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(PDF) Hybrid ML-Based E-Learning Course Recommender
<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="KjmvEoiYW-oSzxbHzoeW78_dB9coEtm5x_SWmKym7Dlo0zZDpdwSzraTtiLsUhIejClOb6XVDJfdBaMuHzjnig" /> <meta name="citation_title" content="Hybrid Recommender System for E-Learning Platforms" /> <meta name="citation_publication_date" content="2023/01/01" /> <meta name="citation_author" content="Grenze International Journal of Engineering and Technology GIJET" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms" /> <meta name="twitter:title" content="Hybrid Recommender System for E-Learning Platforms" /> <meta name="twitter:description" content="Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system" /> <meta name="twitter:image" content="https://0.academia-photos.com/32919427/19089507/19039715/s200_grenze_international_journal_of_engineering_and_technology.gijet.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms" /> <meta property="og:title" content="Hybrid Recommender System for E-Learning Platforms" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system" /> <meta property="article:author" content="https://independent.academia.edu/IdesEditor" /> <meta name="description" content="Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system" /> <title>(PDF) Hybrid ML-Based E-Learning Course Recommender</title> <link rel="canonical" href="https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1739837564000); window.Aedu.timeDifference = new Date().getTime() - 1739837564000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding).","author":[{"@context":"https://schema.org","@type":"Person","name":"Grenze International Journal of Engineering and Technology GIJET","url":"https://independent.academia.edu/IdesEditor"}],"contributor":[],"dateCreated":"2024-05-22","datePublished":"2023-01-01","headline":"Hybrid Recommender System for E-Learning Platforms","image":"https://attachments.academia-assets.com/115146062/thumbnails/1.jpg","inLanguage":"en","keywords":["Computer Science"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://attachments.academia-assets.com/115146062/thumbnails/1.jpg","url":"https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: 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{"work":{"id":119808103,"created_at":"2024-05-22T04:34:14.895-07:00","from_world_paper_id":null,"updated_at":"2025-02-01T03:27:37.774-08:00","_data":{"abstract":"Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding).","ai_title_tag":"Hybrid ML-Based E-Learning Course Recommender","publication_date":"2023,,"},"document_type":"conference_presentation","pre_hit_view_count_baseline":null,"quality":"low","language":"en","title":"Hybrid Recommender System for E-Learning Platforms","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [32919427]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":115146062,"attachmentType":"pdf"}"><img alt="First page of “Hybrid Recommender System for E-Learning Platforms”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/115146062/mini_magick20240802-1-itpsc1.png?1722582666" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Hybrid Recommender System for E-Learning Platforms</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="32919427" href="https://independent.academia.edu/IdesEditor"><img alt="Profile image of Grenze International Journal of Engineering and Technology GIJET" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/32919427/19089507/19039715/s65_grenze_international_journal_of_engineering_and_technology.gijet.jpg" />Grenze International Journal of Engineering and Technology GIJET</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2023</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">6 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 119808103; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=119808103"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding).</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":115146062,"attachmentType":"pdf","workUrl":"https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":115146062,"attachmentType":"pdf","workUrl":"https://www.academia.edu/119808103/Hybrid_Recommender_System_for_E_Learning_Platforms"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Multiple online courses are available on different platform finding appropriate course from this massive available course is difficult for students. Recommender system plays vital role in finding appropriate courses to students. Managing massive amount of information and identifying individual users' choice and behavior has become tedious task nowadays, so the aim of recommender system is to suggest relevant course to student based on user behavior and similarity with another course. Several recommender system techniques are being implemented like content based, collaborative, Knowledge based. This paper aims to build a hybrid approach using collaborative filtering with content base filtering. This system recommendation is based on course description and ratings. Experiments were conducted on real datasets to get the overall performance of proposed system. I. INTRODUCTION With the ever-growing large volume of online information, recommender systems are an efficient strategy to beat such information overload. Recommender systems are the systems that are designed to recommend things to the user supported various factors. Companies like YouTube, Netflix, Amazon, etc. use recommender systems to help their users to recommend the correct product, video or movies for them. Advances in technology has changed the way of education. Massive Open Online Courses (MOOCs) are capable of providing several learners to access courses over the web. Recommender System (RS) is computerized system that suggest/recommend item to user. The number of MOOCs and the number of students registered in MOOCs are growing per annum. In 2018, more than 900 universities were offering