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Validate content, tools, and practices.

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Benefits to Practitioners

Practioners contribute to Navigator in a unique way. Content providers contribute to Navigator by helping build competency models tagging existing open content with rich metadata. Implementers and users provide anonymized data from their use of Navigator which fuels the research-to-practice cycle, and tool providers develop open-source Navigator and other tools to Navigator users.

Validator Benefits

Include access to real-time data to study the efficacy of their tools and practices. Changes made to the content, tools, and practices are informed by data to continually enhance and support learning outcomes and instructor practices.


Navigator is free for students and teachers. Our Practitioners pay a fee based on their organization size and usage and gain a much more in-depth use of the technology. A paid membership gives members tenancy on the Navigator platform, access to Mission Control for all the stakeholders, unlimited access to all the Navigator tools for instructors and learners and the Library tool, personalized training, consulting services for strategic use of Navigator, dedicated user support, as well as many other collaborative opportunities.


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).