Understanding how learner interactions shape collective learning outcomes

Learning is often viewed as an individual process, where success depends primarily on the learner and the content they consume. However, decades of research suggest that learning is also deeply social. Learners influence one another through discussion, collaboration, feedback, and shared experiences.

This project explores how interactions between learners shape collective learning outcomes. By combining ideas from learning sciences and network science, it investigates how learning communities evolve and how patterns of coordination emerge through repeated interaction.

Learning as a Network

The project models a learning community as a network, where learners are represented as nodes and interactions between learners form the connections.

This perspective shifts the focus from individual learning trajectories to the behavior of the community as a whole. It allows us to study how information flows, how learners influence one another, and how collective outcomes emerge over time.

Social Synchrony

One of the central ideas explored in this work is social synchrony—the tendency of learners to gradually align in capability and progress through interaction.

As learners exchange knowledge and experiences, differences within the community can reduce, resulting in more coordinated learning behavior and stronger collective outcomes.

The concept draws inspiration from foundational work in social learning, sociocultural learning, communities of practice, and networked learning.

Key Findings

The study demonstrates that learner interactions play an important role in shaping collective outcomes. Across simulated learning communities, repeated interaction was associated with the emergence of synchrony and reduced capability disparities among learners.

These findings suggest that understanding learner-to-learner relationships may be as important as understanding learner-to-content relationships when designing future learning systems.

Research Outputs

Publication

Modeling Outcomes-led Learner Behavior and Emergent Social Synchrony Ashashree Sarma, Sushree Behera, Srinath Srinivasa, and Prasad Ram. Proceedings of the Twelfth ACM Conference on Learning @ Scale (L@S 2025), Palermo, Italy. DOI: 10.1145/3698205.3733959

https://dl.acm.org/doi/abs/10.1145/3698205.3733959

Poster Presentations

• RISE 2025
• L@S & EDM 2025
• WebSciX 2025

Media Coverage

Google Maps for Learning: IIIT-B Researchers Develop AI Navigator for Personalised STEM Learning https://www.thehindu.com/news/national/karnataka/google-maps-for-learning-iiit-b-researchers-develop-ai-navigator-for-personalised-stem-learning/article70089077.ece

Featured in The Hindu.

Foundations

This work draws inspiration from:

• Albert Bandura’s Social Learning Theory
• Lev Vygotsky’s Sociocultural Theory
• Etienne Wenger’s Communities of Practice
• George Siemens’ Connectivism
• Complex Systems and Network Science

People

Project Lead

Ashashree Sarma
Research Scholar, IIIT Bangalore
Research Profile: https://wsl.iiitb.ac.in/ashashree-sarma/
LinkedIn: https://www.linkedin.com/in/ashashree17321/

Supervisors

Prof. Srinath Srinivasa
Professor, IIIT Bangalore
Profile: https://wsl.iiitb.ac.in/srinath-srinivasa/

Prof. Sushree Sangeeta Behera
Faculty, IIIT Bangalore
Profile: https://wsl.iiitb.ac.in/sushree-behera/

Traditional education often treats learning as a straight line—a rigid checklist of lectures and exams that assumes every student progresses at the exact same pace. But in reality, true human learning is non-linear, unpredictable, and deeply personal, filled with sudden cognitive leaps and variable moments of assimilation. To capture this dynamic reality, my research introduces a data-driven framework centered around the Active Learning Map.

Instead of tracking simple grades, this system maps a student’s evolving knowledge state as a continuous trajectory through a high-dimensional vector space called Polylines. Using a radial mapping algorithm, these complex mathematical dimensions are compressed into an intuitive, two-dimensional coordinate system where each fundamental course topic radiates as its own axis from a central origin.

By integrating a Conditional Generative Adversarial Network (cGAN) to simulate how a student’s knowledge profile changes when they interact with a resource, we’ve built a highly accurate transition state model. Navigating this space is an intelligent, model-free Deep Q-Network (DQN) recommendation engine. Operating much like a GPS for education, the AI agent treats curriculum routing as a shortest-path problem, dynamically discovering personalized, accelerated learning trajectories to close the knowledge gap and guide students toward mastery in the fewest possible steps. By turning abstract cognitive growth into a visually interpretable and traversable landscape, the Active Learning Map lays the groundwork for a truly adaptive, outcome-centric classroom.

Unlocking Mobility to Improve Urban Life in Bengaluru

Bengaluru faces one of the most severe traffic congestion challenges in India. Rapid population growth, economic expansion and increasing vehicle ownership have placed immense pressure on the city’s transport infrastructure. Daily gridlock has become a defining feature of urban life, leading to longer commute times, higher air pollution, reduced productivity, and a declining quality of urban life.

Recognizing that conventional solutions such as road widening and flyovers cannot resolve these systemic challenges, Bengaluru requires a more integrated, forward-looking approach to mobility one that goes beyond city limits and addresses regional travel patterns.

Rethinking Mobility for a Growing Megaregion

To respond to this challenge, we are developing a transformative initiative:

Project Title: Multi-Modal Mobility Solutions for Megaregions around Bengaluru

Once known as the Garden City and now India’s technology capital, Bengaluru has grown rapidly over the past five decades. While this growth has brought innovation, talent, and economic opportunity, it has also resulted in stressed infrastructure, fragmented transit systems, congestion, climate pressures, and limited freight capacity. Addressing these challenges requires moving beyond the scale of a single city.

Across Karnataka, population and economic activity are spreading unevenly, with new growth centers emerging beyond Bengaluru’s urban core. In this context, megaregional planning and multi-modal mobility offer a new paradigm for sustainable development.

