Pixel International Conferences

Educational organizations – whether they be schools, colleges, universities, or statutory institutions – play a cardinal role in the development of knowledge, skills and competences, first-hand through the development of relevant and effective curricula. At the same time, this must ideally also enable academic freedom and allow the learner to practice self-determined learning, which has been shown to yield much higher motivation and cognitive performance over traditional methods. However, due to inter- and intra-organizational knowledge fragmentation, there has been great challenges to assess the relevance of curricula by identifying knowledge gaps and the need for curriculum adjustments, for instance, due to course outcomes that may not (or no longer) serve a purpose. This challenge is amplified when the intention is to further ensure a degree of autonomy and personalized learning to encourage due ownership of the learning path by the learner. In this paper we describe and discuss a prototype tool, called “Software for Target Orientated Personal Syllabus” (STOPS), developed at Aalto University to address these fundamental issues in engineering education. We further associate the concepts and features of STOPS with broader theoretical framework of knowledge management (KM). KM has been a prominent study field for organizational value optimization in a variety of commercial, engineering and scientific sectors, by curbing knowledge fragmentation. KM brings together three core organizational resources – people, processes, and technologies – to enable the organization to create, use and share knowledge more effectively. In recent years KM philosophies and instruments have begun to propagate into the education field. We discuss how STOPS, an apparent curriculum-planning tool, can serve as a tangible manifestation of abstract KM principles, in particular, for engineering and science education. Lastly, we briefly allude to the rapidly changing job markets amid the advent of mass digitization, and how the fundamental impacts on the dynamics of education can be better managed using data mining and machine learning techniques built upon the existing STOPS concepts.