[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127290-en":3,"doc-seo-127290-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},127290,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Improved Adaptive Learning Systems Using Biometric Data and Machine Learning","The push for digitalization increases demand for skilled workers in computer engineering, programming, and computer science, yet programming education often suffers from high dropout rates. This master’s thesis presents a small-scale experiment that integrates biometric data and machine learning with a traditional adaptive-learning system to support learning programming through Parsons Problems. Eye-tracking and emotion detection data feed trained prediction models, compared against an ELO-rating baseline.","Master’s thesis  \nDag Erik Refshal Gjørvad  \nImproved Adaptive Learning Systems Using  \nBiometric Data and Machine Learning  \nMaster’s thesis in MSIT Supervisor: Kshitij Kshitij July 2024  \nNT NU  \nNorwegian Un iversity of Science and Technology  \nFaculty of Information Techno logy and Electrical Engineering Department of Computer Science  \nDag Erik Refshal Gjørvad  \nImproved Adaptive Learning Systems Using  \nBiometric Data and Machine Learning  \nMaster’s thesis in MSIT Supervisor: Kshitij Kshitij July 2024  \nNorwegian University of Science and Technology  \nFaculty of Information Technology and Electrical Engineering Department of Computer Science  \nImproved Adaptive Learning Systems Using Biometric Data and Machine Learning  \nDag Erik Refshal Gjørvad  \nSpring 2024  \nIT3920-Master in Informatics, Master Thesis Supervisor: Kshitij Kshitij  \n01/07/2024  \nAbstract  \nThe push for digitalization puts a larger demand on the education of skilled workers in computer engineering, programming, and computer science. With programming being a field that struggles with many students dropping out. This thesis presents a small-scale experiment in integrating biometric data and machine learning with a traditional adaptive-learning system to help programming students learn how to program. This is done through Parsons Problems. Parsons Problem requires the user to arrange code blocks in the correct order and is an effective way of teaching relevant programming skills.  \nThis research aimed to evaluate if using biometric data could accelerate the rate at which the adaptive learning system correctly estimated a user's performance. Biometric data was collected using an eye-tracking sensor and emotion detection using a web camera.  \nThe methodology involved a two-step process where an adaptive-learning system was developed for the user to solve Parsons Problems and then this was used to collect the biometric data used in the second step. The second step included training a Simple Neural Net, Support Vector Regression, Gradient Boost and Random Forest model on the collected data. The experiment from the first step was then repeated to see if the prediction models could outperform the ELO-rating model used in the first step.  \nThe findings found that the prediction models did not significantly outperform the ELO-rating model, but that there is potential in combining the prediction models with the ELO-rating model which can help with teaching programming to new students. It also covers a information found on how to better develop machine learning models for use with ELO-rating systems in the future  \nSammendrag  \nDet stadig større kravet om digitalisering i samfunnet setter større krav til utdanning av dyktige arbeidere innen datateknikk, programmering og informatikk. Programvareutvikling er et fagfelt som sliter med at et stort antall studenter faller fra. Denne avhandlingen presenterer et småskala eksperiment i å integrere biometriske data og maskinlæring med et tradisjonelt adaptivt læringssystem for å hjelpe programmeringsstudenter å lære seg hvordan programmere. Dette gjøres gjennom bruk av Parsons Problemer. Parsons Problemer krever at brukeren setter kodeblokker i riktig rekkefølge og er en effektiv måte å lære relevante programmeringsferdigheter på . Forskningen presentert i denne oppgaven hadde som mål å evaluere om det er mulig å bruke biometriske data for å akselerere hastighetensom det adaptive læringssystemet bruker på å estimere en brukers ferdigheter. De biometriske dataene ble samlet inn ved hjelp av en øyesporings sensor og følelsesdeteksjon ved hjelp av et webkamera. Metodikken involverte en to-trinns prosesshvor et adaptivt læringssystem ble utviklet hvor brukere kan løse Parsons Problemer, og deretter ble dette brukt i en serie eksperimenter for å samle inn de biometriske dataene som ble brukt i det andre trinnet. Det andre trinnet inkluderte trening av enenkel nevralt nettverk, støttevektorregresjon, gradient boost og random forest-mode","cbCaikkhJlkx3mRN","https://ap.wps.com/l/cbCaikkhJlkx3mRN","pdf",8873819,1,74,"English","en",105,"# Abstract\n# Sammendrag\n# Preface\n# Acknowledgements\n# Contents\n## 1 Introduction","[{\"question\":\"What problem does the thesis address in programming education?\",\"answer\":\"It addresses the challenge that many students drop out of programming courses, despite growing demand from digitalization.\"},{\"question\":\"How is biometric data collected for the adaptive learning system?\",\"answer\":\"Biometric data is collected using an eye-tracking sensor and emotion detection via a web camera.\"},{\"question\":\"Do the biometric-based prediction models outperform the ELO-rating model?\",\"answer\":\"The results show the prediction models do not significantly outperform the ELO-rating model, though combining them may still help teach new programming students.\"}]","Improved Adaptive Learning Systems Using Biometric Data and Machine Learning | PDF",1785938130,186,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"improved-adaptive-learning-systems-using-biometric-data-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improved-adaptive-learning-systems-using-biometric-data-and-machine-learning/127290/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address in programming education?","Question",{"text":76,"@type":77},"It addresses the challenge that many students drop out of programming courses, despite growing demand from digitalization.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is biometric data collected for the adaptive learning system?",{"text":81,"@type":77},"Biometric data is collected using an eye-tracking sensor and emotion detection via a web camera.",{"name":83,"@type":74,"acceptedAnswer":84},"Do the biometric-based prediction models outperform the ELO-rating model?",{"text":85,"@type":77},"The results show the prediction models do not significantly outperform the ELO-rating model, though combining them may still help teach new programming students.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]