Precision Learning for Enterprise


Organizational learning is fast becoming an integral element of the daily operations of organizations given the pace of change in technology and market dynamics. Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Organisational learning is an essential element for the survival of any organisation in this fast paced technological and business landscape.

A concept of “learning organizations” has been a focus of enormous research and managerial interest since the early 1990s. Peter Senge, identifies five key disciplines for organizational learning:

  1. Systems Thinking
  2. Shared Vision
  3. Mental Models
  4. Team Learning
  5. Personal Mastery

While the concept of learning organizations elicited enormous interest in its early years, the above principles of learning organizations were found to be very challenging to manage, in an environment of fast changes and churn in the workplace.

In recent times, a new paradigm called precision learning is gaining a lot of research attention. Precision learning entails the use of “Big Data” techniques to identify expertise and knowledge gaps in a given community or organization, and mediate between them. The result is a continuous, on-going process of learning catering to a population of diverse interests, skills and learning goals.

Navigated Learning is a precision learning model being developed at the Web Science lab at IIIT Bangalore. Navigated learning implements precision learning, by computing a variety of semantic embeddings. This project explores Navigated Learning models for enterprise settings.

Expected Social Impact:

Navigated learning is part of the research focus called “Digital Empowerment” being pursued by the Web Science lab. The underlying social challenge being addressed is the need for informal and continuous upskilling required for overall social empowerment. Current models of technology enabled learning have not gained enough engagement and traction from users to pursue their upskilling activities in a sustained manner. One of the reasons we believe for this, is the dearth of context, relevance and social support during the learning process. Navigated learning is expected to cater to this need, and we hope that it becomes as popular as, and an empowering alternative to streaming video and gaming apps.

Since this is under applied research in cognitive computing “Precision Learning for Enterprise portal” will be delivered in which the concept of Navigated Learning is applied.

This project is sponsored by Mphasis under the Mphasis Center of Excellence for Cognitive Computing.

Team Members:

Dr Aparna Lalingkar, Postdoctorate Research Fellow

Mr Prakhar Mishra, MS Research Scholar

Mr Shyam Kumar VN, MS Research Scholar

Current Members:

Jagatdeep Pattanaik, MTech Student

Puneeth Sarma, IMTech Student

Rohit Katlaa, IMTech Student 

Narrative Arc for Effective Learning

Narrative Arc is one of the research projects under the umbrella of Navigated Learning project at Gooru Labs, IIITB.

The Narrative Arc refers to presenting the sequence of learning activities as a narrative to the learner to make learning interesting and to help the learner navigate seamlessly through the learning space. The project has two parts: First is creating the learning pathways automatically given a corpus of learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. Second part is modelling an AI-based automatic conversational agent which makes the learning pathway interesting and adapts the learning pathways according to the users knowledge and preferences. Here, the learning pathway is first presented to the user according to her learning goal, then the conversation agent interacts with the learner to keep the user interested in the learning pathway and to augment her knowledge. The agent also gauges the knowledge of the learner and supports the learner by providing knowledge and if required re-route the learner through a different learning pathway.

Following link has the presentation for the project in RISE 2019 workshop held at IIITB on 14-16 Feb 2019. The title of the presentation is “Narrative Arc Computation towards Digital Empowerment”. Narrative Arc Computation

  • Lead Researcher
  • Current Project Members
    • Mirambika Sikdar(Summer Intern)
  • Previous Project Members
    • Nikhil Bukka Sai
    • Sai Sri Harsha Vallabhuni
    • Rochan Avlur ( Intern)
    • Niharika Chaudhari (Intern)
    • Vibhav Agarwal
    • Abhiramon R
    • Sanket Kutumbe
    • Karan Kumar Gupta
    • Srinivasan P.S
  • Publications

Navigated Learning

Figure 1: Navigated Learning

Navigated Learning is a new paradigm of learning that aims to balance the three independent requirements: Scale, Personalization and Social Interactions. Please see figure 1 that shows parallels among the concepts that are technological solutions and the three requirements of learning.

This is achieved by representing learning as situated within an abstract “competency space,” and computing semantic embeddings of learning objects and learners into the competency map. The competency map is organized as a progression space– which is a metric space with a partial order. Here, not only is there a notion of “distance” between any two points, but also an element of “progress”. These embeddings can be computed for any semantic object like learning resources, activities, learners, etc. Each point in the space represents a “competency” or a demonstrable skill that can be acquired by the learner.

A primary element of research into Navigated Learning is to construct a competency map for a given subject area of study and to build semantic embedding models for different kinds of objects relevant to the learning process. Semantic embeddings may take different forms depending on the nature of the object. While some objects can be neatly represented as points in the logical space, other objects may be represented by regions, pathways or other contours in the space. In an organizational setting, objects that are embedded onto this space include not just learning resources and learners, but also departments, projects and other organizational elements that require or work with relevant skill sets represented in the competency map.

Navigated learning is manged by a “Learning Navigator” with which every learner interacts. The Learning Navigator (or just, navigator), continuously interacts with the learning map and the learner to perform the following:

Locate: Based on data about their activities and outcomes from formal assessments, the “Locate” module of the navigator embeds learners in the space, and continuously updates their location. Unlike a geographical space, a learner may have acquired several competencies in the competency space. Thus, their location is not identified by a point, but by a data structure called a Skyline, that is detailed in a later section.

Curate: Once a learner’s location is known, based on their stated goals or recently acquired competencies, a set of further candidate competencies are identified. Curating is based on competency modeling principles, that identifies complementary, supplementary and conflicting competencies.

Mediate: This is the logic by which the navigator navigates the learner by making suggestions. Mediation is based on computing an underlying “Narrative Arc” that computes a semantically coherent and meaningful learning sequence individualized for each learner. Mediation also involves suggesting connections with other learners as well as group learning activities.

This project is sponsored by Gooru Learning.

Team Members:

Dr Aparna Lalingkar (PostDoctorate Research Fellow)

Ms Chaitali Diwan (PhD Research Scholar)

Ms Praseeda Kalkur (PhD Research Scholar)

Mr Naman Churiwala (Research Associate)

Mr Prakhar Mishra (MS Research Scholar)

Mr Shyam Kumar VN (MS Research Scholar)


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 (to appear)

Aparna Lalingkar, Srinath Srinivasa, PrasadRam (2019), Characterization of Technology-based Mediations for Navigated Learning, Advanced Computing and Communications, Vol 3 (2), June 2019, ACCS  Publications, pp. 33-47. (Paper Link)

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), Valletta, Malta, October 22-26, 2018, Springer International Publishing.

Lalingkar, A.; Srinivasa, S. & Ram, P. (2018). Deriving Semantics of Learning Mediations, In Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE, pp. 54-55. (Cited by 1 as per GoogleScholar citation index) (Paper Link)

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