Building a Fuzzy Knowledge Management System for Personalized E-learning

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

“…For instance, the Felder-Silverman Learning Style Model (FSLSM) in [57,67,69], the pre-test of multiple-choice questions in [57], and the Myers-Briggs Type Indicator (MBTI) questionnaire in [67] were adopted in several personalized learning software systems. According to these assumptions, formal learning software systems were established, attempting to model learning processes and activities similar to the ones carried out in a classroom [10,12,[14][15][16][17][18][19][30][31][32][33][34]37,39,[45][46][47][49][50][51][52][54][55][56][58][59][60][63][64][65][66]68,[70][71][72][73][74]92]. In such cases, the learning software system implements well-defined learning content, learning outcomes, and assessment measures [42,57,…”

“…Most formal learning systems attempt to model the human tutor and are called tutoring systems [39,71]. Tutoring systems are implemented using different technologies, e.g., mobile technologies for language learning systems [19,37,51] context-aware technologies for ubiquitous learning [52,54,62], web technologies that include intelligent tutors and e-Learning systems [12,[14][15][16][17][18]45,49,53,55,59,60,[63][64][65][66]70,73,74,92], and semantic web technologies [10,[30][31][32][33][34]46,47,56,58,68,71,72]. Tutoring systems are designed with variable interaction models, e.g., game-based tutoring systems [30,31,33,34], including game-based systems for children with learning disabilities [32] and vocabulary learning…”

“…In addition to structured data representation, several studies have included sophisticated knowledge representations such as (i) Learning Objects [59] that support several personalized learning applications, such as e-course generation and recommendation systems [12,15,73], planning personalized learning paths in context-aware ubiquitous environments [54,56], personalized search and delivery of learning objects to learners [47,58], as well as automatic personalized recommendations for e-learners [96]; (ii) ontologies [66], supporting a wide range of personalized learning applications such as ontology-based adaptive, personalized and disability-aware e-learning systems [14,65], personalized healthcare human resource management [16,45], personalized learning material for children with special needs [46], and computer network design courses [71,97], or more recently (iii) Linked Open Data (LOD) [30,74], including ones that support content-based recommender systems [98]. Chiappe defined Learning Objects as: "A digital self-contained and reusable entity, with a clear educational purpose, with at least three internal and editable compo-nents: content, learning activities and context elements.…”

“…For instance, the learning environment can be characterized by different learning processes [10,11], which in turn require different interaction models (e.g., game-based [12], e-learning [13]) and software technologies (e.g., hypermedia [14], mobile [15]) to support them. Similarly, learning contents and user models implemented in personalized learning software systems can be realized using different data and knowledge formalisms (e.g., structured databases [16], Learning Objects [17]), and require different modeling and profiling techniques (e.g., stereotyping [18], machine learning-based [19]).…”

Learner modeling in cloud computing

“…Siddique et al 2018;Gholipour et al 2011), Moodle -LS System (AliSalahli et al 2012), AEHS-LS(Kumar et al 2013), ADMCA(Limongelli et al 2011), AES(Karadimce and Davcev 2012) …”