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Predictive Modeling of Students Performance Through the Enhanced Decision Tree

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Advances in Electronics, Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

Abstract

Prognostic of student performance is one of the major issues in many institutions. The student’s performance is based on many factors such as internal examinations, grade obtained in university examination, Academic, Extra Curricular and Co-Curricular activities and also concern with their activities in learning environment. Student performance prediction is used to model the students into any one of the four categories as excellent, good, average, and poor performance student. The category selection was determined using supervised classifiers. Academic institution can easily able to identify any academic problems and the corresponding solutions for their students through this predictive modeling approach. We have collected real world dataset related to student’s academic performance from leading academic institution in India which consists of details about the students such as CGPA, Lab performance, History of arrears and so on. We have applied various supervised classifiers such as DT, SVM, KNN, NB, NN and Improved DT on student’s academic performance dataset. Besides, the conventional decision tree is further improved by the use of normalized factor and Association Function. By comparing the accuracy of these two methods, the best result is exposed for Improved Decision Tree than all other supervised classifiers in the literature.

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Author information

Authors and Affiliations

  1. Faculty of Computing, Botho University, Gaborone, Botswana

    Subitha Sivakumar

  2. Department of Information Systems, BIUST, Gaborone, Botswana

    Subitha Sivakumar & Rajalakshmi Selvaraj

Authors
  1. Subitha Sivakumar
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  2. Rajalakshmi Selvaraj
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Corresponding authors

Correspondence to Subitha Sivakumar or Rajalakshmi Selvaraj .

Editor information

Editors and Affiliations

  1. Smart Energy Research Unit, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Akhtar Kalam

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Kalpana Sharma

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Sivakumar, S., Selvaraj, R. (2018). Predictive Modeling of Students Performance Through the Enhanced Decision Tree. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_3

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  • DOI: https://doi.org/10.1007/978-981-10-4765-7_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

  • eBook Packages: EngineeringEngineering (R0)

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