PREDICTING STUDENT PERFORMANCE OF DIFFERENT REGIONS OF PUNJAB USING CLASSIFICATION TECHNIQUES

Article Properties
Cite
Garg, Rajni. “PREDICTING STUDENT PERFORMANCE OF DIFFERENT REGIONS OF PUNJAB USING CLASSIFICATION TECHNIQUES”. International Journal of Advanced Research in Computer Science, vol. 9, no. 1, 2018, pp. 236-40, https://doi.org/10.26483/ijarcs.v9i1.5234.
Garg, R. (2018). PREDICTING STUDENT PERFORMANCE OF DIFFERENT REGIONS OF PUNJAB USING CLASSIFICATION TECHNIQUES. International Journal of Advanced Research in Computer Science, 9(1), 236-240. https://doi.org/10.26483/ijarcs.v9i1.5234
Garg, Rajni. “PREDICTING STUDENT PERFORMANCE OF DIFFERENT REGIONS OF PUNJAB USING CLASSIFICATION TECHNIQUES”. International Journal of Advanced Research in Computer Science 9, no. 1 (2018): 236-40. https://doi.org/10.26483/ijarcs.v9i1.5234.
Garg R. PREDICTING STUDENT PERFORMANCE OF DIFFERENT REGIONS OF PUNJAB USING CLASSIFICATION TECHNIQUES. international journal of advanced research in computer science. 2018;9(1):236-40.
Citations
Title Journal Journal Categories Citations Publication Date
Student course grade prediction using the random forest algorithm: Analysis of predictors' importance Trends in Neuroscience and Education 2 2023
Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why IEEE Transactions on Learning Technologies 2023
Early prediction of student performance in CS1 programming courses

 PeerJ Computer Science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2023
Predicting students’ performance in English and Mathematics using data mining techniques Education and Information Technologies
  • Education: Theory and practice of education
  • Education
  • Social Sciences
 3 2022
A machine learning prediction of academic performance of secondary school students using radial basis function neural network Trends in Neuroscience and Education 7 2022
Citations Analysis
Category Category Repetition
Education: Theory and practice of education3
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics2
Science: Mathematics: Instruments and machines: Electronic computers. Computer science2
Technology: Engineering (General). Civil engineering (General)2
Education2
Social Sciences2
Science: Science (General): Cybernetics: Information theory2
Technology: Mechanical engineering and machinery1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Science: Physics1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Geography. Anthropology. Recreation: Environmental sciences1
Medicine: Internal medicine: Special situations and conditions: Industrial medicine. Industrial hygiene1
Medicine: Public aspects of medicine1
Technology: Environmental technology. Sanitary engineering1
Science: Biology (General): Ecology1
Education: Special aspects of education1
Technology: Technology (General): Industrial engineering. Management engineering: Information technology1
The category Education: Theory and practice of education 3 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Predicting academic success in higher education: literature review and best practices and was published in 2020. The most recent citation comes from a 2023 study titled Early prediction of student performance in CS1 programming courses. This article reached its peak citation in 2022, with 4 citations. It has been cited in 10 different journals, 20% of which are open access. Among related journals, the Trends in Neuroscience and Education cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year