Estimating of student success with artificial neural networks

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Published: Jul 23, 2017
Updated: 2017-07-23
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2017-07-23 (2)
DOI: https://doi.org/10.18844/prosoc.v3i7.1980

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Raziye Demiralay
Alaaddin Keykubat University, Alanya, 07400, Turkey
Ibrahim Akdenizli
Alaaddin Keykubat University, Alanya, 07400, Turkey
Halime Boztoprak
Alaaddin Keykubat University, Alanya, 07400, Turkey

Abstract

Purpose of the study is classifying students’ success of Information and Communication Technology-ICT courses by using Artificial-Neural-Networks-ANNs models. To classify success of 161 students, a three-layer-feed-forward-ANNs model is used. 3 parameters which contain demographic data and 27 parameters which contain ICT-Usage data captured by a questionnaire chosen as input layer parameters. Hidden nodes are determined experimentally. Logic-0 and Logic-1 are the output level values which define success of the students in ICT courses. The Back-Propagation algorithm is used for training of ANN. The mean squares of the errors are used as a performance (error) function with its goal set to zero. In conclusion, the application done with success ratio of 96%. If same variables are used, realistic estimations will be reached. It is recommended that a research with same parameters would be better results with higher participation.


Keywords: Artificial Neural network, backpropagation, student success, classification, ICT.

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How to Cite
Demiralay, R., Akdenizli, I., & Boztoprak, H. (2017). Estimating of student success with artificial neural networks. New Trends and Issues Proceedings on Humanities and Social Sciences, 3(7), 21–27. https://doi.org/10.18844/prosoc.v3i7.1980 (Original work published July 20, 2017)
Issue
Vol. 3 No. 7 (2016): Selected Papers of 6th World Conference on Educational Technology (WCTER-2016)
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Articles
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