With the constant rise of the use of technology in learning education, more and more students are relying on
learning software/systems to make use of open educational repositories or take part in massive open online
courses. Typically, in such systems, learners are exposed to an overload of information where they need to find
information sources on their own. However, not all students are versed in information seeking/studying tactics,
or have strong learning skills to build optimal learning paths. This brings attention to the great challenge of
producing software platforms that facilitate learning for each and every learner by offering them a personalized
and optimized learning experience. Building on the principles of technology-enhanced learning, semantic
technologies and learning analytics, we will investigate and propose design principles and acceptance factors
for optimizing personalized learning experience in adaptive online learning systems. In particular, the
anticipated outcomes of this six-month industry collaboration project are as follows: 1) Principles for designing
and configuration of personalized learning platforms; 2) A technology acceptance model for personalized
learning platforms; and 3) Transfer of research results to the industry partner in order to validate and calibrate
their existing research and inform their future research and development activities.