Each student brings their knowledge, skills, experiences, and dispositions to learning. Learning Navigator uses AI techniques that curate a customized learning experience and make individualized learning at scale possible. The AI used in Learning Navigator is generalizable across languages, disciplines, and learners.
Gooru, in collaboration with researchers, uses the science of learning and data science to create an adaptive, real-time data backbone for learning. Based on the findings and research inputs and outputs, we have developed the Navigated Learning approach.
Navigated Learning operationalizes learning principles using emerging ideas in artificial intelligence and data science. The result is the continuous, real-time generation of students' cognitive and non-cognitive data to support a teacher's ability to customize instruction. In Navigated Learning, every aspect of the learning ecosystem and experience is aligned around students gaining mastery in the various competencies they are expected to learn.
Big data operationalizes science of learning. Machine learning approaches are used to curate the full spectrum of learning resources and activities. As learners interact with materials learning vectors are continuously updated. The data science team has developed Skyline Algebra, which is the math that structures the competency model. This mathematical structure and rigor enables computations such as route and re-route suggestions.
The team established a set of models for the operationalization of the learning principles called ECPAs for Event Condition learning Principles and Action. These models “listen” to events, and based on the condition about a learner, the models trigger actions, which are suggestions based on the learning principles associated with the model. By operationalizing the learning principles, the suggestions and actions offered to the students are backed by learning theories and science.
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.
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).
The search engine for learning is distinguished based on the structuring of the learning space, the curated catalog of learning activities and an understanding of the user as a learner and as a teacher. We use all of these signals in query rewrites and ranking to keep the search results pedagogically aware and personalized.