MOOCs with 11,400 courses available, and around 101 million students had registered in them (Shah, 2018), providing learners with a good sort of choices. With such a high number of courses available, learners now face the matter of choosing courses without being overwhelmed. With the rise in e-commerce and online business, the number of users interested in online Web services has increased. Both MOOC providers and online businesses advertise their courses and services while learners look for courses that match their interests and needs. In these situations, recommender systems play a crucial role, and have attracted the attention of researchers. Recommender systems are algorithms and techniques that, based on their preferences, suggest matching and related courses or services to the learner, knowledge about which comes from learner profiles and systems-gathered histories. Recommender systems help MOOC providers grow and learners find more appropriate and customized services based on their personalities and interests. Recommender systems discover patterns in considerable datasets to find out preferences of different users and predict items that correlate to their needs. Recommender systems is divided into two broad categories: collaborative filtering recommender systems and content-based recommender systems. Collaborative filtering recommender systems perform recommendations on users who have had similar taste in the past will make similar choices in the future. Content based recommender systems consider the profile of users and items. The online course recommendation systems suggest to the students the best courses in which they are interested. This paper presents a recommendation methodology that recommends courses to students based on similarity between courses taken by the target student and other students. It aims to provide an effective course recommendation using multiple techniques. The students will be clustered into groups based on traditional data-mining (DM) techniques will to Collaborative filtering using knowledgebase.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Automated Online Course Recommendation System using Collaborative Filtering","attachmentId":65938163,"attachmentType":"pdf","work_url":"https://www.academia.edu/45425412/Automated_Online_Course_Recommendation_System_using_Collaborative_Filtering","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/45425412/Automated_Online_Course_Recommendation_System_using_Collaborative_Filtering"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="96652003" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/96652003/A_Comprehensive_Review_of_Relevant_Techniques_used_in_Course_Recommendation_System">A Comprehensive Review of Relevant Techniques used in Course Recommendation System</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Through a variety of internet venues, there has been a significant increase in online learning tools in recent years, and COVID lockdown was the cherry on top, making us more web friendly for learning things through the web, therefore defining a course recommendation system has never been more important. Course recommendation systems try to simplify the difficulty of identifying relevant courses based on ranking and set criteria. Despite all of the advantages, we encounter several difficulties and complexities, such as accuracy, time consumption, and insufficient data. This research examines online recommendation systems using a variety of methods, including Content-Based, Collaborative Filtering, Knowledge-Based, and Hybrid Systems. Datasets, methods, evaluation, and outputs were discovered to be necessary components during the research. And the proposed way of recommendation system in this paper will be highly beneficial in recommending courses to students.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Comprehensive Review of Relevant Techniques used in Course Recommendation System","attachmentId":98491042,"attachmentType":"pdf","work_url":"https://www.academia.edu/96652003/A_Comprehensive_Review_of_Relevant_Techniques_used_in_Course_Recommendation_System","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/96652003/A_Comprehensive_Review_of_Relevant_Techniques_used_in_Course_Recommendation_System"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="52631287" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52631287/Best_Combination_of_Machine_Learning_Algorithms_for_Course_Recommendation_System_in_E_learning">Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9855789" href="https://ucsd.academia.edu/SunitaDolAher">Sunita Dol Aher</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Data Mining is the extraction of hidden predictive information from large database which can be used in various commercial applications like bioinformatics, Ecommerce etc. Association Rule, classification and clustering are three different algorithms in data mining. Course Recommender System plays an important role in identifying the behavior of students interested in particular set of courses. We collect the data regarding the course enrollment for specific set of data. For collecting this data, we use the learning management system like Moodle. After collecting the data, we apply the different combination of data mining algorithm like classification & association rule algorithm, clustering & association rule algorithm, association rule mining in classified & clustered data, combining clustering & classification algorithm in association rule algorithms or simply the association rule algorithm. Here in this paper we use ADTree classification algorithm, Simple K-means Algorithm & Apriori Association Rule algorithm as different machine learning algorithm. So we propose the five different methods to find the best combination of algorithm in recommending the courses to students in E-learning. We compare the result of this combined approach as well as only the association rule algorithm & present the best combination of algorithm for recommendation of courses in E-learning according to our simulation.