Gooru leverages big data, AI, and research to develop Navigator technology.
Navigated Learning employs AI techniques to individualize the learning experience and make individual learning at scale possible. Navigator can use transcripts from any collection of works in any discipline in any language to compute the competency framework with topic analysis and deep learning techniques. The Navigator’s curated catalog of learning activities is machine-classified into competencies and uses vectors such as relevance, engagement, and efficacy. These are then used to understand each learner by computing their preferences, context, perseverance, and grit to provide real-time suggestions of learning activities.
Navigator goes beyond codifying concepts and structures learning as a progression space of competencies. They are organized in a metric space with partial ordering of dependencies called the Navigator Competency Framework. With Navigator, each learner profile shows a polyline across domains for each subject. We call this polyline a Skyline. The Navigator Competency Framework uses polyline algebra to compute measures like route, reroute, and mean time-to-learn. The Framework details each competency with factors such as depth of knowledge, common struggles, and decay functions. It simultaneously embeds a variety of curated resources, captures each learner’s profile, and is aligned to state and national standards. Gooru uses 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.
Navigator can measure all facets of learning and amplify education and skills training. To fully locate and characterize a learner, the many facets of learning need to be measured or calculated. Knowledge of non-cognitive skills, such as grit and perseverance and soft skills, such as communication and collaboration are integral to provide a complete and accurate representation of the learner. With this information, Navigator can make appropriate suggestions on each learner’s personalized pathway.
Big data is captured across distributed systems and offline activities. Navigator uses xAPI records aggregated across the Learning Record Stores to gather the big data. Machine learning approaches use the xAPI data streams to curate learning activities and locate learners with increasing precision. As learners engage with Navigator’s activities and resources, the system continuously improves the curation of content and refines location of the learner.
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). Navigator uses all of these signals in query analysis and ranking to keep the search results pedagogically aware and personalized.
Navigator encodes Event, Condition, and Action (ECpA) models using Principles of Learning as the overarching guide to make suggestions. 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.