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Educational Data Mining: A Review and Analysis of Student’s Academic Performance

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Intelligent Technologies and Applications (INTAP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1198))

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Abstract

Data mining is a technique for extraction of valuable patterns from multiple sources. Data mining plays an important role in marketing, electronic-commerce, business intelligent, healthcare and social network analysis. Advancement in these applications, many researchers show their interest in development of data mining applications in educational context. Educational data mining is a technique defined as a scientific area making inventions within rear types of data that derived from educational surroundings. This Paper reviews different case studies based on data mining educational systems. These systems and mining methods are considered for gathering and analysis of information. Due to huge amount of data in Educational databases, it becomes very challenging to evaluate student performance. Currently in Pakistan, there is dire need to monitor and examine student’s academic progress. There are two main causes of why existing systems were not able to analyze performance of students. First, the study on present evaluation methods is still not satisfactory to analyze the appropriate methods for evaluating the progress and performance of students in institutions of Pakistan. Second is because of absence of investigations on parameters; that effects student’s success in specific courses. Thus, a comprehensive review is proposed on evaluation of student’s performance by using techniques of Data Mining methods to progress student’s achievements. The aim of paper is to improve students’ academic performance by identifying most suitable attributes by using techniques of EDM.

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Authors and Affiliations

  1. Department of Computer Science, Air University Multan Campus, Multan, 6000, Pakistan

    Sadia Ijaz, Tauqeer Safdar & Muhammad Sanaullah

Authors
  1. Sadia Ijaz
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  2. Tauqeer Safdar
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  3. Muhammad Sanaullah
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Corresponding author

Correspondence to Sadia Ijaz .

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Editors and Affiliations

  1. Islamia University of Bahawalpur, Baghdad, Pakistan

    Imran Sarwar Bajwa

  2. Metropolitan University, Belgrade, Serbia

    Tatjana Sibalija

  3. University of Technology Malaysia, Johor Bahru, Malaysia

    Dayang Norhayati Abang Jawawi

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Ijaz, S., Safdar, T., Sanaullah, M. (2020). Educational Data Mining: A Review and Analysis of Student’s Academic Performance. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_44

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  • DOI: https://doi.org/10.1007/978-981-15-5232-8_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5231-1

  • Online ISBN: 978-981-15-5232-8

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