Management of Latent Learning Needs in Adaptive e-Learning Systems

Abstract

The construction of personalized courses taking into account needs and preferences of each learner is one of the most studied topics within the field of adaptive learning systems. Such systems assume that learning needs are well know by the teacher or, in self directed learning settings, that learners can easily determine and express own needs. Nevertheless, in real situations, learning needs often remain latent and, for this reason, unsatisfied. To overcome this limitation we propose in this paper a methodology and a prototype system enabling the elicitation of latent learning needs, as well as the automatic generation of learning experiences capable of satisfying such needs. The proposed methodology applies principles of recommending systems and also relies on a semantic representation of topics to be taught. The encouraging experimental results obtained with University students are also discussed.

Information Dissemination Prediction of College Students’ Learning Requirements Based on Mobile Social Network

“…Because mobile learning is a personalized and continuous training method which is free from the limitations of time and space, so it's particularly important to make accurate predictions and good plans for college students' requirements for mobile learning [7][8][9][10][11][12][13]. Initiators and subjects of the mobile learning requirements are college students who conduct learning activities to acquire knowledge of the learning targets, and the mobile learning platforms are providers of learning resources to meet the learning requirements of students [14][15][16][17][18][19][20]. There is a service receiver-provider relationship between college students and the mobile learning platforms, so taking the information dissemination and fluctuation of learning requirements between the two parties as the starting point of research to explore the information dissemination efficiency of college students' learning requirements under different conditions and study the prediction accuracy optimization method is a research direction that is innovative and groundbreaking.…”

“…The topic about teacher requirements involves the initial and ongoing motivation as well as the behaviors and attitudes toward current or upcoming ICT systems for education. Capuano et al [23] argue that designing personalized courses with requirements and preferences of each learner taken into consideration is one of the most studied topics within the field of adaptive learning system, the authors developed an approach and a prototype system that can trigger potential learning requirements and automatically generating learning experiences that can meet such learning requirements. Watanabe [24] discussed the requirements of Japanese universities and learners for e-learning.…”

A Dynamic Learning Interest Model for a Personalized Learning Requirement Analysis of College Students

“…Chen and Hsu [15] adopted the theory of attractive quality (Kano model) to analyze the needs of users for multimedia design in e-learning, so as to have a better under-standing of how customers evaluate a product, and help companies focus on the most important attributes that need to be improved. Capuano et al [16] pointed out that designing personalized courses taking into account the needs and preferences of each learner is one of the most studied topics within the field of adap-tive learning systems, however, in real cases, learning needs are often latent and un-satisfied; to breakthrough this limitation, the authors proposed a methodology and a prototype system to trigger the latent learning needs, and automatically generate learning experiences capable of satisfying such needs. The proposed methodology conformed to the principles of recommending systems and depend on the semantic representations of topics to be taught.…”

A Semi-Automated System for Recognizing Prior Knowledge

“…Conceptually, adaptive learning can be performed in three different non-disjoint ways: 1) personalization of the learning processes: where the learning process of each student is personalized according to the student profile and behavior [2][3], 2) content personalization: where the learning objects (or the courses) offered to the student (and the activities the student should do) are personalized according to the student experience [4][5] [6]; and 3) interface adaptation: where the learning management system interface is adapted according to every student's needs, preferences and history [7] [8].…”