Gooru is a learning environment modeled as a social machine. It acts as a learning navigator. Any learning experience is in the form of a navigation across the “learning map” using “competencies”. Competencies are made up of two dimensions; namely pedagogic depth and topic. In order to learn a particular topic or to reach a target competency, Gooru provides the learner with an optimal learning route. The skyline of a learner is the set of the highest achieved competencies in every topic. Every time the learner learns a new topic or achieves a new competency, his skyline is updated. This gives the learner his position on the learning map. The learner skyline is used to compute the route the learner has to take to reach a target competency.

  • Project Members

Machine Classification of Learning Resource

learning goals based on their competency level. Every competency level is associated with many learning resources manually assigned by teachers. In this project, we have developed a classifier to automatically map a learning resource to set of competencies. This helps in providing similar resources to students in a particular lesson.

  • Project Members
    • Ashish Kumar Pani
    • Prajith Kumar Chilummula
    • Shankul Jain


Gooru honors the human right to education by creating technology that enables educators and researchers to “open-source” effective practices and content to improve learning outcomes for all.

The Gooru model of learning is a Computer Aided Instruction (CAI) environment that aims to provide effective, individualized learning experiences at scale, by utilizing both adaptive tutoring as well as community interactions. User interaction is modeled around a “Navigation” paradigm, where the subject matter is represented as learning activities scattered across a logical space called the “learning map.” Lessons are modeled as learning pathways in this logical space. The logical space metaphor enables the student to obtain different levels of overview of the subject matter by perusing the landscape, and understand semantic relatedness of topics based on their visual proximity. The learning map is also a social space, where students interact with other learners (other students, human tutors and teachers), rather than only an algorithmic agent.

The underlying paradigm of such a system is called a social machine. The social machine approach is characteristically different from both scalable classroom models like MOOCs and personalized learning environments like Intelligent Tutoring Systems

Sub Projects

Narrative Arc

This project aims to compute Narrative Arc for the learning activities in Gooru. The Narrative Arc refers to the semantic structure of a sequence of learning activities suggested to the user for learning. The suggested sequence of learning activities that are presented to the user need to result in a coherent narrative and it should be engaging to the user for a greater learning experience. A Narrative Arc hence comprises of two elements: Semantic coherence and Cognitive engagement.

Co-creation of a Center of Excellence in Big Data Engineering

This project aims to establish a collaboration between the International Institute of Information Technology (IIIT-B), with City University London, as the UK partner and Siemens Research, India, as the industry partner, to set up a centre of excellence in Big Data Engineering. With emerging trends like Web Science and the Internet of Things, expertise in Big Data is going to be in high demand in the future. As part of our initiatives to create a talent pool of research and engineering expertise, IIIT-B has collaborated with several partners in this area on specific projects. This project aims to consolidate our disparate activities in this area and create a Centre of Excellence in Big Data Engineering. The term “Big Data” is defined here to mean any kind of data management problem for which, conventional RDBMS based solutions are inadequate. The “Big” refers to not just the volume of data, but also challenges concerning variety, veracity and velocity of the data.


  • Dr. Srinath Srinivasa IIIT Bangalore
  •  Prof Muttukrishnan Rajarajan, City University, Northampton Square , London,
    United Kingdom
  • Dr. Amarnath Bose, Siemens Technology and Service, Bangalore
  • Praseeda , Research Scholar, IIIT Bangalore
  • Anish, Mtech. Thesis Student, IIIT Bangalore


  • A two day workshop on Big Data on April 18th and 19th at IIIT Bangalore

Associated Project




Sandesh is Semantic Data Mesh for publishing of Knowledge aggregated from Indian Open Data. Open structured data is published by several agencies like World Health Organization (WHO), United Nations Organization (UNO), private firms, NGOs, governmental bodies etc. Government of India publishes open data on its data portal called To aggregate and integrate data from disparate datasets,  a framework called Many Worlds on a Frame (MWF) is proposed. The framework is partially implemented in software called RootSet on top of which, the module Sandesh is implemented.

