AMPLE: Adaptive systeM for Personalized LEarning       


DANISH CONTRACTOR photoBIKRAM SENGUPTA photoKommy Weldemariam photo

AMPLE: Adaptive systeM for Personalized LEarning - overview

Online courses and learning systems have been gained tremendous popularity over the last few years. While their ease of access and availability make them a very useful medium for knowledge sharing and learning, they do not keep the learners and their learning abilities in mind. The “one size fits all” approach to learning content does not work in a large virtual classroom consisting of diverse students with different skill profiles, learning styles, aptitude and capability. In a traditional classroom, teachers interact closely with students are in a position to evaluate the pace and depth of the curriculum being taught and can also suggest learning content to students not being able to cope with the general classroom teaching. Such suggestion and guidance is absent in current online learning systems and we aim to address this gap.

The Adaptive System for Personalized Learning (AMPLE) allows learning content to be customized based on the learning pace, skill profiles, preferences of a learner. AMPLE does this by determining the comprehension burden associated with the content and identifying what content is most suited for different learners. The comprehension burden of content is determined using measures that quantify notions of "Readability", "Concept density", "Illustrative richness", "Topic dispersion" etc. For instance, content that introduces a new topic can be expected to have higher illustrative richness and less topic dispersion scores, while advanced topics could be expected to have high concept density. Thus, by automatically analyzing content in terms of its comprehension burden, AMPLE can suggest content suited to different learning needs. We analyze the learning contents, interaction (unstructured data) and social learning data on Big Data platform and link the learning contents with curriculum standards and learners' structured data stored in learning management system. In addition, AMPLE continuously builds and improves a learner's profile based on the content accessed, the duration for which it was used, test scores etc. By keeping track of learners activities, AMPLE has the ability to suggest supplementary content, or related content that may be of interest to the learner. We are also developing a rich and smart big data catalogue for indexing rich media content and mapping information.