What Is a Megaregion?

A megaregion is a large, networked geographic area that extends beyond individual cities and administrative boundaries. It is formed by multiple interconnected growth centers that are economically and functionally interdependent, collectively driving regional and national development.

The Bengaluru megaregion extends well beyond the city’s current periphery, encompassing emerging centers, each with distinct strengths and development potential. As growth increasingly clusters across these locations, mobility planning must evolve to treat the region as a single, integrated system.

Data-Driven Planning for Integrated Connectivity

Understanding travel demand, growth corridors, and inter-city linkages enables smarter, more resilient mobility planning. Improved regional connectivity through suburban rail, metro systems, highways, regional rapid transit systems, high-speed rail proposals (such as the Chennai–Mysuru corridor), and shared mobility can activate new economic nodes, strengthen rural–urban integration, and distribute opportunities more evenly across the state.

This project leverages satellite imagery, geospatial analytics, night-time lights data, transport network modeling, and real-time mobility insights to delineate megaregional boundaries, anticipate future growth patterns, identify strategic mobility corridors, and propose integrated, multi-modal transport solutions.

By enabling efficient, low-carbon movement of people and goods, a balanced network of cities can reduce migration pressures on Bengaluru, ease congestion, and support more livable, resilient communities across megaregion.

Aligning with National and Regional Vision

This initiative aligns closely with Viksit Bharat 2047, supporting sustainable, inclusive, and intelligent urban and regional development. Megaregional planning opens new pathways for investment, industry, innovation, and equitable access to opportunity.

Stronger connectivity between Bengaluru and small and medium towns can foster diversified economic growth, reduce pressure on the capital, and enhance quality of life across the region. The study of megaregional mobility is more than a research effort, it is a long-term vision for balanced, sustainable growth.

The study of megaregional mobility is enabled by Bengaluru Science and technology (BeST) Cluster (https://www.bestkc.in/), an initiative by the Office of the Principal Scientific Adviser to the government of India.

Parichaya project aims at capturing indigenous oral traditional knowledge about sandalwood in rural communities and making it available through an interface that can enable users to interact with audio content to support broader cultural awareness, decision-making, cultivation practices and promote community involvement.

Objectives

Sandalwood plays an important role in Indian cultural, religious, and therapeutic practices. It is extremely important to capture relevant indigenous knowledge in rural communities about the tree from aging populations, support preservation and renew cultivation efforts. In addition to this, the verbal knowledge transferred through multiple generations in rural communities is largely uncodified. The project aims at shaping initial ontologies for a knowledge base about sandalwood.

The Parichaya application contains two interfaces;

1. The first interface enables browsing the content using frequent keywords and their context words, representing critical aspects of information in the corpus and providing a good viewpoint of the content
2. The second interface supports question-answering, where a user can post a question, get the summary answer, and listen to the audio contents with answers to the question.

Funding Agency

Mphasis F1 foundation

Demos

Parichaya interface : http://103.156.19.244:33404/
(username: guest, password: guest123)

Parichaya demo video :

Publications

Sharath Srivatsa, Aparna M, Samarth P, Malavika V, and Srinath Srinivasa. 2025. Parichaya: Rural Colloquial Knowledge AI Interface. In Proceedings of the 8th Joint International Conference on Data Science & Management of Data (12th ACM IKDD CODS and 30th COMAD) (CODS-COMAD ’25). Association for Computing Machinery, New York, NY, USA. [to appear]

The primary aim of our work is to develop a formal framework to manage consent for open-ended data sharing use cases like Digital Public Infrastructures (DPIs). Our framework delves into philosophical, legal and technical aspects of consent to ensure a holistic solution. In this regard, we propose policy-based consent management which resolves consent across 4 key dimensions – ownership, policy compliance, access-control and post-access conditions. The prototype of the work can found at Anumati: Consent Management Service.

Other important aspects of the work include developing an architecture for cross-border data flows in DPIs , developing a consent ontology to understand meaningful consent policies.

Funding Agency

Centre of Data for Public Good (CDPG), IISc

Publications

1. Ayappane, Balambiga, et al. “Extensible Consent Management Architectures for Data Trusts.” arXiv preprint arXiv:2309.16789 (2023).
2. Rohith Vaidyanathan, Srinath Srinivasa, Praseeda, Dev Shinde. Ownership and Flow Primitives for Scalable Consent Management in Digital Public Infrastructures. ArXiv pre-print 2511.02950 
3. Balambiga Ayappane, Rohith Vaidyanathan, Srinath Srinivasa, Santosh Kumar Upadhyaya, and Srinivas Vivek. 2024. Consent Service Architecture for Policy-Based Consent Management in Data Trusts. In 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (CODS-COMAD 2024), January 04–07, 2024, Bangalore, India. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3632410
4. Asilata Karandikar. 2024. What Makes Consent Meaningful? In Companion Publication of the 16th ACM Web Science Conference (Websci Companion ’24). Association for Computing Machinery, New York, NY, USA, 42–46. https://doi.org/10.1145/3630744.3658616

Team Details

1. Prof. Srinath Srinivasa (PI)
2. Balambiga Ayappane (Phd Scholar) 
3. Dev Shinde (MS Scholar)
4. Asilata Karandikar (collaborating PhD Scholar) 
5. Sachin Poojary (Developer)
6. Meghana TM (Developer)
7. Prof. V Sridhar (collaborator) 
8. Prof. Srinivas Vivek (collaborator)