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Published Paper Details:
Paper Title
Explainable Ai For Student Performance Analysis in Online Judge Systems
Authors
Mr.C.Rambabu, Uppara Manvi Sreekanth, Uppara Aravind, Mulla Mohammad Kaif, Saya Karthik
Keywords
Explainable AI (XAI), Online judge Systems (OJ),Educational Data Mining (EDM),Multi Instance Learning (MIL), Machine Learning (ML),Random Forest, Student Performance Analysis, Shapley Additive Explanations (SHAP),AUC Score, Feedback Generation, Programming Education, Cohort Analysis, Personalized Instruction, Adaptive Feedback
Abstract
This study aims to enhance Online Judge (OJ) systems in programming education using Explainable AI (XAI).
Researchers used Educational Data Mining with Multi-Instance Learning and Machine Learning to analyze student submission behavior.
Data from 2,500+ submissions by 90 students (2019-2022) was collected from a Java-based course.
A Random Forest model achieved the highest accuracy (AUC = 0.70).
SHAP explanations were used to provide interpretable feedback for students and instructors.
Early submission (>=7 days before deadline) and frequent attempts (>40) were linked to success.
Assignment difficulty had little effect; student effort and timing mattered more.
Cohort analysis identified late and sparse submitters as high-risk for failure.
The system offers actionable, human-readable feedback to guide learning.
Future work will explore motivational and personality factors to improve predictions
IJCRT's Publication Details
Unique Identification Number - IJCRT2504509
Paper ID - 282090
Page Number(s) - e341-e344
Pubished in - Volume 13 | Issue 4 | April 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Mr.C.Rambabu, Uppara Manvi Sreekanth, Uppara Aravind, Mulla Mohammad Kaif, Saya Karthik, "Explainable Ai For Student Performance Analysis in Online Judge Systems", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 4, pp.e341-e344, April 2025, Available at :http://www.ijcrt.org/papers/IJCRT2504509.pdf
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Volume 14 | Issue 2
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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