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning","attachmentId":69804387,"attachmentType":"pdf","work_url":"https://www.academia.edu/52631287/Best_Combination_of_Machine_Learning_Algorithms_for_Course_Recommendation_System_in_E_learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/52631287/Best_Combination_of_Machine_Learning_Algorithms_for_Course_Recommendation_System_in_E_learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="121063582" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/121063582/Course_Recommendation_System_for_E_Learning">Course Recommendation System for E-Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="132646369" href="https://independent.academia.edu/PakshalShah11">Pakshal Shah</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Zenodo (CERN European Organization for Nuclear Research), 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">Since the pandemic of 2020, Online learning platforms have boomed. People worldwide are eager to learn and explore new things during their free time. Also, with this emerging world of technologies, contents are available online for free but present in huge chunks. This freely available content is cluttered, due to which many people face problems in reaching their goals with proper guidance free of cost. Speaking of E-Learning, it is mainly focused on two students, i.e., a) Traditional people who only gain knowledge before jumping to professional life, and the other student is b) Workers who have prior knowledge but need to improve what they already have to keep up with the job requirement, this learning can be called lifelong learning. There are several resources for learning as a result of technological innovation. different sources for learning, i.e., For notes, there are many resources and learning from video sources there are countless numbers of video content which leads users to churn from one source to another and thus leads to distraction from goal without any proper guidance. To overcome this, we build a course recommendation system that will help learners choose the right a ?.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Course Recommendation System for E-Learning","attachmentId":116040481,"attachmentType":"pdf","work_url":"https://www.academia.edu/121063582/Course_Recommendation_System_for_E_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/121063582/Course_Recommendation_System_for_E_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="82618217" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/82618217/Hybrid_content_and_collaborative_filtering_based_recommendation_system_for_e_learning_platforms">Hybrid content and collaborative filtering based recommendation system for e-learning platforms</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="171721299" href="https://independent.academia.edu/beeiiaes">beei iaes</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Bulletin of Electrical Engineering and Informatics , 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of elearning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to generalize across content sources and content types. In this work, we present a hybrid recommendation system that combines the desirable characteristics of collaborative filtering, as well as from content-based filtering, for the task of recommending course content/curriculum to users of an e-learning system. Our recommendation easily incorporates changing user profiles (as learners step through course content) and also generalize across content sources (courses taught by various departments) and types. We apply our system on a real dataset comprising 111 students organized into interdisciplinary groups. Our results showcase the clear benefits that our hybrid recommendation system enjoys, showing more than 30 percentage points of improvement over conventional filtering techniques.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hybrid content and collaborative filtering based recommendation system for e-learning platforms","attachmentId":88265194,"attachmentType":"pdf","work_url":"https://www.academia.edu/82618217/Hybrid_content_and_collaborative_filtering_based_recommendation_system_for_e_learning_platforms","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/82618217/Hybrid_content_and_collaborative_filtering_based_recommendation_system_for_e_learning_platforms"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="39502272" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/39502272/A_Personalized_Course_Recommender_System_for_E_Learning">A Personalized Course Recommender System for E-Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2688209" href="https://independent.academia.edu/SenthilKumar32">WARSE The World Academy of Research in Science and Engineering</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Networks and Systems, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, the internet has witnessed an aggressive growth in the amount of learning resources. This explosion of learning resources on the internet results in expanded interest for online learning resources by learners in e-learning environment. With this expansion of online learning resources, learners are experiencing challenges in deciding learning resources that are valuable and significant to their learning needs. Recommender systems can overcome this issue by filtering out inappropriate learning resources and automatically recommending suitable resources to the learners according to their interests. In this paper we are focusing on a course recommender system in an e-learning platform which tries to intelligently recommend courses to the learners based on their interest. This recommendation approach is used to provide learners some suggestions when they have trouble in choosing correct courses. It also allows us to study the behavior of learner regarding their course selection and suggests the best combination of courses in which the learners are interested.