Enabling data mining of handwritten coursework

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Stahovich, Thomas F., and Hanlung Lin. “Enabling Data Mining of Handwritten Coursework”. Computers &Amp; Graphics, vol. 57, 2016, pp. 31-45, https://doi.org/10.1016/j.cag.2016.01.002.
Stahovich, T. F., & Lin, H. (2016). Enabling data mining of handwritten coursework. Computers &Amp; Graphics, 57, 31-45. https://doi.org/10.1016/j.cag.2016.01.002
Stahovich, Thomas F., and Hanlung Lin. “Enabling Data Mining of Handwritten Coursework”. Computers &Amp; Graphics 57 (2016): 31-45. https://doi.org/10.1016/j.cag.2016.01.002.
Stahovich TF, Lin H. Enabling data mining of handwritten coursework. Computers & Graphics. 2016;57:31-45.
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Citations
Title Journal Journal Categories Citations Publication Date
Systematic Review and Analysis of EDM for Predicting the Academic Performance of Students Journal of The Institution of Engineers (India): Series B 2024
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  • Technology: Mechanical engineering and machinery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
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  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
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  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
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Data Mining Studies in Education: Literature Review For The Years 2014-2020

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Using Lexical Properties of Handwritten Equations to Estimate the Correctness of Students’ Solutions to Engineering Problems International Journal of Artificial Intelligence in Education
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 1 2019
Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science2
Technology: Mechanical engineering and machinery1
Technology: Engineering (General). Civil engineering (General)1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Using Lexical Properties of Handwritten Equations to Estimate the Correctness of Students’ Solutions to Engineering Problems and was published in 2019. The most recent citation comes from a 2024 study titled Systematic Review and Analysis of EDM for Predicting the Academic Performance of Students. This article reached its peak citation in 2024, with 1 citations. It has been cited in 4 different journals. Among related journals, the Journal of The Institution of Engineers (India): Series B cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year