Project Primes

ents of these courses. Yet, a pedagogy and set of learning outcomes designed for students in one setting (e.g., country, level, subject), are often not appropriate for students in different settings, mainly due to differences in culture, language and terminology and educational backgrounds. Moreover, even for students sharing the above characteristics, differences in their personal learning styles, strengths and weaknesses, mean static MOOCs can never attain the same level of rapport with the student as a teacher. To this end, PRIMES will deliver: (a) a platform allowing for automatic multi-level personalisation of MOOCs, and (b) a first set of courses provided as a proof-of-concept. For every student registered with PRIMES, a detailed educational and social profile will be created, encoding the students’ educational background, academic interests, and possible future aspirations, along with their attainment in several courses and grasp of threshold concepts. Each course and lecture will be annotated, to allow for selective extraction of self-contained excerpts of sessions/lectures with specific (language, theme, level, difficulty, etc.) characteristics. PRIMES will then employ novel machine learning and recommendation algorithms, taking into account the individual student profiles and the courses/sessions on offer, to produce personalised recommendations to further each student’s progress and attainment; our platform will be able to extract and recommend, for each subject and intended learning outcome, parts of lectures and/or courses, along with matching (formative and/or summative) assessment tasks, matching the current level of attainment of each student, while covering the areas where the student faced difficulties in grasping or applying the corresponding knowledge, methods and techniques.