2026 ASEE Annual Conference & Exposition

Beyond Examinations: A Multi-modal Approach to Measure Learning Through Physiological Biomarkers

Presented at Computers in Education (CoED): Learning, Engagement & Inclusion (7 of 9) -- T508B

Research has shown that traditional student assessment methods, ranging from subjective essays to standardized tests, face notable limitations as they can induce stress and may be susceptible to plagiarism, particularly with the rise of generative AI tools. Thus, there is a growing need for real-time measures of learning that not only reduce student stress but also efficiently detect genuine cognitive engagement as markers of learning. By leveraging the recent advances in bio-sensing, signal processing, and machine learning, we aim to uncover physiological signatures that may correlate with learning. This fusion of sensor modalities represents a departure from reliance on verbal or written student outputs, offering a potential window into learning processes in real time. In this pilot study (n=6), eye-tracking features often showed larger effect-size correlations with learning gains than facial-expression or ECG features; however, no single biomarker remained significant after correcting for multiple comparisons. The most robust result was a cross-modal interaction in which focused medium-duration, low-dispersion fixations interacted with heart-rate variability (RMSSD) to predict learning gain (FDR q < 0.05). These results are exploratory and motivate larger studies to validate single-feature and cross-modal biomarkers.This exploratory work examines assumptions about education assessments, shifting the focus from assessing learning using traditional methods that measure the current state of knowledge through oral or written submissions to capturing the change in knowledge (delta) in the student during the course intervention. These preliminary findings suggest that real-time physiological biomarkers may open up new avenues for personalized education, though further research with larger samples is needed to validate these initial observations.

Authors

Dr. Brainerd Prince Plaksha University [biography]

Brainerd Prince is the Associate Professor and the Director of the Centre for Thinking, Language and Communication at Plaksha University. He teaches courses such as Reimagining Technology and Society, Ethics of Technological Innovation, and Art of Thinking for undergraduate engineering students and Research Design for PhD scholars. He completed his PhD on Sri Aurobindo’s Integral Philosophy from OCMS, Oxford – Middlesex University, London. He was formerly a Research Tutor at OCMS, Oxford, and formerly a Research Fellow at the Oxford Centre for Hindu Studies, a Recognized Independent Centre of Oxford University. He is also the Founding Director of Samvada International Research Institute which offers consultancy services to institutions of research and higher education around the world on designing research tracks, research teaching and research projects. His first book The Integral Philosophy of Aurobindo: Hermeneutics and the Study of Religion was published by Routledge, Oxon in 2017. For more information, please visit: https://plaksha.edu.in/faculty-details/dr-brainerd-prince
Dr. SIDDHARTH SIDDHARTH Plaksha University [biography]

Dr. Siddharth leads the Human-Technology Interaction Lab at Plaksha University. He recently came to Plaksha from the Human Factors Center of Excellence at Microsoft USA where he worked as Senior Human Factors Researcher. He is passionate about working on research themes with practical applications in the technology industry.

At Plaksha, Dr. Siddharth is investigating and modeling human physiological responses to improve cognitive well-being, and enhance human safety, productivity, and creativity. By integrating domain-specific knowledge from the fields of signal processing, computational neuroscience, computer vision, and machine learning, his research group is utilizing human physiological responses to track various cognitive states such as emotions, stress, awareness, mental load, and more. His research and recommendations have practical applications in the technology industry for the development of safer and more user-centered designs that promote cognitive well-being.

In the past, Dr. Siddharth led the Microsoft Human Factors Center of Excellence’s efforts to design personalized user experiences through neurophysiological insights into users' preferences enabling them to be more productive and establish meaningful connections with each other. He has also worked at Facebook Reality Labs, Samsung Research America, French National Center for Scientific Research (CNRS), and National University of Singapore. His research has been awarded fellowships and research grants from various institutions including the National Science Foundation, Army Research Lab, and the Kavli Institute of Brain and Mind. It has also been published in many international conferences and reputed journals including IEEE Transactions and Nature Scientific Reports and has spawned multiple patents.

Dr. Siddharth received his Ph.D. from the University of California San Diego where he also served as the Graduate Fellow Editor of the Prospect Journal of International Affairs and Graduate Student Member of the South Asia Initiative. His first book, Founding Generations: Democracy's Origins and Parallels in America and India, was released recently. For more up-to-date information about him, please visit https://ssiddharth.in/about-me
Mr. Utkarsh Agarwal Plaksha University [biography]

Utkarsh Agarwal is an undergraduate engineering student pursuing a B.Tech in Computer Science and Artificial Intelligence at Plaksha University. His academic interests lie at the intersection of multimodal systems, human cognition, and applied machine learning, with a particular focus on leveraging sensor data and physiological signals to better understand learning processes. In this work, we explore a multimodal approach to assessing examinations by integrating behavioral and physiological markers, aiming to move beyond traditional evaluation methods toward more holistic and data-driven measures of learning.
Mr. Yash Sangtani Plaksha University [biography]

Yash Sangtani is a final-year undergraduate student in Computer Science and Artificial Intelligence at Plaksha University, building data-driven solutions at the intersection of machine learning, systems thinking, and real-world impact. His work spans reinforcement learning, predictive modeling, and LLM-powered pipelines — from training autonomous racing agents on AWS DeepRacer, to developing ML models that predict stubble-burning incidents and agricultural yield from satellite imagery. He has also applied ML to sports analytics, modeling expected possession value in basketball to extract actionable insights from in-game dynamics.

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