[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-601593-105":59,"doc-detail-601593-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","comparing-strategies-may-i-suggest-comparing-three-ple-recommender-strategies","Comparing Strategies - May I Suggest? Comparing Three PLE Recommender Strategies","","Personal learning environment (PLE) solutions empower learners to design ICT- and web-based environments that combine content, people, and applications for varied learning contexts. Recommender systems can strengthen these PLE activities by helping learners discover relevant materials, connect with peers sharing learning interests, or find experts on specific topics. This paper investigates how recommender technology can be utilized in PLEs and proposes three recommendation strategies, comparing their strengths and weaknesses across educational scenarios.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparing-strategies-may-i-suggest-comparing-three-ple-recommender-strategies/601593/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparing-strategies-may-i-suggest-comparing-three-ple-recommender-strategies/601593.png","ImageObject",300,407,{"name":92,"@type":93},"anakgang17","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem do the authors address in this paper?","Question",{"text":112,"@type":113},"The paper examines how recommender technology can be used to support personal learning environments (PLEs). It focuses on generating and providing recommendations that help learners use PLE technology effectively.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are recommender systems considered useful for PLE-based learning?",{"text":117,"@type":113},"Recommendations can support learners by helping them discover relevant content, peers with similar learning interests, and experts on specific topics. This complements the learner-centric nature of PLEs.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the paper structured regarding the recommendation approaches?",{"text":121,"@type":113},"After introducing PLEs and related work, the paper describes three recommender approaches developed in the ROLE project, followed by a discussion of benefits and limitations for applying each approach in PLEs, then concludes with future work.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},601593,1792192313,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090883568,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Comparing Strategies  \nMay I Suggest? Comparing Three PLE Recommender Strategies  \nFelix Mödritscher  \n[felix.modritscher@wu.ac.at](felix.modritscher@wu.ac.at)  \nBarbara Krumay  \n[barbara.krumay@wu.ac.at](barbara.krumay@wu.ac.at)  \nVienna University of Economics and Business, Austria  \nSandy El Helou  \n[sandy.elhelou@epfl.ch](sandy.elhelou@epfl.ch)  \nDenis Gillet  \n[denis.gillet@epfl.ch](denis.gillet@epfl.ch)  \nEcole Polytechnique Fédérale de Lausanne (EPFL), Switzerland  \nAlexander Nussbaumer  \n[alexander.nussbaumer@tugraz.at](alexander.nussbaumer@tugraz.at)  \nDietrich Albert  \n[dietrich.albert@tugraz.at](dietrich.albert@tugraz.at)  \nGraz University of Technology, Asutria  \nIngo Dahn  \n[dahn@uni-koblenz.de](dahn@uni-koblenz.de)  \nUniversity of Koblenz-Landau, Germany  \nCarsten Ullrich  \n[ullrich_c@sjtu.edu.cn](ullrich_c@sjtu.edu.cn)  \nShanghai Jiao Tong University, China  \n1  \nF.Mödritscher, B.Krumay, S. El Helou, D. Gillet, A. Nussbaumer, D. Albert, I. Dahn and C. Ulrich  \nDigital Education Review-[http://greav.ub.edu/der/](http://greav.ub.edu/der/)  \nAbstract  \nPersonal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic . In this paper we examine the utilization of recommender technology for PLEs . However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners . Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general.  \n.  \nKeywords  \nPersonal learning environments; Recommender technology; Federated search;  \ncollaborative recommendations; Community-based recommendations; Psycho  \npedagogical recommender; Technology review .  \nI. Introduction  \nOver the last decades, recommender systems have been successfully applied in various areas, like online retailing (cf. Amazon) or social networking (cf. Facebook) . Due to the success of this kind of technology, research on technology-enhanced learning (TEL) has started to deal with recommender strategies for learning, as documented by workshop proceedings (Manouselis et al., 2010) and special issues in journals (Santos and Boticario, 2011) . Addressing more learner-centric TEL developments, recommendations seem to be a powerful tool for personal learning environment (PLE) solutions (Mödritscher, 2010) .  \nUnlike traditional LMS (Learning Management Systems) where content is predefined, PLEs are based on “soft” context boundaries (Wilson et al., 2007), with resources and apps being added at run time. In such “open corpus” environments (Brusilovsky and Henze, 2007), personalized recommendations give learners the opportunity to take the best of an environment where shared content differs in quality, target audience, subject matter, and is constantly expanded, annotated, and repurposed (Downes, 2010) .  \nAs being addressed within the EU project ‘ROLE’ (abbreviation for ‘Responsive Open Learning Environments’, cf. [http://www.role‐project.eu](http://www.role‐project.eu)), this paper deals with the generation and provision of recommendations which should support learners in using PLE technology. Such recommendations could comprise artifacts, peers or experts, pre-defined PLE designs as well as shared experiences which are helpful in designing or making use of PLEs for learning (Mödritscher, 2010) . As the ROLE project deals with a wide range of educational scenarios, we present three different strategies, each one aiming at supporting certain needs of learners.  \nThe paper is structured as follows. The next section summarizes our understanding of personal learning environ","cbCaibn3lpIGjgxb","https://ap.wps.com/l/cbCaibn3lpIGjgxb","pdf",872441,13,"English","# Abstract\n# Keywords\n# I. Introduction\n# II. PLEs, PLE recommendations, and related work","[{\"question\":\"What problem do the authors address in this paper?\",\"answer\":\"The paper examines how recommender technology can be used to support personal learning environments (PLEs). It focuses on generating and providing recommendations that help learners use PLE technology effectively.\"},{\"question\":\"Why are recommender systems considered useful for PLE-based learning?\",\"answer\":\"Recommendations can support learners by helping them discover relevant content, peers with similar learning interests, and experts on specific topics. This complements the learner-centric nature of PLEs.\"},{\"question\":\"How is the paper structured regarding the recommendation approaches?\",\"answer\":\"After introducing PLEs and related work, the paper describes three recommender approaches developed in the ROLE project, followed by a discussion of benefits and limitations for applying each approach in PLEs, then concludes with future work.\"}]","Comparing Strategies - May I Suggest? Comparing Three PLE Recommender Strategies | PDF",1792092925,33]