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Prediction of Learning Disorder: A-Systematic Review

  • Mohammad Azli Jamhar
  • Ely SalwanaEmail author
  • Zahidah Zulkifli
  • Norshita Mat Nayan
  • Noryusliza Abdullah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11870)

Abstract

Learning Disorder refers to a number of disorder which may influence the understanding or use of verbal or nonverbal information. The most well-known types of learning disorder involve an issue with reading, writing, listening, and speaking. When we talk about learning disorder, most people only focusing on social development plan. Therefore, in this study, a systematic review was performed to identify, assess and aggregate on the prediction methods used for a predict learning disorder. The main objective of this paper is to, identify the most common prediction methods for learning disorder, in terms of accuracy by using the systematic review technique. From the main objective, we can define the research questions such as, which is the most common and the most accurate prediction methods used for learning disorder. In conclusion, the most common prediction methods for learning disorder which is Decision Tree and Support Vector Machine. For accuracy, Decision Tree, Linear Discriminant Analysis and K-Nearest Neighbor methods have the highest prediction accuracy for a learning disorder. From these findings, this paper can guide others to predict learning disorder by using the most common methods to get the best result in term of accuracy.

Keywords

Learning disorder Prediction model Data mining Systematic review 

Notes

Acknowledgement

This work was supported by the Ministry of Education under Skim Geran Penyelidikan Fundamental (FRGS) (grant number FRGS/1/2018/ICT04/UKM/02/8).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammad Azli Jamhar
    • 1
  • Ely Salwana
    • 1
    Email author
  • Zahidah Zulkifli
    • 2
  • Norshita Mat Nayan
    • 1
  • Noryusliza Abdullah
    • 3
  1. 1.Institute of Visual InformaticsUniversity Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Information Systems, Kulliyyah of ICTInternational Islamic University MalaysiaKuala LumpurMalaysia
  3. 3.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn Malaysia (UTHM)Parit Raja, Batu PahatMalaysia

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