Personalized Recommendation Based on Collaborative Tagging Techniques for an e‐Learning System

Abstract

<p>The&nbsp; research&nbsp; topic&nbsp; involves&nbsp; personalization&nbsp; of&nbsp; an&nbsp; e‐learning&nbsp; system&nbsp; based&nbsp; on<br />collaborative&nbsp; tagging&nbsp; techniques&nbsp; integrated&nbsp; in&nbsp; a&nbsp; recommender&nbsp; system.&nbsp; Collaborative&nbsp; tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags.&nbsp; The&nbsp; systems&nbsp; can&nbsp; be&nbsp; distinguished&nbsp; according&nbsp; to&nbsp; what&nbsp; kind&nbsp; of&nbsp; resources&nbsp; are&nbsp; supported. Besides helping user to organize his or her personal collections, a tag also can be regarded as a user&rsquo;s personal opinion expression. The increasing number of users providing information about themselves&nbsp; through&nbsp; social&nbsp; tagging&nbsp; activities&nbsp; caused&nbsp; the&nbsp; emergence&nbsp; of&nbsp; tag‐based&nbsp; 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&nbsp; research&nbsp; aims&nbsp; to&nbsp; analyze&nbsp; and&nbsp; define&nbsp; an&nbsp; enhanced&nbsp; model&nbsp; to&nbsp; select&nbsp; tags&nbsp; that&nbsp; reveal the preferences and characteristics of users required to generate personalized recommendations. Options&nbsp; on&nbsp; the&nbsp; use&nbsp; of&nbsp; models&nbsp; for&nbsp; personalized&nbsp; tutoring&nbsp; system&nbsp; were&nbsp; also&nbsp; considered. Personalized&nbsp; learning&nbsp; occurs&nbsp; when&nbsp; e‐learning&nbsp; systems&nbsp; make&nbsp; deliberate&nbsp; efforts&nbsp; to&nbsp; design educational&nbsp; experiences&nbsp; that&nbsp; fit&nbsp; the&nbsp; needs,&nbsp; goals,&nbsp; talents,&nbsp; learning&nbsp; styles,&nbsp; interests&nbsp; of&nbsp; their<br />learners&nbsp; and&nbsp; learners&nbsp; with&nbsp; similar&nbsp; characteristics.&nbsp; In&nbsp; practice,&nbsp; models&nbsp; defined&nbsp; in&nbsp; 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 &scaron;to tagovi korisnicima pružaju pomoć u organizaciji sadržaja, oni su korisni i u izražavanju mi&scaron;ljenja 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 pobolj&scaron;anih 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>