Math and Science
of Navigated Learning

Learning is complex; there is no one size fits all

Sign Up Free

Navigator and AI

Each student brings their knowledge, skills, experiences, and dispositions to learning. Navigated Learning employs AI techniques to individualize the learning experience and make individualized learning at scale possible. Navigator curates a catalog of learning activities to compute vectors such as relevance, engagement, and efficacy, and also uses AI techniques to understand each learner 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 suggest individualized pathways. Navigator is generalizable across languages, disciplines, and learners.

Competency Framework

Navigator goes beyond codifying concepts to structure learning as a progression space (metric space of competencies with partial ordering of dependencies) of competencies that we refer to as the Navigator Competency Framework (NCF). A polyline across domains of competencies for each subject represents a learner's proficiency that we call as their skyline. NCF includes a polyline algebra to compute measures such as route, reroute, and mean-time-to-learn.

NCF enabling AI

NCF enabling AI: The Navigator Competency Framework (NCF) provides a framework for organizing all learning concepts. The NCF details the competency, embeds a variety of curated learning activities, captures each learner's profile, and relates to local norms. We use LDA and word2/doc2-vec embeddings to compute competency models from a set of documents, machine learning techniques on activity stream data to curate learning activities and understand the learner, and principles of learning to personalize pathways. NCF enables AI techniques to personalize learning pathways and provide real-time dashboards to stakeholders.

Progression Space

We structure the vastness of the learning space as a progression space of competencies. Creating the progression space makes it possible for mathematical measures such as route and mean-time-to-learn to be computed. The structure also allows for the implementation of learning sciences research and best practices in teaching and learning.

Competencies

Factors, such as depth of knowledge, common struggles on a concept, and decay functions, characterize the multidimensional competencies. The Navigator Competency Framework structures the competencies.

Facets of Learning

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 understanding of the learner.

Big Data

Big data is captured across distributed systems and offline activities. Machine learning approaches are used to curate the full spectrum of learning resources and activities. As learners interact with materials, learner and activity vectors are continuously updated. We "locate the learner" using a polyline across facets of learning and have developed a polyline Algebra to compute individual pathways and re-route suggestions.

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 teacher). We use all of these signals in query analysis and ranking to keep the search results pedagogically aware and personalized.

Operationalizing Science

Operationalizing Science: Navigator encodes Event, Condition, Principles of learning, and Action (ECpA) as a set of models. These model “listens” to events, and based on the condition of a learner, triggers actions, which are suggestions based on the learning principles associated. 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.

Research

Gooru is a research organization and establishes partnerships with leading researchers in data science, learning science, psychology, and teacher education from around the world.

Songer, Nancy & Newstadt, Michelle & Lucchesi, Kathleen & Ram, Prasad. (2019). Navigated Learning: An Approach for Differentiated Classroom Instruction Built On Learning Science and Data Science Foundations. Human Behavior and Emerging Technologies. 10.1002/hbe2.169.

Srinath Srinivasa, and Prasad Ram. Characterizing Navigated Learning, Technical Report, Gooru Labs, 2019.

Chaitali Diwan, Srinath Srinivasa, and Prasad Ram. Automatic Generation of Coherent Learning Pathways for Open Educational Resources, In Proceedings of the Fourteenth European Conference on Technology Enhanced Learning (EC-TEL 2019), Springer LNCS, Delft, Netherlands, 16-19 September 2019 .

Aparna Lalingkar, Srinath Srinivasa, and Prasad Ram. (2019). Characterizing Technology-based Mediations for Navigated Learning, Advanced Computing and Communications, 3(2), ACCS Publication, pp. 33-47.

Praseeda, Srinath Srinivasa and Prasad Ram Validating the Myth of Average through Evidences In: The 12th International Conference on Educational Data Mining, Michel Desmarais, Collin F. Lynch, Agathe Merceron, & Roger Nkambou (eds.) 2019, pp. 631 – 634.

Chaitali Diwan, Srinath Srinivasa, and Prasad Ram. Computing Exposition Coherence of Learning Resources, In Proceedings of The 17th International Conference on Ontologies, Databases and Applications of Semantics (ODBASE 2018), Springer LNCS, Valletta, Malta, October 22-26, 2018.

Sharath Srivatsa, Srinath Srinivasa. Narrative Plot Comparison Based on a Bag-of-actors Document Model. In Proceedings of the 29th ACM Conference on Hypertext and Social Media (ACM HT’18), Baltimore, USA, ACM Press, July 2018.

Lalingkar. A., Srinivasa, S. and Ram, P. (2018). Deriving semantics of learning mediation, In Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE, 9th July to 13th July, IIT Bombay.

Aditya Ramana Rachakonda, Srinath Srinivasa, Sumant Kulkarni, M S Srinivasan. A Generic Framework and Methodology for Extracting Semantics from Co-occurrences. Data & Knowledge Engineering, Elsevier, Volume 92, July 2014, Pages 39–59. DOI: 10.1016/j.datak.2014.06.002.

Sumant Kulkarni, Srinath Srinivasa, Tahir Dar. 2018. Syncretic Matching: Story Similarity Between Documents. In Proceedings of ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018).

Further Reading

As a research organization, we strive to improve learning outcomes and teaching practices continually. We publish in peer-reviewed journals and present at academic conferences.