Personalized Recommendation Based on Collaborative Tagging Techniques for an e‐Learning System
<p>The research topic involves personalization of an e‐learning system based on<br />collaborative tagging techniques integrated in a&...
Summary: | <p>The research topic involves personalization of an e‐learning system based on<br />collaborative tagging techniques integrated in a recommender system. Collaborative tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags. The systems can be distinguished according to what kind of resources are supported. Besides helping user to organize his or her personal collections, a tag also can be regarded as a user’s personal opinion expression. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag‐based profiling<br />approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. Dissertation research aims to analyze and define an enhanced model to select tags that reveal the preferences and characteristics of users required to generate personalized recommendations. Options on the use of models for personalized tutoring system were also considered. Personalized learning occurs when e‐learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, learning styles, interests of their<br />learners and learners with similar characteristics. In practice, models defined in the dissertation were evaluated on tutoring system for teaching Java programming language.</p> === <p>Predmet istraživanja disertacije obuhvata personalizaciju tutorskih sistema za elektronsko učenje primenom tehnika kolaborativnog tagovanja (collaborative tagging techniques) integrisanih u sisteme za generisanje preporuka (recommender systems). Tagovi, kao oblik meta podataka, predstavljaju proizvoljne ključne reči ili fraze koje korisnik može da upotrebi za označavanje različitih sadržaja. Pored toga što tagovi korisnicima pružaju pomoć u organizaciji sadržaja, oni su korisni i u izražavanju mišljenja korisnika. Veliki broj informacija koje korisnici pružaju o sebi kroz aktivnosti tagovanja otvorio je mogućnost primene tagova u generisanju preporuka. Istraživanje disertacije je usmereno na analizu i definisanje poboljšanih modela za odabir tagova koji otkrivaju sklonosti i osobine korisnika potrebne za generisanje personalizovanih preporuka. Razmatrane su i mogućnosti primene tako dobijenih modela za personalizaciju tutorskih sistema. Personalizovani tutorski sistemi korisniku pružaju optimalne putanje kretanja i adekvatne aktivnosti učenja na osnovu njegovih osobina, njegovog stila učenja, znanja koje on poseduje u toj oblasti, kao i prethodnog iskustva korisnika sistema koji imaju slične karakteristike. Modeli definisani u disertaciji u praksi su evaluirani na tutorskom sistemu za učenje programskog jezika Java.</p> |
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