Sanjay Chandrasekharan

Sanjay Chandrasekharan

Associate Professor (G)

Group Leader, The Learning Sciences Research Group

Adjunct Faculty, Interdisciplinary Program in Educational Technology, Indian Institute of Technology Bombay


  • Ph.D. Cognitive Science, Carleton University, Ottawa, Canada


  • Postdoctoral Fellow, School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA
  • Postdoctoral Fellow, Cognitive & Motor Neuroscience Lab, Faculty of Kinesiology, University of Calgary, Canada
  • Senior Lecturer, Centre for Behavioral and Cognitive Sciences, University of Allahabad, India
  • Predoctoral Fellow, Adaptive Behavior and Cognition Group, Max Planck Institute for Human Development, Berlin, Germany

Research Areas

  • Learning Sciences, New Computational Media, Science Cognition, Model-based Imagination and Reasoning in Science and Engineering, Philosophy of Scientific Modeling
  • Building/Making Cognition, Distributed Cognition, Embodied Cognition, Motivation, Sustainability

The LSR group's main research focus is the rapidly evolving interface between Cognitive Science, the Learning Sciences,  and Philosophy of Scientific Modeling. Based on this frontier research, the group designs and develops novel curricular frameworks, extending the capabilities of both new digital/computational media and distributed & embodied theories of cognition.

A specific objective of this design-based research is to develop curricular structures that allow science teachers, learners, and policymakers to adapt quickly to emerging model-building practices, where three different kinds of models -- simulation models, physical models, and machine learning models -- are built in tandem, to address challenging scientific problems. This melding of disparate modeling practices is part of an ongoing system-level transition, where science and engineering are coming ever closer, to create the new Engineering Sciences.

Such melding of different modeling approaches is critical to address complex interdisciplinary problems (including climate change, sustainability, pandemics, and renewable energy). Other ongoing frontier challenges (such as AI,  robotics, bio-engineering, and space travel) also require blending science and engineering modeling practices, in novel ways.

Associate Editor (2020-2022)

IEEE Transactions on Learning Technologies

Advisory Board Member

Studies in Applied Philosophy, Epistemology, and Rational Ethics


Advanced Topics in Cognition, 2023

Conceptual Blending in Science and Science Education, 2023

Cognition, Conceptual Development, and Conceptual Change, 2022

Cognitive Accounts of Modeling and Conceptual Change, 2020

Environment and Behavior, 2016

Representations, 2015

Motivation, 2015

Selected Publications

Google Scholar Page

ACM Author Page

25. Sinha, R., Swanson, H., Clarke-Midura, J., Shumway, J. F., Lee, V. R., Chandrasekharan, S. (2023). From Embodied Doing to Computational Thinking in Kindergarten: A Punctuated Motor Control Model. Proceedings of the ACM Learning, Design and Technology (LDT ’23) Conference, June 23, 2023, Evanston, IL, USA. ACM, New York, NY, USA

24. Mashood, K. K., Khosla, K., Prasad,A., Sasidevan, V., Ashefas, M., Jose, C., Chandrasekharan,S. (2022).Participatory approach to introduce computational modeling at the undergraduate level, extending existing curricula and practices: Augmenting derivations. Physical Review Physics Education Research, 18, 020136

23. Pande, P., Chandrasekharan, S. (2021).Expertise as Sensorimotor Tuning: Perceptual Navigation Patterns Mark Representational Competence in Science. Research in Science Education, 52(2), 725-747.

22. Karnam, D.P., Agrawal, H., Parte, P., Ranjan, S., Borar, P., Kurup, P., Joel, A. J., Srinivasan, P. S., Suryawanshi, U., Sule, A., & Chandrasekharan, S. (2021). Touchy-Feely Vectors: a compensatory design approach to support model-based reasoning in developing country classrooms. Journal of Computer Assisted Learning, 37(2), 446-474.

21. Chandrasekharan, S., Nersessian, N.J. (2021). Rethinking correspondence: how the process of constructing models leads to discoveries and transfer in the bioengineering sciences. Synthese, 198(21), 1-30.

20. Date, G., Dutta, D., Chandrasekharan, S.(2019).Solving for Pattern: An Ecological Approach to Reshape the Human Building Instinct. Environmental Values, 30(1), 65-92.

