Math and Science
of Navigated Learning

Progression space of competencies to locate learners, Polyline algebra to navigate learners, use principles of learning to suggest personalized pathways

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Partnering with Leading Researchers

The NLC establishes partnerships with leading researchers in data science, learning science, psychology, and teacher education from around the world. We innovate Navigator to incorporate the science of learning and evaluate the efficacy of learning. We publish in peer-reviewed journals and present at academic conferences.

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" (metric space of competencies with partial ordering of dependencies) of competencies that we refer to as the Navigator Competency Framework (NCF). A polyline across domains of competencies for each subject represents a learner's proficiency that we call their "skyline." NCF includes a polyline algebra to compute measures such as route, reroute, and mean-time-to-learn. The NCF 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 NCF 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 (LRS) 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 teacher). 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.

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).

The Researchers Behind Navigator

Nancy Butler Songer, PhD

Nancy Butler Songer is a Distinguished University Professor and Dean of the School of Education at Drexel University. Dr. Songer is an expert in science education and learning technologies with particular emphasis on the assessment of science knowledge and the design of learning environments that emphasize critical thinking in science.

Srinath Srinivasa, PhD

Srinath Srinivasa is Dean of Research and Professor of Computer Science at International Institute of Information Technology at Bangalore (IIIT-B). He works in the broad areas of web information retrieval, multi-agent systems, network analysis and text mining. He is a member of various technical and organizational committees for international conferences. He has served and continues to serve as a technical reviewer for journals like the VLDB journal and IEEE TKDE.

Teomara (Teya) Rutherford, PhD

Dr. Teomara (Teya) Rutherford at the University of Delaware is working to understand how motivation and self-regulation feature in student's choices within Math Navigator and how their choices illustrate their strengths and area of improvement in social-emotional learning to provide actionable insights for teachers.

Kenji Hakuta, PhD

Kenji Hakuta is an Emeritus Professor in the Graduate School of Education at Stanford University. His areas of teaching and research are bilingualism, second language acquisition, education policy and social and experimental research.

Lav Varshney, PhD

Lav Varshney is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. He is interested in analyzing socio-technical information systems through data analytics.

Youngmoo Kim, PhD

Dr. Youngmoo Kim is Director of the Expressive and Creative Interaction Technologies (ExCITe) Center and Professor of Electrical and Computer Engineering at Drexel University. He is interested in human-machine interfaces and robotics for expressive interaction, analysis-synthesis of sound, and K-12 outreach for engineering, science, and mathematics education.

Neal Finkelstein, PhD

Dr. Neal Finkelstein serves as Co-Director of WestEd's Innovation Studies program. He is responsible for developing research and evaluation designs that study the impact of program implementation in K–12 public schools.

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.