[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124415-en":3,"doc-seo-124415-105":30,"detail-sidebar-cat-0-en-105":90},{"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},124415,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",4,"Exam","A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions - Research Summary","Enhancing and preserving standards in higher education depends on timely, reliable assessment and accreditation of higher education institutions. With India’s NAAC framework relying on qualitative and quantitative data and requiring time-consuming human intervention, this work proposes a machine learning prediction model to evaluate HEI performance within a short timeframe. Multiclass label classification predicts Key Indicator qualitative metric scores, while total quantitative and qualitative scores support further classification for accreditation performance.","Siddalingappa, Rashmi ORCID logoORCID: [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)[0001-9786-8436](0001-9786-8436) , P. , Prakash, Gornale, Shivanand S. and Kumar, Satish (2024) A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions.  \nNanotechnology Perceptions, 20 (S14) .  \nDownloaded from: [https://ray.yorksj.ac.uk/id/eprint/12836/](https://ray.yorksj.ac.uk/id/eprint/12836/)  \nThe version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [http://dx.doi.org/10.62441/nano-ntp.vi.3001](http://dx.doi.org/10.62441/nano-ntp.vi.3001)  \nResearch at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repositories Policy Statement  \nRaY  \nResearch at the University of York St John For more information please contact RaY at  \n[ray@yorksj.ac. uk](ray@yorksj.ac. uk)  \nA Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions  \nPrakash. P1, Shivanand S. Gornale1, Rashmi Siddalingappa2, Satish  \nKumar1  \n1Department of Computer Science, School of Mathematics and Computing Sciences Rani Channamma University, Belagavi, Karnataka, India 2Department of Computer Science, Christ University, Bangalore, Karnataka, India  \nThe enhancement and preservation of standard in the higher education are pivotal for the enduring viability of Higher Education Institutes (HEIs) .  \nNational Assessment and Accreditation Council (NAAC) in India introduced anew framework for evaluating HEIs in July 2017 based on qualitative and quantitative data analysis and will be assessed and is carried in two ways Data Validation & Verification (DVV) and the onsite peer team visit. The entire Assessment and Accreditation (A&A) process will take the timeline of six to seven months to complete is time consuming and the human intervention. In this proposed work, a predication model using machine learning techniques is developed to assess the performance of HEIs based on the NAAC Criteria within short timeframe and without human intervention. We have used the Multiclass label classification to predict the Key Indicator Qualitative metric score, and the classification based on the total Quantitative & Qualitative Score.  \nThe study utilized four distinct algorithms of Machine learning (ML) for classification: Naive Bayes (NB), Random Forest (RT), K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) . The SVM classification technique exhibited the highest accuracy at 97%, followed by Random Forest at 94%, among the four classifiers.  \nKeywords: Higher Education Institutions (HEIs), Assessment & Accreditation, NAAC, Machine Learning, Support Vector Machine, Random Forest & KNN.  \n1. INTRODUCTION  \nIn India, the NAAC is the primary regulatory body responsible for maintaining good  \nstandards and excellence in higher education. In response to the suggestions of the National  \nNanotechnology Perceptions 20 No. S14 (2024) 1683-1697  \nPolicy in Education (1986), NAAC was established in 1994. Its main goal is to maintain and improve the quality of higher education while also assessing and accrediting Higher Education Institutions (HEIs) . With its main office located in Bangalore, the NAAC actively takes part in the process used by colleges and institutions across the country to monitor and assess academic performance. A combination of internal and external measures is used by the NAAC to guarantee & improve the quality ofHEIs [19] .  \nNAAC obje","cbCaimDtGbXMKEUJ","https://ap.wps.com/l/cbCaimDtGbXMKEUJ","pdf",1038000,1,16,"English","en",105,"# Introduction\n## NAAC framework and objectives\n## Machine learning approach for HEI assessment\n# Methodology and Algorithms\n## Multiclass classification strategy\n## Classifiers compared: Naive Bayes, Random Forest, KNN, SVM\n# Results and Performance","[{\"question\":\"What problem does the proposed system address in NAAC assessment?\",\"answer\":\"It targets the lengthy, human-intervention-dependent NAAC assessment and accreditation timeline by predicting HEI performance using machine learning within a short timeframe.\"},{\"question\":\"How does the system predict NAAC evaluation metrics?\",\"answer\":\"It uses multiclass label classification to predict Key Indicator qualitative metric scores and classification based on the total quantitative and qualitative score.\"},{\"question\":\"Which machine learning algorithm achieved the best accuracy?\",\"answer\":\"Support Vector Machine (SVM) achieved the highest accuracy at 97%, followed by Random Forest at 94% among the four classifiers compared.\"}]","A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions - 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