001434294 000__ 04671cam\a2200661\i\4500 001434294 001__ 1434294 001434294 003__ OCoLC 001434294 005__ 20230309003721.0 001434294 006__ m\\\\\o\\d\\\\\\\\ 001434294 007__ cr\nn\nnnunnun 001434294 008__ 210122s2021\\\\si\a\\\\ob\\\\000\0\eng\d 001434294 019__ $$a1233243575$$a1235593509$$a1244118258 001434294 020__ $$a9789813346819$$q(electronic bk.) 001434294 020__ $$a9813346817$$q(electronic bk.) 001434294 020__ $$z9789813346802 001434294 020__ $$z9813346809 001434294 0247_ $$a10.1007/978-981-33-4681-9$$2doi 001434294 035__ $$aSP(OCoLC)1239683696 001434294 040__ $$aUKBTH$$beng$$erda$$epn$$cUKBTH$$dOCLCO$$dGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dDCT$$dOCLCF$$dN$T$$dOCLCO$$dOCLCQ$$dCOM$$dSFB$$dOCLCQ 001434294 049__ $$aISEA 001434294 050_4 $$aLB1028.43 001434294 08204 $$a371.33/4$$223 001434294 1001_ $$aSweta, Soni,$$eauthor. 001434294 24510 $$aModern approach to educational data mining and its applications /$$cSoni Sweta. 001434294 264_1 $$aSingapore :$$bSpringer,$$c[2021] 001434294 300__ $$a1 online resource (xxvii, 93 pages) :$$billustrations (chiefly color) 001434294 336__ $$atext$$btxt$$2rdacontent 001434294 337__ $$acomputer$$bc$$2rdamedia 001434294 338__ $$aonline resource$$bcr$$2rdacarrier 001434294 347__ $$atext file 001434294 347__ $$bPDF 001434294 4901_ $$aSpringerBriefs in applied sciences and technology. Computational intelligence,$$x2625-3704 001434294 504__ $$aIncludes bibliographical references. 001434294 5050_ $$aEducational Data Mining in E-Learning System -- Adaptive E-Learning System -- Educational Data Mining Techniques with Modern Approach -- Learning Style with Cognitive Approach -- Framework with Stakholders in Adaptive E-Learning System -- Personalization Based on Learning Preference -- Recommender System to Enhancing Efficacy of E-Learning System. 001434294 506__ $$aAccess limited to authorized users. 001434294 520__ $$aThis book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life's problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners' preferences minutely and provide adaptive learning materials to them in a way most appropriately. 001434294 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 9, 2021). 001434294 650_0 $$aEducation$$xData processing. 001434294 650_0 $$aData mining. 001434294 650_0 $$aLearning. 001434294 650_0 $$aComputational intelligence. 001434294 650_0 $$aStudy skills. 001434294 650_0 $$aMachine learning. 001434294 650_6 $$aÉducation$$xInformatique. 001434294 650_6 $$aExploration de données (Informatique) 001434294 650_6 $$aApprentissage. 001434294 650_6 $$aIntelligence informatique. 001434294 650_6 $$aÉtude$$xMéthodes. 001434294 650_6 $$aApprentissage automatique. 001434294 655_0 $$aElectronic books. 001434294 77608 $$iPrint version:$$z9813346809 001434294 830_0 $$aSpringerBriefs in applied sciences and technology.$$pComputational intelligence.$$x2625-3704 001434294 852__ $$bebk 001434294 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4681-9$$zOnline Access$$91397441.1 001434294 909CO $$ooai:library.usi.edu:1434294$$pGLOBAL_SET 001434294 980__ $$aBIB 001434294 980__ $$aEBOOK 001434294 982__ $$aEbook 001434294 983__ $$aOnline 001434294 994__ $$a92$$bISE