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Personalized Course Recommender System for E-Learning","attachmentId":59650343,"attachmentType":"pdf","work_url":"https://www.academia.edu/39502272/A_Personalized_Course_Recommender_System_for_E_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/39502272/A_Personalized_Course_Recommender_System_for_E_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="99021813" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99021813/PCRS_Personalized_Course_Recommender_System_Based_on_Hybrid_Approach">PCRS: Personalized Course Recommender System Based on Hybrid Approach</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="145096201" href="https://independent.academia.edu/LeemaAnny">Anny Leema</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Procedia Computer Science, 2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PCRS: Personalized Course Recommender System Based on Hybrid Approach","attachmentId":100218483,"attachmentType":"pdf","work_url":"https://www.academia.edu/99021813/PCRS_Personalized_Course_Recommender_System_Based_on_Hybrid_Approach","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/99021813/PCRS_Personalized_Course_Recommender_System_Based_on_Hybrid_Approach"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="52136622" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52136622/Toward_a_Hybrid_Recommender_System_for_E_learning_Personnalization_Based_on_Data_Mining_Techniques">Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="39210315" href="https://independent.academia.edu/MKoutheairKHRIBI">M. Koutheair KHRIBI</a></div><p class="ds-related-work--metadata ds2-5-body-xs">JOIV : International Journal on Informatics Visualization</p><p class="ds-related-work--abstract ds2-5-body-sm">Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques","attachmentId":69540516,"attachmentType":"pdf","work_url":"https://www.academia.edu/52136622/Toward_a_Hybrid_Recommender_System_for_E_learning_Personnalization_Based_on_Data_Mining_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/52136622/Toward_a_Hybrid_Recommender_System_for_E_learning_Personnalization_Based_on_Data_Mining_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="101786174" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/101786174/Development_of_a_Course_Recommender_System_for_Students">Development of a Course Recommender System for Students</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="269723020" href="https://independent.academia.edu/HARISHYADAV170">HARISH YADAV</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Students at the university have an information need to find the courses of their interest. The current university registration portals do not fulfill this information need completely. We have proposed the development of a recommender system which can take a course name and based on the description of that course recommend other courses to students. The recommended course list could help save time and effort for students registering for courses. The proposed system was trained with sample data collected from the course catalog of the University of North Carolina at Chapel Hill. We tested the recommender system with different courses as input and evaluated the resulting recommended courses.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Development of a Course Recommender System for Students","attachmentId":102230479,"attachmentType":"pdf","work_url":"https://www.academia.edu/101786174/Development_of_a_Course_Recommender_System_for_Students","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/101786174/Development_of_a_Course_Recommender_System_for_Students"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="65281762" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/65281762/Recommender_System_Collaborative_Filtering_of_e_Learning_Resources">Recommender System: Collaborative Filtering of e-Learning Resources</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="56678832" href="https://independent.academia.edu/BabaMBAYE">Baba MBAYE</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">The significant amount of information available on the web has led to difficulties for the learner to find useful information and relevant resources to carry out their training. The recommender systems have achieved significant success in the area of e-commerce, they still have difficulties in formulating relevant recommendations on e-learning resources because of the different characteristics of learners. Most of the existing recommendation techniques do not take these characteristics into account. This problem can be mitigated by including learner information in the referral process. Currently many recommendation techniques have cold start problems and classification problems. In this paper, we propose an ontology-based collaborative filtering recommendation system for recommending learners&#39; online learning resources based on a decision algorithm (DA). In our approach, ontology is used to model and represent domain knowledge about the learner and learning resources. Our approa...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Recommender System: Collaborative Filtering of e-Learning Resources","attachmentId":76950538,"attachmentType":"pdf","work_url":"https://www.academia.edu/65281762/Recommender_System_Collaborative_Filtering_of_e_Learning_Resources","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/65281762/Recommender_System_Collaborative_Filtering_of_e_Learning_Resources"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":115146062,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":115146062,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_115146062" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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