The Navigated Learning Collaborative joins collaborators across disciplines and geography to bring together diverse stakeholders in the learning ecosystem - content providers, implementers, researchers, technologists, educators, funders, school administrators, and government officials.
The Navigated Learning Collaborative developed out of a fundamental understanding by its collaborators that learning is complex, learners are diverse, and ecosystems are intricate. We recognized that the answers to these challenges are already available; what was missing was coordination between the various stakeholders to organize the answers into a simple, effective practice with free to use tools.
The Navigated Learning Collaborative includes innovators, practitioners, and partners to integrate and scale Navigator tools. It is designed that all Collaborators can leverage Navigator in ways specific to their needs. They contribute content, develop or share competency models, provide feedback from their implementation, explore and validate usage metrics, or leverage anonymized data to enhance the underlying science and advance Navigator for all users.
Content providers, administrators, implementers, funders, and partners contribute to Navigator applications and use them with their cohorts. Researchers bring learning science and data science to develop models for learner identity, use principles of learning in suggesting individual pathways, and estimate the efficacy of learning activities. Funders fuel innovation, place a spotlight on the diversity of learners, the complexity of learning, and the intricacies of the learning ecosystem. Partners integrate Navigator tools to sustainably reach millions of users.
Navigator is free for students and teachers. Our Navigated Learning Collaborative Members 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, unlimited access to all the Navigator tools including access for their admins to Mission Control and Navigator Library, personalized training, consulting services for strategic use of Navigator, dedicated user support, as well as many other collaborative opportunities.Membership
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.
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).