Navigated Learning

Each student brings their knowledge, skills, experiences, and dispositions to learning. Navigated Learning employs AI techniques to individualize the learning experience and make individual learning at scale possible. From a corpus of content in any discipline, Navigator can use the transcripts to compute the competency framework for the discipline with topic analysis and deep learning techniques. Navigator curates a catalog of learning activities to machine classify learning activities to competencies, and compute vectors such as relevance, engagement, and efficacy. Navigator uses AI techniques to understand each learner and to compute their preferences, context, and citizenship. Navigator then uses the science of learning with its curated catalog and real-time understanding of the learner to rank suggestions of learning activities. Navigator is generalizable across languages, disciplines, and learners.

Navigator goes beyond codifying concepts to structure learning as a progression space of competencies (metric space of competencies with partial ordering of dependencies) that we refer to as the Navigator Competency Framework. A polyline across domains of competencies for each subject represents a learner's proficiency that we call their Skyline. The Navigator Competency Framework includes a polyline algebra to compute measures such as route, reroute, and mean-time-to-learn. The Navigator Competency Framework details the competency with factors such as depth of knowledge, common struggles on a concept, and decay functions, embedding a variety of curated learning activities, capturing each learner's profile, and relating to local norms. We use LDA and word2/doc2-vec embeddings to compute the Navigator Competency Framework from a set of documents that are then reviewed and finalized by discipline experts.

There are several facets to learning that relate to the many disciplines, including curriculum, skills, and non-cognitive aspects. These facets are often related. Learner's performance in science is often associated with their proficiency in Math and their non-cognitive skills. Navigator has detailed and consistent representation of the learner across all of their facets. This enables the suggestion of personalized pathways based on an increasingly complete understanding of the learner.

Big Data

Big data is captured across distributed systems and offline activities. Navigator uses xAPI records aggregated across Learning Record Stores to gather the big-data. Machine learning approaches use the xAPI data streams to curate learning activities and locating learners with increasing precision. As learners engage in learning activities, Navigator continuously improves the curation of content and location of the learner. 

Search Engine for Learning

The search engine for learning is based on the structure of the learning space, the curated catalog of learning activities, and an understanding of the user (learner or instructor ). We use all of these signals in query analysis and ranking to keep the search results pedagogically aware and personalized.

Operationalizing Science

Navigator encodes Event, Condition, Principles of learning, and Action (ECpA) as a set of models. These models trigger a ranked list of suggested actions based on events, learner conditions, and principles of learning. By operationalizing the learning principles with big data, Navigator ensures the suggestions offered to learners and their instructors are backed by learning theories and science.