19. Rahaman, J., Agrawal, H., Srivastava, N., Chandrasekharan, S. (2018). Recombinant enaction: manipulatives generate new procedures in the imagination, by extending and recombining action spaces. Cognitive Science, 42(2), 370–415.

Follow-up paper, Proceedings of Cogsci 2019

18. Date, G., Chandrasekharan, S. (2017).Beyond Efficiency: Engineering for Sustainability Requires Solving for Pattern, Engineering Studies, 10(1), 12-37

17. Dutta, D., Chandrasekharan,S. (2017).Doing to being: farming actions in a community coalesce into pro-environment motivations and values. Environmental Education Research, 24(8), 1192-1210.

16. Pande, P., & Chandrasekharan, S. (2017).Representational competence: Towards a distributed and embodied cognition account. Studies in Science Education , 53(1), 1-43.

15. Chandrasekharan, S. (2016).Beyond Telling: Where New Computational Media is Taking Model-Based Reasoning. In Model-Based Reasoning in Science and Technology, Volume 27 of the series Studies in Applied Philosophy, Epistemology and Rational Ethics, pp 471-487, Springer, Heidelberg.

     14. Chandrasekharan, S., Nersessian, N.J. (2015).Building Cognition: the Construction of Computational Representations for Scientific Discovery. Cognitive Science, 39, 1727–1763.

     13. Chandrasekharan, S. (2014).Becoming Knowledge: Cognitive and Neural Mechanisms that Support Scientific Intuition In Osbeck, L., Held, B.(Eds.). Rational Intuition: Philosophical Roots, Scientific Investigations. Cambridge University Press. New York.

     12. Chandrasekharan, S. (2013).The Cognitive Science of Feynmen. Metascience, 22, 647–652

     11. *Aurigemma, J., Chandrasekharan, S., Newstetter, W., Nersessian, N.J. (2013).Turning experiments into objects: the cognitive processes involved in the design of a lab-on-a-chip device. Journal of Engineering Education, 102(1), 117-140.
    *All authors contributed equally

    10. Welsh, T. N., Wong, L., & Chandrasekharan, S. (2013).Factors that affect action possibility judgments: The assumed abilities of other people. Acta Psychologica , 143(2), 235-244.

     9. Chandrasekharan, S., Nersessian, N.J., Subramanian, V. (2012).Computational Modeling: Is this the end of thought experiments in science?.In J. Brown, M. Frappier, & L. Meynell, eds. Thought Experiments in Philosophy, Science and the Arts. London: Routledge, 239-260.

    8. Chandrasekharan, S., Tovey, M. (2012).Sum, Quorum, Tether: design principles for external representations that promote sustainability. Pragmatics and Cognition, 20 (3), 447-482.

    7. Chandrasekharan, S., Binsted, G. Ayres, F., Higgins, L., Welsh, T.N. (2012).Factors that Affect Action Possibility Judgments: Recent Experience with the Action and the Current Body State. The Quarterly Journal of Experimental Psychology, 65(5), 976-993.

    Follow-up paper, Proceedings of Cogsci 2015

    6. Villiger, M., Chandrasekharan, S., & Welsh, T. N. (2011).Activity of human motor system during action observation is modulated by object presence. Experimental Brain Research, 209(1), 85-93.

    5. Chandrasekharan, S., Mazalek, A., Chen, Y., Nitsche, M., Ranjan, A. (2010).Ideomotor Design: using common coding theory to derive novel video game interactions. Pragmatics & Cognition, 18 (2), 313-339.

    4. Chandrasekharan, S., Osbeck, L. (2010).Rethinking Situatedness: Environment Structure in the Time of the Common Code. Theory & Psychology, 20 (2), 171-207.

    3. Chandrasekharan, S. (2009).Building to discover: a common coding model. Cognitive Science, 33 (6), 1059-1086.

    2. Chandrasekharan, S., Stewart T.C. (2007).The origin of epistemic structures and proto-representations. Adaptive Behavior , 15 (3), 329-353.

    Python Code, Interactive Simulation
    Follow-up paper, arxiv
      Interactive Simulation

      1. Chandrasekharan, S. (2006).Money as Epistemic Structure  Comment on the target article "Money as tool, money as drug: The biological psychology of a strong incentive", by Stephen E. G. Lea and Paul Webley, Behavioral and Brain Sciences, 29 (2), 183-184.