Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Green IT and Environmental Sustainability Issues Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Student’s Performance Evaluation of an Institute Using Various Classification Algorithms

Authors : Shiwani Rana, Roopali Garg

Published in: Information and Communication Technology for Sustainable Development

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Machine learning is the field of computer science that learns from data by studying algorithms and their constructions. The student’s performance based on slow learner method plays a significant role in nourishing the skills of a student with slow learning ability. The performance of the students of Digital Electronics of University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh is calculated by applying two important classification algorithms (Supervised Learning): Multilayer Perceptron and Naïve Bayes. Further, a comparison between these classification algorithms is done using WEKA Tool. The accuracy of grades prediction is calculated with these classification algorithms and a graphical explanation is presented for the BE (Information Technology) third semester students.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A New Approach Toward Sorting Technique: Dual-Sort Extraction Technique (DSET)
next chapter Speaker Identification in a Multi-speaker Environment
Appendix
Available only for authorised users
Literature
1.
Khan, Irfan Ajmal, and Jin Tak Choi. “An Application of Educational Data Mining (EDM) Technique for Scholarship Prediction.” International Journal of Software Engineering and Its Applications 8, no. 12 (2014): 31–42.
2.
Goyal, Monika, and Rajan Vohra. “Applications of data mining in higher education.” International journal of computer science 9, no. 2 (2012): 113.
3.
Ayodele, Taiwo Oladipupo. Types of machine learning algorithms. INTECH Open Access Publisher, 2010.
4.
Kaur, Gurneet, and Er Neelam Oberai. “NAIVE BAYES CLASSIFIER WITH MODIFIED SMOOTHING TECHNIQUES FOR BETTER SPAM CLASSIFICATION.” (2014).
5.
Fukumizu, Kenji. “Active learning in multilayer perceptrons.” Advances in Neural Information Processing Systems (1996): 295–301.
6.
Ahmed, Abeer Badr El Din, and Ibrahim Sayed Elaraby. “Data Mining: A prediction for Student’s Performance Using Classification Method.” World Journal of Computer Application and Technology 2, no. 2 (2014): 43–47.
7.
Kumar, Vaneet, and Vinod Sharma. “Student‟s Examination Result Mining: A Predictive Approach.” International Journal of Advanced Computer Science and Applications (IJSER) 3, no. 11 (2012).
8.
Mohammad, Thakaa Z., and Abeer M. Mahmoud. “Clustering of Slow Learners Behavior for Discovery of Optimal Patterns of Learning.”LITERATURES 5, no. 11 (2014).
9.
Anuradha, C., and T. Velmurugan. “A data mining based survey on student performance evaluation system.” In Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on, pp. 1–4. IEEE, 2014.
10.
Bydovska, Hana, and Lubomir Popelinsky. “Predicting Student Performance in Higher Education.” In Database and Expert Systems Applications (DEXA), 2013 24th International Workshop on, pp. 141–145. IEEE, 2013.
11.
de Morais, Alana M., Joseana MFR Araujo, and Evandro B. Costa. “Monitoring student performance using data clustering and predictive modelling.” In Frontiers in Education Conference (FIE), 2014 IEEE, pp. 1–8. IEEE, 2014.
12.
Singh, Sushil, and Sunil Pranit Lal. “Educational courseware evaluation using Machine Learning techniques.” In e-Learning, e-Management and e-Services (IC3e), 2013 IEEE Conference on, pp. 73–78. IEEE, 2013.
13.
Lopez, Manuel Ignacio, J. M. Luna, C. Romero, and S. Ventura. “Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums.” International Educational Data Mining Society (2012).
14.
Borkar, Suchita, and K. Rajeswari. “Attributes Selection for Predicting Students’ Academic Performance using Education Data Mining and Artificial Neural Network.” International Journal of Computer Applications 86, no. 10 (2014).
15.
Pradeep, Anjana, Smija Das, and Jubilant J. Kizhekkethottam. “Students dropout factor prediction using EDM techniques.” In Soft-Computing and Networks Security (ICSNS), 2015 International Conference on, pp. 1–7. IEEE, 2015.
Metadata
Title
Student’s Performance Evaluation of an Institute Using Various Classification Algorithms
Authors
Shiwani Rana
Roopali Garg
Copyright Year
2018
Publisher
Springer Singapore
Book
Information and Communication Technology for Sustainable Development

Print ISBN: 978-981-10-3919-5

Electronic ISBN: 978-981-10-3920-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-3920-1_23

Premium Partner

    Image Credits