Navigated Learning

The technology of Navigated Learning.

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Data Informed Navigation

Gooru leverages big data, AI, and research  to develop Navigator technology.

Navigator and AI

Each student brings their knowledge, skills, experiences, and dispositions to learning. Navigated Learning employs AI techniques to individualize the learning experience and make individual learning at scale possible. From a corpus of content in any discipline, Navigator can use the transcripts to compute the competency framework for the discipline with topic analysis and deep learning techniques. Navigator curates a catalog of learning activities to machine classify learning activities to competencies, and compute vectors such as relevance, engagement, and efficacy. Navigator uses AI techniques to understand each learner and to compute their preferences, context, and citizenship. Navigator then uses the science of learning with its curated catalog and real-time understanding of the learner to rank suggestions of learning activities. Navigator is generalizable across languages, disciplines, and learners.

Competency Framework

Navigator goes beyond codifying concepts to structure learning as a progression space of competencies (metric space of competencies with partial ordering of dependencies) that we refer to as the Navigator Competency Framework. A polyline across domains of competencies for each subject represents a learner's proficiency that we call their Skyline. The Navigator Competency Framework includes a polyline algebra to compute measures such as route, reroute, and mean-time-to-learn. The Navigator Competency Framework details the competency with factors such as depth of knowledge, common struggles on a concept, and decay functions, embedding a variety of curated learning activities, capturing each learner's profile, and relating to local norms. We use LDA and word2/doc2-vec embeddings to compute the Navigator Competency Framework from a set of documents that are then reviewed and finalized by discipline experts.

Facets of Learning

There are several facets to learning that relate to the many disciplines, including curriculum, skills, and non-cognitive aspects. These facets are often related. Learner's performance in science is often associated with their proficiency in Math and their non-cognitive skills. Navigator has detailed and consistent representation of the learner across all of their facets. This enables the suggestion of personalized pathways based on an increasingly complete understanding of the learner.

Big Data

Big data is captured across distributed systems and offline activities. Navigator uses xAPI records aggregated across Learning Record Stores to gather the big-data. Machine learning approaches use the xAPI data streams to curate learning activities and locating learners with increasing precision. As learners engage in learning activities, Navigator continuously improves the curation of content and location of the learner. 

Search Engine for Learning

The search engine for learning is based on the structure of the learning space, the curated catalog of learning activities, and an understanding of the user (learner or instructor ). We use all of these signals in query analysis and ranking to keep the search results pedagogically aware and personalized.

Operationalizing Science

Navigator encodes Event, Condition, Principles of learning, and Action (ECpA) as a set of models. These models trigger a ranked list of suggested actions based on events, learner conditions, and principles of learning. By operationalizing the learning principles with big data, Navigator ensures the suggestions offered to learners and their instructors are backed by learning theories and science.

Research Collaborators

Tanya Dewey, PhD

Research Scientist, Colorado State University; Director, Animal Diversity Web      

Neal Finkelstein, PhD

Director, Innovation Studies program, WestEd  

Kenji Hakuta, PhD

Professor Emeritus, Graduate School of Education, Stanford University

Youngmoo Kim, PhD

Director, Expressive and Creative Interaction Technologies (ExCITe) Center; Professor, Electrical and Computer Engineering, Drexel University

Anjini Kochar, PhD

Director, India Programme at the Stanford Centre for International Development (SCID), Stanford University

Zachary Pardos

Assistant Professor, Graduate School of Education and School of Information, UC Berkeley

Vasile Rus, PhD

Professor, Computer Science, University of Memphis

Teomara (Teya) Rutherford, PhD

Assistant Professor, School of Education, University of Delaware 

Nancy Butler Songer, PhD

Fulbright Scholar; Distinguished Professor, School of Education, Drexel University 

Srinath Srinivasa, PhD

Professor and Dean (R&D), Web Science Lab, International Institute of Information Technology, Bangalore (IIIT-B)

Lav Varshney, PhD

Assistant Professor, Electrical and Computer Engineering, University of Illinois, Urbana-Champaign

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).