Adaptive scheduling algorithms for online learning providers

Learning success in an online environment (e.g. for learning languages, maths, and others) can be improved if it is supported by algorithms that present contents and questions according to the learners learning rate. We offer to support the development of such algorithms in cooperation with online learning providers. Based on a dataset with learning and answer patterns from current use of their service, we develop algorithms that compute and suggest an optimal schedule of learning and practicing new knowledge, by modeling users’ learning and forgetting curves. Data needed for development should reflect a given user’s learning history in terms of the knowledge items practiced and the user’s rate of success/failure in recollecting already practiced items. The result is a set of algorithms that help learners learn faster and with greater retention.