Transgenic learning： towards a rule-based eLearning recommendation model for massive enrollment
Daniel Burgos and Alberto Corbí
Current models and methodologies in the field of educational technology do not involve engagement between formal and informal learning. Usually, only activities within a formal environment （i.e., assignments, grades, etc.） are stored, tracked and retrieved as an input parameter in recommendation systems. There is normally no useful combination with the informal activity of every user （e.g., social networks and continuous evaluation）. In addition, tutoring systems in academic domains are usually based only on content filtering and collaboration from other students, which contributes to dissolving the crucial role of the tutor. Last, MOOCs and SPOCs have become a crucial part of educational models combining formal and informal settings, playing a key role in the learning path of every user. Education requires a disruptive approach to boost the learning-teaching process. We call it transgenic learning and by making use of information about the user’s behavior and interactions as well as efficient monitoring and personalized counselling by a tutor, we can improve the learning performance of every user. This paper presents LIME, a personalized eLearning recommendation model for public and private social networks and learning management systems, which supports this approach, specifically for massive courses and large data sets. It then elaborates on a framework and software prototype （iLIME） which has been developed to demonstrate how the LIME model could operate independently of the learning management infrastructure in use. Finally, it reports on a case study developed around the Apereo Sakai CLE 2.10-svn, in the context of a MOOC strategy to be implemented at university level. Technical issues and challenges are also discussed and solutions are proposed in order to run iLIME and deliver LIME-based recommendations to learners in a real academic scenario.
Keywords： transgenic learning； informal learning； massive open online courses； rule-based recommendation system； learning tool interoperability （LTI）
Developing a MOOC experimentation platform： Insights from a user study
Vitomir Kovanovi?, Sre?ko Joksimovi?, Philip Katerinopoulos, Charalampos Michail,
George Siemens, and Dragan Ga?evi?
In 2011, the phenomenon of MOOCs swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact student learning outcomes and experience. In this paper, we introduce MOOCito （MOOC intervention tool）, a user-friendly software platform for the analysis of MOOC data, which focuses on conducting data-informed instructional interventions and course experimentations. We cover important design principles behind MOOCito and provide an overview of the trends in MOOC research leading to its development. Although a work-in-progress, we outline the prototype of MOOCito and the results of a user evaluation study that focused on the system"s perceived usability and ease-of-use. The results of the study are discussed, as well as their practical implications.