On Improving Learning Outcomes Through Sharing of Learning Experiences Au Thien Wan, Shazia Sadiq, Xue Li School of ITEE, Department of EAIT University of Queensland St Lucia, Australia {twau,shazia,xueli}@itee.uq.edu.au Abstract— The sharing of experience in eLearning has recently attracted the attention of researchers to improve efficacy of eLearning in higher learning especially in the absence of face-to-face contact with educators, lecturers, facilitators and tutors. We propose the recommendation of learning experiences (LEs), a type of tacit knowledge, from a learner to other learners by capturing LEs in the form of events of interactions which are similar to sequence of events. The sequence of events is treated as basket data and analyzed using sequential data mining to determine the patterns of learning. Collaborative filtering recommendation is introduced to recommend appropriate LEs to peer learners for the improvement of learning. Keywords-learning experience; data mining; recommender system; experience sharing I. INTRODUCTION Experience sharing has been a common practice and a hot research topic since the 90’s within organizations and has benefited many organizations tremendously both financially and epistemologically. In eLearning especially with the absence of face-to-face contact with educators, lecturers, facilitators and tutors, capturing and utilizing the learning experiences of learners, referred to as learners’ experience (LE) throughout the paper, as knowledge available or sharable to peers could become a critical catalyst in making learning more efficient and producing better outcomes. According to [1], the three main essential components of learning paradigm in eLearning are human, knowledge and technology (HKT). The nature of learning process is a process of transfer between tacit and explicit knowledge, an idea first championed by Polany’s [2] but later highly promoted and made popular by Nonaka [3] in his famous SECI model. In eLearning the human-to-human contact is non-existence or at its minimal, which is very crucial in the transfer of tacit knowledge. Further more the process of learning is being transformed by the digitization of our society and this makes the choice and appropriate usage of technology the critical driving force of eLearning development. The purpose of the paper is to propose that learning experiences (LE) can be conceptualized as events of learner interactions with an eLearning system and treated as basket data. The application of data mining can help to identify appropriate LEs by different users and are introduced to peers learners in the light of hoping to improve the efficacy of learning through a Learning Experience Recommender System (LERS). The next section of the paper provides some background on learning experience in the context of eLearning. Section 3 discusses briefly the overall conceptual model of LERS architecture. Section 4 describes sequence of events treated as basket data where sequential data mining is used to harvest and analyze the learning trends. Section 5 highlights the LE recommender system and finally we conclude at Section 6. II. ELEARNING EXPERIENCE A. Learning experience Experience is a term which has preoccupied philosophers and which many have avoided. It acts as a noun sometimes and at times acts as verb making it difficult to establish a definitive view with which to work. Boud, D., Cohen, R. and Walker, D. [4] at their earlier work believed the idea of experience has within it judgment, thought and connectedness with other experience. In its most elementary form it involves perception and it implies consciousness. They further argued that experience is not just an observation, a passive undergoing of something, but an active engagement with the environment, of which the learner is an important part. Each learner forms part of the milieu, which becomes the individual as well as the shared learning experience. This continuing, complex and meaningful interaction is central to the understanding of experience. When applying to learning it is the process, systematic or random, of exploring and active or passive cognitive engagement with a domain knowledge with the objectives of gaining skill and wisdom knowledge in the hope of fulfilling the Bloom’s Taxonomy of educational objectives [5]. Explicit knowledge can be written down or drawn and described to other people. Tacit knowledge on the other hand, are things known by people but usually not documented anywhere such as the know-how, understanding mental models and insights of an individual or disciplines. In eLearning, the explicit knowledge is presented to learners in the form of instructional materials, course notes, quizzes, etc, and quite often abundant and excessive due to advances in information communications technologies 2010 10th IEEE International Conference on Advanced Learning Technologies 978-0-7695-4055-9/10 $26.00 © 2010 IEEE DOI 10.1109/ICALT.2010.134 461