Evaluation of Academic Performance of Students of Computer Application Course. IJRASET 2015, 3 (X).

Apatova, N. V., Gaponov, A. I., Mayorova, A. N. (2017). Forecasting students well doing based on fuzzy logic. Advanced scientific technology, 4, 7–11.

Johnson, A. (2019). 5 Ways AI Is Changing The Education Industry. ELearning Industry. Available at: https://elearningindustry.com/ai-is-changing-the-education-industry-5-ways

Lomakin, N. I., Plaksunova, T. A., Loginova, E. V., Lukyanov, G. I., Kozlova, E. A., Skobora, E. A. et. al. (2017). Neural network for evaluating the competence of students. EDCRUNCH Ural: new educational technology at uiversity–2017. Ekaterenburg, 307–319.

Abuzagia, K. M. (2017). International intentions in the field of information technology (Artificial intelligence systems and their importance in the fields of education). 2017 Joint International Conference on Information and Communication Technologies for Education and Training and International Conference on Computing in Arabic (ICCA-TICET). doi: http://doi.org/10.1109/icca-ticet.2017.8095300

Hahm, N.-W., Hong, B.-I. (2009). A simultaneous neural network approximation with the squashing function. Honam Mathematical Journal, 31 (2), 147–156. doi: http://doi.org/10.5831/hmj.2009.31.2.147

Guzmán‐Ramírez, E., Garcia, I., García‐Juárez, M. (2019). A “learning by design” application for modeling, implementing, and evaluating hardware architectures for artificial neural networks at undergraduate level. Computer Applications in Engineering Education, 27 (5), 1236–1252. doi: http://doi.org/10.1002/cae.22148

Gasimov, H. (2018). Fuzzy sets method to adhere to the student's intellectual potential to choose the path of education. DILET2018 The 2nd International Conference on Distance Learning and Innovative Educational Technologies. Ankara, 240

Mammadova, M. H., Jabrayilova, Z. (2012). Multi-criteria model of decision-making support in the personnel management problems. Problems of information technology, 2, 37–46.

Min, W. Y. (2017). Neural Network Application to Control and Prediction of Educational Process Results in the University. Economic and social-humanitarian research, 4, 130–132.

Arora, N., Saini, J. R. (2014). Predicting student academic performance using fuzzy artmap network. International Journal of Advances in Engineering Science and Technology, 3 (3), 187–192.

Drachsler, H., Kirschner, P. A. (2012). Learner Characteristics. Encyclopedia of the Sciences of Learning, 1743–1745. doi: http://doi.org/10.1007/978-1-4419-1428-6_347

Qasımov, H. (2018). Yapay sinir ağları kullanarak öğrencinin bilgi düzeyine daha uygun üniversite meslek seçimine yönlendirilmesi metödünün yapılandırılması. DILET 2018 – 2. Uluslararası Uzaktan Öğrenme ve Yenilikçi Eğitim Teknolojileri Konferansı. Ankara, 239.

Lyamin, A. V. (2018). Creation of individual learning trajectories based on student’s achievements and functional state analysis. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 115 (3), 543–553. doi: http://doi.org/10.17586/2226-1494-2018-18-3-543-553

Ktona, A., Xhaja, D., Ninka, I. (2014, May). Extracting Relationships between Students' Academic Performance and Their Area of Interest Using Data Mining Techniques. 2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks, 6–11. doi: http://doi.org/10.1109/cicsyn.2014.18

Alsobhi, A. Y., Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53 (2), 189–200. doi: http://doi.org/10.1108/dta-10-2018-0092

Oancea, B., Dragoescu, R., Ciucu, S. (2013). Predicting students’ results in higher education using a neural network. International Conference on Applied Information and Communication Technologies (AICT2013), 190–193.

Gasymov, G. A. ogly. (2018). Development of the mechanism of intellectual management of “student-lecturer” relations in the space of virtual education with the use of neural networks. Open Education, 22 (5), 94–103. doi: http://doi.org/10.21686/1818-4243-2018-5-94-103

Matúšová, M., Hrušková, E. (2019). Applying the Computer Aided Systems in Education Process. Management Systems in Production Engineering, 27 (1), 46–50. doi: http://doi.org/10.1515/mspe-2019-0008

Kotova, E. E. (2017). Intellectual data analysis in the educational process. 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), 757–759. doi: http://doi.org/10.1109/scm.2017.7970714

Aydoğan, İ., Zirhlioğlu, G. (2018). Öğrenci Başarılarının Yapay Sinir Ağları ile Kestirilmesi. Yuzunci Yil Universitesi Egitim Fakultesi Dergisi, 15 (1), 577–610. doi: http://doi.org/10.23891/efdyyu.2018.80

Domenech, D., Sherman, M., Brown, J. L. (2016). Personalizing 21st century education: A framework for student success. John Wiley & Sons, 144.

Ray, S., Saeed, M. (2018). Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education. Applications of Big Data Analytics. Cham: Springer, 135–160. doi: http://doi.org/10.1007/978-3-319-76472-6_7

Jyothi, G., Parvathi, C., Srinivas, P., Althaf, S. (2014). Fuzzy expert model for evaluation of faculty performance in Technical educational Institutions. International Journal of Engineering Research and Applications, 4 (5), 41–50.

State Examination Centre of the Republic of Azerbaijan (2014–2017). Available at: http://dim.gov.az/en/center/

Mammadovа, M. (2019). Methods for fuzzy demand assessment for it specialties. EUREKA: Physics and Engineering, 4, 23–33. doi: http://doi.org/10.21303/2461-4262.2019.00939

Mammadova, M. H., Jabrayilova, Z. G., Mammadzada, F. R. (2015). Managing the IT labor market in conditions of fuzzy information. Automatic Control and Computer Sciences, 49 (2), 88–93. doi: http://doi.org/10.3103/s0146411615020030

Grosan, C., Abraham, A. (2011) Artificial Neural Networks. Intelligent Systems Reference Library. Berlin, Heidelberg: Springer, 281–323. doi: http://doi.org/10.1007/978-3-642-21004-4_12


👁 770
⬇ 439
Published
2020-03-31
How to Cite
Gasimov, H. (2020). MODELLING SUPPORT SYSTEMS FOR SELECTING PROFESSIONS FOR APPLICANTS IN THE CONTENT OF PERSONALIZATION OF EDUCATION. EUREKA: Physics and Engineering, (2), 83-97. https://doi.org/10.21303/2461-4262.2020.001181
Section
Computer Science