[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118464-en":3,"doc-seo-118464-105":30,"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118464,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting of Academic Achievement of Student Using Machine Learning Model - Research Report","Predicting university student graduation supports both learners and higher education institutions by enabling well-informed choices about academic and career paths. It also allows institutions to proactively identify students unlikely to graduate and provide tailored interventions to improve outcomes. Machine learning leverages large datasets of student performance to learn patterns for future prediction, with reported accuracy reaching around 90% in related studies. Despite extensive systematic literature reviews, limitations remain in model and algorithm coverage, dataset scope, synonym coverage in keywords, and data collection transparency.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \nPredicting of Academic Achievement of Student Using Machine Learning Model  \nMoola Koushik, Koduri Bhava Priya, Kshatriya Vaishnavi, Kella Sai Ganesh Pavan Kumar  \nUG Scholar, Dept. of Computer Science Engineering (Artificial Intelligence and Data Science), Satya Institute of  \nTechnology and Management, Vizianagaram, India  \nRamarapu Bangari  \nAssistant Professor, Dept. of Computer Science Engineering (Artificial Intelligence and Data Science), Satya Institute  \nof Technology and Management, Vizianagaram, India  \nABSTRACT: Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, andinstitutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many systematic literature reviews (SLRs) have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency.  \nKEYWORDS: Datamining, Educational analysis, systematic literature review, machine learning  \nI. INTRODUCTION  \nThe diversity and complexity of today's world have made higher education essential, and as a result, the body of scientific literature devoted to forecasting academic achievement or the likelihood of student dropout has grown significantly [1] . Higher education institutions' traditional function of disseminating knowledge has evolved; the training of skilled professionals and innovation in new knowledge, particularly with the rise of artificial intelligence [2], have led to many of them interacting with various facets of society. Actually, the main goals of its academic and cultural activities are to raise the standard of knowledge in society through teaching, research, and the capacity to share and transmit this information. In order to produce competitive professionals in society, they play a crucial role in imparting information, skills, and values to pupils. Since HEIs must carry on the job started and further expand their engagement, relevance, and service capacity in connection to the social, cultural, and economic context, guiding students towards academic achievement is therefore transcendental [3].  \nAs a result, educational managers are interested in using historical data on students who have successfully finished their university education to forecast academic achievement. This is because it enables them to make better judgements and develop machine learning alternatives for improvement. Therefore, developments in machine learning methods and other fields of research are forerunners of educational data mining. The graduation rate, which is the total number of students who graduate out of all the students who enrol, is a statistical indicator of students' academic performance in higher education. Indeed, by examining both endogenous and exogenous elements in the student environment, it is feasible to consider student achievement in a broader sense [4]. As a result, the ongoing desire to improve students' academic performance has prompted m","cbCaibkLrbnUuiBC","https://ap.wps.com/l/cbCaibkLrbnUuiBC","pdf",2217158,1,9,"English","en",105,"# Abstract\n# Keywords\n# I. Introduction\n## Higher education goals and the need for prediction\n## Educational data mining and graduation rate as performance indicator\n## Prior research on dropout and academic achievement\n# II. Related Work (supervised and unsupervised approaches)","[{\"question\":\"How does predicting university student graduation benefit students and institutions?\",\"answer\":\"It helps students make informed academic and career decisions, while enabling institutions to identify students at risk of not graduating and offer targeted support.\"},{\"question\":\"What role does machine learning play in this prediction task?\",\"answer\":\"Machine learning trains on large datasets of student academic performance to learn patterns that can forecast future outcomes such as graduation likelihood.\"},{\"question\":\"What limitations are noted in existing systematic literature reviews?\",\"answer\":\"They often lack detailed discussion of predictive models and algorithms, have limited coverage of applied algorithms and datasets, use keyword selections that miss relevant synonyms, and provide less transparency about data collection.\"}]","Predicting of Academic Achievement of Student Using Machine Learning Model - 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