Learner analytics use the experiences of a community [network?] of learners to help individual learners in that community to identify more effectively learning content from a potentially overwhelming set of choices.
Analytics and recommendations have generative power. The object recommended many not yet exist – it may be something that the learner must construct or that is constructed from the recommendation.
Analytics can be assessed from numerous perspectives, including: accuracy, adaptivity, behaviour, confidence, coverage, coverage, diversity, drop-out rate, effectiveness of learning, efficiency of learning, learner improvement, novelty, precision (comparing resources selected by user with those selected by algorithm), prediction accuracy, privacy, reaction of learners, recall (ratio of relevant results to those presented by algorithm) results, risk, robustness, satisfaction, scalability, serendipity, trust, user preference, utility.
(MUPPLE seminar – Hendrik Drachsler)