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Student Grade Prediction Using Machine Learning in Iot Era

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Forthcoming Networks and Sustainability in the IoT Era (FoNeS-IoT 2020)

Abstract

The work proposed in this paper is the application of machine learning techniques in recognizing patterns and predicting student success rate on the bases of their performance on their previous grades in this IoT era. With this, using machine learning algorithms improves predicting student grade efficiently. This method is implemented with their previous academic data for students present in the tertiary institution. However, the education system of students in Portugal have enhanced during the past decades. Precisely, the inadequate achievement of success in critical courses like the Portuguese language and also Mathematics is a grave issue. In this paper, we intend to analyze student’s success in tertiary institutions using ML techniques. Real-world raw data were received by using existing data from the school. The two core courses were modeled, also four ML techniques were tested. The results gotten shows that student success rates can greatly be instigated by their previous performance. With the direct outcome of the research, a more adequate predicting tool can also be developed, which improves education quality and enhances resource management for schools. This study is said to increase student performance greatly if taken into consideration.

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Authors and Affiliations

  1. Computer Engineering Department, Near East University, Nicosia, Mersin 10, Turkey

    Adedoyin A. Hussain

  2. Electrical and Electronic Engineering Department, Near East University, Nicosia, Mersin 10, Turkey

    Kamil Dimililer

  3. Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Adedoyin A. Hussain & Kamil Dimililer

Authors
  1. Adedoyin A. Hussain
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  2. Kamil Dimililer
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Correspondence to Adedoyin A. Hussain .

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Editors and Affiliations

  1. Computer Engineering Department, Northern Cyprus Campus, Middle East Technical University, Mersin, Turkey

    Enver Ever

  2. Artificial Intelligence Department, Near East University, Nicosia, Turkey

    Fadi Al-Turjman

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Hussain, A.A., Dimililer, K. (2021). Student Grade Prediction Using Machine Learning in Iot Era. In: Ever, E., Al-Turjman, F. (eds) Forthcoming Networks and Sustainability in the IoT Era. FoNeS-IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-69431-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-69431-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69430-2

  • Online ISBN: 978-3-030-69431-9

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