Characterizing the online social cognition as a Marketplace of Opinions

Abstract: Social media is that part of the web, where social cognition is molded on a daily basis. To understand how our society is transforming under social media, we need abstract models to characterize the dynamics of online social cognition. Social media is more than just a platform for expression. It is a platform for engagement and persuasion — where different vested interests actively seek to engage with others and propagate their opinions or viewpoints so that it yields them concrete returns. In other words, it is a “Marketplace of Opinions,” where opinion itself is the currency of trade. This thesis aims to propose a working model of social media dynamics as an opinion marketplace. We define an opinion as a complex object comprising of “abstractive” and “expressive” elements. Based on literature from social psychology and network theory, we model the communication, propagation, calibration and assimilation of opinions on social media. A preliminary milestone that has been achieved, is to characterize a trending topic on social media by the following: the disparate opinions that characterize the trend, and the primary user accounts driving these disparate opinions.

Our understanding of web has been evolving from that of a passive repository to a participatory socio-cognitive space, where human beings are participants rather than users of it. To be able to perceive how the society is transforming, it is very important to understand how the web is impacting the social cognition. The socio-cognitive space is providing a common global platform where each individual can express his/her viewpoints and can be heard by others. The socio-cognitive space is acting as a Marketplace of Opinions, where ‘opinion’ is the currency and all the participants invest their opinions on the marketplace to get certain returns. We are trying to understand how this opinion marketplace is shaping or transforming the social cognition of the society in various ways. As a part of this bigger domain of interest, one of the specific problem we are addressing is to identify the opinion `drivers’ on the social media, who have an intention to steer the topic of discussion to a particular direction.


  • Demonstration:
  • Current Project Members
  • Previous Project Members
  • Publication
    • Anish Bhanushali, Raksha Pavagada Subbanarasimha, and Srinath Srinivasa. 2017. Identifying Opinion Drivers on Social Media. In On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II. Springer International Publishing, Cham, 242–253.
    • Raksha Pavagada Subbanarasimha, Lokesh Todwal, Mamillapalli Rachana, Aditya Naidu, and Srinath Srinivasa. 2018. Mithya: A Framework For Identifying Opinion Drivers On Social Media. In
      Proceedings of ACM IKDD Conference on Data Science and International Conference on Management of Data, Goa, India, Jan 2018 (CODS-COMAD 2018)(To Appear).


Sponsors and Collaborators: Horizon 2020, European Commission

Time Frame: Jan 2016 – Dec 2020

Status – Active

The REACH project aims to develop solution to avail the provision for high speed Internet access in rural India using unlicensed TV white space spectrum and designing the Geolocation database for it. With the wide increase of population and use of Internet in India, the efficient utilization and management of spectrum is needed. The utilization of TV white space spectrum is emerging as a best alternative to fulfill this need since there are many unused channel in TV spectrum due to migration from analog to digital transmission technology.

At IIIT-B, we are working on Distributed Algorithms for Spectrum Assignment for White Space Devices. Spectrum assignment for devices in white-space spectrum is challenging due to the fact that, white-space spectrum has temporal and spatial variations and is most often fragmented. We have created an autonomous agent model for spectrum assignment of white space devices at a given location. Each white space device (WSD) acts autonomously out of self-interest, choosing a strategy from its bag of strategies. It obtains a payoff based on its choice and choices made by all other agents. WSDs interact with each other using a central shared memory located at a “Master” device. Based on payoffs received by different strategies, WSDs evolve their strategic profile over time. This has the effect of “demographic changes” in the population. The system is said to have reached a state of equilibrium (or, in a state of evolutionary best-response) when the demographic profile stabilises. The system is trained on different load profiles to compute their respective evolutionary best responses.

Project Outcomes

  1. Chaitali Diwan. Autonomous Spectrum Assignment of White Space devices. MTech thesis. June 2016.
  2. Chaitali Diwan, Srinath Srinivasa, Bala Murali Krishna. Autonomous Spectrum Assignment of White Space Devices. Proceedings of the 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks. Lisbon, Portugal. September 2017.
  3. Simulation dashboard for autonomous spectrum allocation algorithm

Other Relevant Links