Leverage free and open Navigator to advance education.

Become a Member


Gooru’s Navigator technology allows each innovator to engage with their own goals and agendas including research areas, tools, study outcomes, and validation while joining other innovators to give all learners equal access to results. Each innovator can measure learning gains, study positive learning outcomes and validate data that goes back into the Navigator system and informs the system.

Big Data

Gooru brings anonymized usage and efficacy data back to researchers to help improve science and practice of learning through a mathematical structure. This structure allows the system to gather rich activity stream data for every learner in all facets of learning, including cognitive and non-cognitive skills and knowledge. This activity stream data can be captured for all users and form large, rich data sets for researchers in the Navigated Learning Collaborative. Gooru's research-to-practice model allows for a distributed learning system to come together to accelerate research and develop best practices in implementation.

Refining Navigated Learning

Gooru has established a research life cycle that informs the platform at every stage of design, development, and implementation. With Navigated Learning, Gooru can measure all facets of learning and amplify education and skills training using a real-time data backbone to create personalized, adaptive learner journeys. Gooru provides real-time actionable information to all stakeholders in the learning ecosystem to track learning and struggles. Researchers for all disciplines can work together on major research projects to better unpack the complexities of learning.

Research to Practice Cycle

By integrating research and practice, the two inform each other at each stage in the research and product development cycle. It creates a closed loop information gathering approach to validate the data and learning science that informs the system and product design and moves the academic fields forward with rich, large learning data sets. It establishes a rich environment for transdisciplinary convergence research. Each project will inform and ensure Navigator is designed, implemented, and validated with science.

Accessing Data

Innovators will access data through Mission Control. Mission Control’s dashboard provides access to real-time learner data at different grain sizes. Innovators can query the database and apply their own filters to get the data sets that are most relevant to their work. This data and information is easily accessible and exportable. Gooru maintains the highest levels of data privacy and security. Any access to data will require permissions and automated settings will be set conservatively to ensure the privacy of all users.

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. 

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).