Learning From Experience: Can e-Learning Technology be Used as a Vehicle?

In the academic, corporate and consumer fields the adoption of eLearning is on the rise. Despite the popularity and huge investment on it, the result from eLearning is still regarded as not quite living up to its expectations and some major concerns in its effectiveness and appropriateness have been revealed in various studies. Many of the eLearning systems developed today were merely the automation of the process and management of teaching and delivering of courses with the advantages of eliminating the time and space barrier. The value towards better learning outcomes is still an area of study, although some researchers have recognized the issues and provided innovative solutions to solve some related problems. In eLearning especially with the absence of face-to-face contact with educators, lecturers, facilitators and tutors, capturing and utilizing experiences of learners as knowledge available or sharable to peers would be a critical catalyst in making learning more efficient and producing better outcomes. Sharing experience in organization has been a research topic in the 90's and used extensively in organization. Hence many organizations since the 90's benefited epistemologically and financially from sharing experience and knowledge. On the other hand sharing of learning experience (LE) in academic eLearning is not so common but has recently been catching attention and seen as an important asset in eLearning. The evaluation of results in eLearning with respect to the increase of learning effectiveness by knowledge sharing indicated improved learning effectiveness. Accordingly an eLearning system comprises three essential components: Human, knowledge and technology (HKT). The nature of learning process is a transfer process between tacit and explicit knowledge. The purpose of the paper is to redefine LE in the context of eLearning within the HKT-paradigm and to propose a structure of LE and a conceptual architecture of an LE Recommender System (LERS). The LERS architecture utilizes learners' profiles, outcomes and behavior in order to capture and store learners' experience. Essentially LE is conceptualized as events as a result of interactions, satisfying personalized needs and promoting use and reuse of sharing of personal or common knowledge. LE reuse in the form of knowledge implies the transformation of knowledge to action typically represented as the ability to solve problem and accelerate learning efficiency. Data mining technique will form part of the LERS in helping to optimize peers learning by recommending appropriate LEs to learners based on their behavior and profiles. The originality of the concept is the use of data mining to recommend LEs dynamically to targeted learners based on user profiles and user behavior to optimize the learning process, improve effectiveness and producing better outcomes. [ABSTRACT FROM AUTHOR]