[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118480-en":3,"doc-seo-118480-105":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},118480,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","MAN 51001 Analytics for Strategic Management - Term paper","The term paper analyzes how machine learning can be used in grade prediction for BI Norwegian Business School, with a cost-cutting focus on exam and grading expenses. It explores business objectives, stakeholders, and internal, industry, and academic research, then applies a CRISP-DM style workflow from data collection through model training, evaluation, and deployment planning. The study aims to provide an automation tool that predicts grades accurately while supporting students as a training aid, not replacing human graders.","Handelshoyskolen Bl  \n# MAN 51001 Analytics for Strategic Management\n\nTerm paper 60%-W  \n## Predefinert informasjon\n\nStartdato:  \n06-09-202209:00 CEST  \nTermin:  \n202310  \nSluttdato:  \n04-05-202312:00 CEST  \nNorsk 6-trinns skala(A-F)  \nVurderingsform:  \nEksamensform:  \nP  \n202310l|10055||INO8||W||P  \nFlowkode:  \n(Anonymisert)  \nIntern sensor:  \nNavn;  \nAnne Lenæs,Kenneth Myksvoll Means  \nInformasjon fra deltaker  \nMachine Learning for prediction of grades.  \nTittel:  \nNaun på ueileder*:  \nEspen Andersen and Chandler Johnson  \nInneholder besuarelsen Nei  \nKan besuarelsen Ja  \nkonfidensielt  \noffentliggjsres?:  \nmateriale?:  \nGruppe  \nGruppenaun:  \nGruppenummer:  \nAndre medlemmer i  \ngruppen:  \n(Anonymisert)  \n2  \n# Term paper examination\n\nMachine Learning for prediction of grades.  \nHand-in date:  \n04.05.2023  \nCampus:  \nBI Oslo  \nExamination code and name:MAN 51001 Analytics for Strategic Management  \nProgramme:Executive Master of Management  \n1  \nContent  \nCONTENT…………………………………………………………………………………………………………………………………………I  \nEXECUTIVE SUMMARY………………………………………………………………………………………………………………………IⅢI  \nBUSINESS OBJECTIVE AND CONTEXT…………………………………………………………………………………………1  \nTHE PROJECT IN BRIEF……………………………………………………………………………………………………………………………………………2  \nResearch question………………………………………………………………………………………………………………………………………………2  \n\"WHAT VALUE WILL THIS PROJECT PROVIDE?\"………………………………………………………………………………………………2  \nRECIPIENTS /STAKEHOLDERS OF THE ANALYSIS……………………………………………………………………………………………3  \nINTERNALANALYSES,INDUSTRY RESEARCH, AND ACADEMIC RESEARCH………4  \nINTERNALANALYSIS…………………………………………………………………………………………………………………………………………………5  \nA new exam tool,and implications of ChatGPT……………………………………………………………………………………5  \nINDUSTRY RESEARCH………………………………………………………………………………………………………………………………………………6  \nWhat automation projects exist using grades and exams?……………………………………………………………………7  \nACADEMIC RESEARCH……………………………………………………………………………………………………………………………………………8  \nAutomation is coming! ……………………………………………………………………………………………………………………………………9  \nText is everywhere……………………………………………………………………………………………………………………………………………10  \nANALYSIS…………………………………………………………………………………………………………………………………………………………11  \nCRISP-DM…………………………………………………………………………………………………………………………………………………………………11  \nDATA COLLECTION…………………………………………………………………………………………………………………………………………………12  \nTOOLS AND TECHNIQUE ………………………………………………………………………………………………………………………………………12  \nPython……………………………………………………………………………………………………………………………………………………………………12  \nAuto ML………………………………………………………………………………………………………………………………………………………………12  \nChat GPT………………………………………………………………………………………………………………………………………………………14  \nDATA DESCRIPTION…………………………………………………………………………………………………………………………………………………14  \nDATA PREPARATION………………………………………………………………………………………………………………………………………………15  \nMoDEL TRAINING……………………………………………………………………………………………………………………………………………………17  \nIteration1-Data leak discovery…………………………………………………………………………………………………………………17  \nIteration 2-As good as it gets………………………………………………………………………………………………………………………18  \nIteration 3-Re-evaluating business use case …………………………………………………………………………………………19  \nIteration 4-Importance of data quantity………………………………………………………………………………………………21  \nIteration 5-Audit use case…………………………………………………………………………………………………………………………22  \nIteration 6-Binary classification……………………………………………………………………………………………………………23  \nMoDEL DESCRIPTION………………………………………………………………………………………………………………………………………………25  \nMoDEL EVALUATION………………………………………………………………………………………………………………………………………………27  \nEvaluation by ChatGPT…………………………………………………………………………………………………………………………………27  \nEvaluation metrics……………………………………………………………………………………………………………………………………………28  \nConfusion matrix inspections………………………………………………………………………………………………………………………31  \nWord cloud inspections……………………………………………………………………………………………………………………………………31  \nModel tuning………………………………………………………………………………………………………………………………………………………32  \nMoDEL DEPLOYMENT…………………………………………………………………………………………………………………………………………3  \nCONCLUSIONS AND FURTHER RECOMMENDATIONS…………………………………………………………36  \nBIBLIOGRAPHY………………………………………………………","cbCaiu8PM44e5dev","https://ap.wps.com/l/cbCaiu8PM44e5dev","pdf",17262050,1,95,"English","en",105,"# Predefinert informasjon\n# Term paper examination\n# Executive Summary\n# Business objective and context\n# The project in brief\n## Research question\n## \"WHAT VALUE WILL THIS PROJECT PROVIDE?\"\n## Recipients / stakeholders of the analysis\n# Internal analyses, industry research, and academic research\n## Internal analysis\n## Industry research\n## Academic research\n# Analysis\n## CRISP-DM\n## Data collection\n## Tools and technique\n## Data description\n## Data preparation\n## Model training\n## Model description\n## Model evaluation\n## Model deployment\n# Conclusions and further recommendations\n# Bibliography\n# Appendix A - Data and model info\n# Appendix B - Notebook\n# Appendix C - Exam texts","[{\"question\":\"What problem does the project target for BI Norwegian Business School?\",\"answer\":\"The project targets reducing costs and improving efficiency in exams and grading, while assessing how AI and machine learning could support the process.\"},{\"question\":\"What is the main goal of using machine learning in this project?\",\"answer\":\"To investigate whether a machine learning automation model can predict grades with high accuracy similar to a human grader, while serving as a training tool rather than replacing human graders.\"},{\"question\":\"What modeling workflow and evaluation approach are used?\",\"answer\":\"The paper follows a CRISP-DM oriented process, training multiple iterations and evaluating results with metrics and tools such as confusion matrix inspections and word cloud inspections, including evaluation by ChatGPT.\"}]","MAN 51001 Analytics for Strategic Management - Term paper | 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