[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127614-en":3,"doc-seo-127614-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127614,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Design and development of a students' performance predicting LMS utilizing machine learning based on mental stress level measured through a Bluetooth enabled smart watch","Stress and academic anxiety can impair students’ academic achievement, quality of life, and social behaviour, and prior studies link depression with lower academic performance. This research compiles a university dataset in Punjab, Pakistan to correlate students’ mental stress with academic performance using Perceived Stress Scale (PSS), cognitive assessment, and demographic questions. Machine learning analysis is used to train a smart, low-cost Learning Management System that predicts performance while accounting for mental stress measured via Heart Rate Variability (HRV) from a Bluetooth-enabled smartwatch, reaching 98.1% stress prediction accuracy.","MASTER THESIS  \nTITLE: Design and development of a students’ performance predicting LMS utilizing machine learning based on mental stress level measured through a Bluetooth enabled smart watch  \nMASTER DEGREE: Master's degree in Applied Telecommunications and Engineering Management (MASTEAM)  \nAUTHOR: Syed Haider Ali Kazmi  \nADVISOR: Cristina Barrado, Doctor Faiyaz, Angelica Reyes  \nDATE: June, 5th 2023  \nAbstract  \nStress and academic anxiety problems can negatively impact numerous aspects of students’ lives, resulting in degrading their academic achievement, quality of life, and social behaviour. Various research suggests that depression is associated with lower academic performance of students. The aim of this research is twofold. Firstly, in order to establish a correlation between students’mental stress level and their academic performance, a dataset has been compiled through gathering the data by conducting a survey in a university located in Punjab, Pakistan. The questionnaires were based on measuring the stress level of students using Perceived Stress Scale (PSS) , Cognitive performance assessment scale, in addition to some other demographic questions. Afterwards, this dataset has been analysed utilizing various machine learning algorithms. The second objective was to develop an innovative, affordable and smart performance predicting Learning Management System that takes into account students’mental stress while predicting the students’ performance using machine learning models. The technique that was used for the mental stress measurements of the students was based on a phenomenon known as the Heart Rate Variability (HRV) . A smart watch was utilized to measure the Heart Rate Variability of the students that was used to assess the stress level of students in academics. A Machine Learning (ML) model was trained using various parameters that were derived from the Heart Rate Variability. The original dataset that was used to train the model is known as Swell dataset. The SWELL dataset consists of HRV indices computed from the multimodal SWELL knowledge work dataset for research on stress and user modelling. The ML model effectively made prediction about the stress levels of the students with an accuracy of 98. 1% .  \nCONTENTS  \nCHAPTER 1. INTRODUCTION.......................................................................... 1  \n1.1. Research motivation....................................................................................... 1  \n1.2. Overall aim........................................................................................................ 2  \n1.3. Research agenda............................................................................................. 2  \nCHAPTER 2. THEORETICAL FRAMEWORK................................................... 3  \n2.1. Machine learning .............................................................................................. 3  \n2.1.1. Supervised & Unsupervised machine learning ........................................................3  \n2.1.2. Decision Tree.........................................................................................................4  \n2.1.3. Boosting algorithms ...............................................................................................5  \n2.1.4. Light Gradient Boosting Machine............................................................................6  \n2.2. Background and related work........................................................................ 7  \n2.2.1. Student performance evaluation in educational data mining ...................................7  \n2.2.2. Predicting the academic performance of middle- and high-school students using machine learning algorithms ............................................................................................7  \n2.2.3. Tracking and predicting student performance in degree programs ..........................7  \n2.3. Heart Rate Variability .......................","cbCaiv9LG4O7kuCJ","https://ap.wps.com/l/cbCaiv9LG4O7kuCJ","pdf",1352041,3,1,54,"English","en",105,"# Chapter 1. Introduction\n## Research motivation\n## Overall aim\n## Research agenda\n# Chapter 2. Theoretical Framework\n## Machine learning\n## Background and related work\n## Heart Rate Variability\n# Chapter 3. Methodology\n## Project methodology\n## ML model performance measures\n## Ethical and societal considerations\n# Chapter 4. Dataset Features and Analysis\n## PSS-Score\n## Cognitive performance\n## Gender\n## Age","[{\"question\":\"How is students’ mental stress measured in this study?\",\"answer\":\"Mental stress is measured using Heart Rate Variability (HRV) derived from a Bluetooth-enabled smart watch, which reflects students’ stress levels in academics.\"},{\"question\":\"What data is used to analyze the link between stress and academic performance?\",\"answer\":\"A dataset is compiled using a university survey in Punjab, Pakistan, with questionnaires based on Perceived Stress Scale (PSS), cognitive performance assessment, and additional demographic questions.\"},{\"question\":\"What is the main outcome of the machine learning work?\",\"answer\":\"The study trains machine learning models using HRV-derived parameters (from the SWELL dataset) and uses them to develop an affordable performance-predicting LMS that considers student mental stress, achieving 98.1% stress prediction accuracy.\"}]","Design and development of a students' performance predicting LMS utilizing machine learning based on mental stress level measured through a Bluetooth enabled smart watch | PDF",1785940278,136,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"design-and-development-of-a-students-performance-predicting-lms-utilizing-machine-learning-based-on-mental-stress-level-measured-through-a-bluetooth-enabled-smart-watch","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/design-and-development-of-a-students-performance-predicting-lms-utilizing-machine-learning-based-on-mental-stress-level-measured-through-a-bluetooth-enabled-smart-watch/127614/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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},"How is students’ mental stress measured in this study?","Question",{"text":76,"@type":77},"Mental stress is measured using Heart Rate Variability (HRV) derived from a Bluetooth-enabled smart watch, which reflects students’ stress levels in academics.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data is used to analyze the link between stress and academic performance?",{"text":81,"@type":77},"A dataset is compiled using a university survey in Punjab, Pakistan, with questionnaires based on Perceived Stress Scale (PSS), cognitive performance assessment, and additional demographic questions.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the main outcome of the machine learning work?",{"text":85,"@type":77},"The study trains machine learning models using HRV-derived parameters (from the SWELL dataset) and uses them to develop an affordable performance-predicting LMS that considers student mental stress, achieving 98.1% stress prediction accuracy.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]