Meeting the Needs of All Learners

Navigated Learning is designed for flexibility and accessibility.

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Scale Across Disciplines, Geographies, and Learners

An approach that works in one sociocultural context with one set of users and educators is not guaranteed to work when replicated in another environment. Navigated Learning is designed to adapt and scale across disciplines and geographies.

It's All About the Learner

Navigator's ability to locate the learner is designed for scale with one representational model that is consistent across all facets of learning - subjects, non-cognitive, and socio-emotional. The representational model tracks progress and performance as well as non-cognitive measures. Gooru's visualization model is intuitive and actionable, all members can see data and make real-time informed decisions for each learner. While other systems deal with extreme details, Gooru enables learners with a visualization that is immediately understandable and actionable.

Full-spectrum of Learning Activities

Digital learning is about leveraging all available resources in education, from watching videos and answering multiple-choice questions to writing essays and working on group projects. Navigator scales because it captures data from a full-spectrum of learning activities. Offline projects, writing essays, and proofs are graded using rubrics and entered by the instructor and allow for a deeper characterization of the learner. Digital videos, simulations, and multiple-choice enhance the learning experience. All types of digital, offline, and social learning produce digital data that helps to dynamically locate the learner's knowledge, skills, and mindsets.

Works in All Environments

A large number of learners around the world have limited access to devices or connectivity. Navigator scales by working in classrooms with one instructor, one smartphone, and limited internet access to schools with computer labs and technology-rich environments with one-to-one devices and broadband connectivity.

Designed for All Learners

Navigator is designed for all learners, including those with special needs. Learners with disabilities have Skylines that locate them and obtain individual pathways with activities that are appropriate to them, including resources appropriate for the hearing impaired, those with color blindness, or learners on the autism spectrum.

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).