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Enhancing the capabilities of Student Result Prediction System

Published: 04 March 2016 Publication History

Abstract

In the age of information and communication technology, technology is being used in every domain. Education is the integral part of our society consisting of the teaching, learning and evaluation process. Information and communication technologies are being used for the purpose of e --learning, measuring student's learning, course design, student performance evaluation e.t.c. Using machine learning techniques performance of the students has been studied and useful results have been derived. Predicting the performance of a student accurately in the upcoming exam is of extreme significance. Every machine learning tool heavily depends upon the input data. Studying and implementing the elaborate feature set for students has improved the accuracy of the prediction system. Further use of preprocessing techniques along with classification algorithms has significantly improved the results of the prediction system.

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ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 March 2016

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Author Tags

  1. SMOTE
  2. class imbalance
  3. data preprocessing
  4. prediction
  5. student performance

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ICTCS '16

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Overall Acceptance Rate 97 of 270 submissions, 36%

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  • (2024)The Impact of Artificial Intelligence and Machine Learning in Medical ImagingEnhancing Medical Imaging with Emerging Technologies10.4018/979-8-3693-5261-8.ch014(221-249)Online publication date: 14-Jun-2024
  • (2024)DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUESOCENA WYDAJNOŚCI AKADEMICKIEJ STUDENTÓW W NAUCE ONLINE ZA POMOCĄ TECHNIK UCZENIA MASZYNOWEGOInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska10.35784/iapgos.617314:3(109-117)Online publication date: 30-Sep-2024
  • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
  • (2024)A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning TechniquesWireless Personal Communications10.1007/s11277-023-10838-x133:3(1643-1674)Online publication date: 26-Jan-2024
  • (2023)A Comparative Analysis of Nature-Inspired Feature Selection Algorithms in Predicting Student Performance2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00256(1692-1696)Online publication date: 15-Dec-2023
  • (2023)Genetic Algorithm-Based Approach for Predicting Student Academic Success2023 24th International Arab Conference on Information Technology (ACIT)10.1109/ACIT58888.2023.10453789(1-5)Online publication date: 6-Dec-2023
  • (2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122(106071)Online publication date: Jun-2023
  • (2023)A Survey of Machine Learning for Assessing and Estimating Student PerformanceProceedings of International Conference on Recent Innovations in Computing10.1007/978-981-19-9876-8_48(633-648)Online publication date: 3-May-2023
  • (2023)Rice Yield Estimation Using Deep LearningInnovations in Intelligent Computing and Communication10.1007/978-3-031-23233-6_28(379-388)Online publication date: 1-Jan-2023
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