[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119656-en":3,"doc-seo-119656-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119656,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","A Comparative Study of Behaviour Predictors for School Students in Indore Using Machine Learning Algorithms - Abstract","Predicting student academic performance has become a core focus in educational data mining, where machine learning supports early intervention and better decision-making. This study applies classification models to forecast student success and behavioural outcomes, aiming to strengthen academic support systems and lower dropout risk. Two student-information datasets are used, and three boosting-focused machine learning approaches—XGBoost, AdaBoost, and an Artificial Neural Network—are implemented after feature engineering to refine input variables. Results indicate boosting methods can deliver strong predictive accuracy, with XGBoost and AdaBoost reaching about 88%, while the DenseNet-based model attains roughly 49%.","\"A Comparative Study of Behaviour Predictors for School Students in Indore Using Machine  \nLearning Algorithms\" .  \nYougal Kishore Sharma *1, Dr. Arpana Bharani *2  \n*1 (Research Scholars, Department of Computer Science, Dr. A. P. J. Abdul Kalam University, Indore)  \n*2 (Research Guide, Department of Computer Science, Dr. A.P.J. Abdul Kalam University, Indore)  \nABSTRACT  \nPredicting student academic performance has become a key area in educational data mining, with machine learning techniques offering powerful tools for early intervention and decision-making. This study explores the application of classification models to forecast student success and behavioural outcomes, with the goal of improving academic support systems and reducing dropout rates. Two distinct datasets of student information were utilized, and three boosting-based machine learning algorithms-XGBoost, AdaBoost, and an Artificial Neural Network (DenseNet) -were implemented. Feature engineering techniques were applied to optimize input variables and enhance model effectiveness.  \nThe results demonstrate that it is feasible to predict student behaviour and academic performance with significant accuracy using machine learning models. Among the evaluated methods, XGBoost and AdaBoost achieved the best predictive performance with an accuracy rate of approximately 88%. Conversely, the DenseNet-based neural network model produced the lowest accuracy, around 49% . These findings underscore the effectiveness of boosting methods for educational prediction tasks and highlight the role of machine learning as a practical approach to advancing educational research and institutional planning.  \nKeywords: student performance prediction, educational data mining, machine learning, boosting algorithms, XGBoost, AdaBoost, neural networks, feature engineering.  \nINTRODUCTION  \nPredicting student behaviour and academic performance using machine learning has emerged as a powerful approach for educational research and institutional planning. By analyzing data from various sources such as academic records, attendance logs, survey responses, and even digital footprints, machine learning algorithms can uncover hidden patterns, forecast outcomes, and generate insights that help institutions make informed decisions. These predictions allow educators to identify at-risk learners, address individual weaknesses, and provide targeted interventions that enhance both student success and institutional performance.  \nEducation plays a crucial role in national development and constitutes a key driver of long-term socio-economic growth. However, persistent challenges such as student dropouts and failure in key subjects significantly impact literacy levels and overall academic achievement. Since educational institutions maintain extensive records of students, large databases containing academic and behavioural information offer potential opportunities for  \nextracting actionable knowledge. Questions such as which students perform equally across subjects, what factors influence academic performance, which courses are most attractive to students, and whether performance can be reliably predicted can be addressed using data mining and machine learning. The application of these techniques provides valuable insights by identifying patterns and trends, thereby optimizing institutional success rates and educational planning.  \nData mining, particularly classification methods, plays a central role in predicting student behaviour. Classification, a supervised learning process, categorizes datasets into predefined labels using algorithms such as Decision Trees, Support Vector Machines (SVM), Naïve Bayes, Random Forests, and logistic regression. More advanced approaches, including boosting algorithms and neural networks, further extend predictive capabilities. The process typically involves data collection from multiple sources, pre-processing and feature engineering to extract meaningful attributes, and the","cbCaipJQxYTZmmdR","https://ap.wps.com/l/cbCaipJQxYTZmmdR","pdf",637654,1,"English","en",105,"# Abstract\n# Introduction\n# Research Gap\n# Literature Review","[{\"question\":\"What problem does the study address in educational data mining?\",\"answer\":\"The study addresses predicting student academic performance and behavioural outcomes to support early intervention and reduce dropout rates.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"XGBoost, AdaBoost, and a DenseNet-based artificial neural network are evaluated using feature-engineered inputs.\"},{\"question\":\"What were the key findings about predictive accuracy?\",\"answer\":\"XGBoost and AdaBoost achieved the best performance at around 88% accuracy, while the DenseNet-based model produced the lowest accuracy at about 49%.\"}]","A Comparative Study of Behaviour Predictors for School Students in Indore Using Machine Learning Algorithms - 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