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May-2019
eISSN: 2349-5162
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Journal of Emerging Technologies and Innovative Research ( An International Scholarly Open Access Journal, Peer-reviewed, Refereed, Crossref DOI Journal )
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UGC Approved Journal no 63975(19)
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ISSN: 2349-5162 | ESTD Year : 2014 Volume 12 | Issue 10 | October 2025
New UGC Peer-Reviewed Rules
ISSN: 2349-5162 | ESTD Year : 2014 Volume 12 | Issue 10 | October 2025
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UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975
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JETIRBQ06004
Registration ID:
209372
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Title
Validation, Testing and Sensitivity Analysis of Binary Logistic Regression Model for Predicting the Performance of Engineering Students
Abstract
A binary logistic regression (BLR) model was developed to predict the performance of engineering students in university examination. This model shows the mathematical relationship between influencing factors and the performance of engineering students in university examination. Pass/fail result in university examination is taken as performance measure, and personal, pre-admission, institutional and self-learning factors as influencing factors. This BLR model was validated by Artificial Neural Network (ANN) and tested by using newly collected samples. The accuracy of the model was found as 80.95 % which showed a good accuracy for such type of models. The optimum values of each influencing factors were also calculated as 4.01, 3.16, 4.12 and 4.04 for personal, pre-admission, institutional and self-learning factors respectively. In sensitivity analysis, it is observed that personal factors are most sensitive followed by institutional factors whereas pre-admission factors are less sensitive than self-learning factors. This study will help the engineering students to improve their performance in university examination by predicting their probability of passing in advance.
Key Words
Validation, Testing, Sensitivity Analysis, Binary Logistic Regression, Students’ Performance.
Cite This Article
"Validation, Testing and Sensitivity Analysis of Binary Logistic Regression Model for Predicting the Performance of Engineering Students", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.23-30, May-2019, Available :http://www.jetir.org/papers/JETIRBQ06004.pdf
ISSN
2349-5162 | Impact Factor 7.95 Calculate by Google Scholar
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Cite This Article
"Validation, Testing and Sensitivity Analysis of Binary Logistic Regression Model for Predicting the Performance of Engineering Students", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp23-30, May-2019, Available at : http://www.jetir.org/papers/JETIRBQ06004.pdf
Publication Details
Published Paper ID: JETIRBQ06004
Registration ID: 209372
Published In: Volume 6 | Issue 5 | Year May-2019
Page No: 23-30
Country: Bikaner, Rajasthan, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication
Published Paper URL :: https://www.jetir.org/view?paper=JETIRBQ06004
Published Paper PDF: https://www.jetir.org/papers/JETIRBQ06004
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