EngageMe: Multimodal Analysis of Attention among Children with Attention Deficit Hyperactivity Disorder for Digital Learning

Specific Learning Disabilities (SLDs) refer to a category of developmental disorder of scholastic skills (like reading, writing, calculations, etc.), not attributable to mental retardation, neurological deficit, sensory or emotional problems [1]. The SLD conditions manifest as a deficit in processing language, spoken or written, that may manifest itself as a difficulty to comprehend, speak, read, write, spell, or to do mathematical calculations and includes such conditions as perceptual disabilities, dyslexia, dysgraphia, dyscalculia, dyspraxia, and developmental aphasia. SLD interferes with the normal learning process of the person. One-third of people with learning disabilities are estimated to also have attention-deficit hyperactivity disorder (ADHD). Further, it is estimated that nearly 5-15% of children struggle with Specific Learning disabilities (SLDs) in India [2].

The cognitive flexibility associated with SLDs can manifest itself in noteworthy talents, which include a multi-sensory lens for creative and lateral thinking, resulting in out-of-the-box solutions for problems. The untapped potential of SLDs causes high opportunity costs for the Nation’s progress. However, prevailing learning environments for SLDs create disparity in the education system, trigger divergence from the policy of ‘Learning for All (NEP-20)’, and depart from the provisions of ‘The Rights of Persons with Disabilities Act (2016)’. Feelings of isolation and a loss of interest in learning are often reflected in children with SLDs. Children with SLDs experience repeated failures and poor performance despite their continuous efforts and practice in learning [3]. At the same time, worldwide, the condition with SLDs has been exacerbated due to the COVID-19 pandemic when education delivery shifted online. According to global experts, “Future of Schools” is a hybrid model, where students will be both; on & off-campus.

Thus, strengthening online education delivery will be important and impacting. However, research has indicated that educators might not always be aware of their students’ attentional focus, and this may be particularly true for novice teachers [4]. The effort further increases when a single educator has to monitor the attention of the class at the individual level rather than the group level and across the entire class duration. Hence, technological tools that can improve and monitor the attention of children with SLDs can play a significant role in their inclusion during digital learning.

“EngageMe” aims to develop an intelligent platform that will offer personalized, monitored and evidence-based identification of attention levels among children with SLDs during digital learning. We will employ novel sensing technologies for multimodal behavioral analysis of the child’s online engagement using physiological, behavioral, and contextual information in a non-intrusive manner. Using Artificial Intelligence (AI) and Machine Learning (ML), we aim to better understand the cognitive state and affective processes behind attention and engagement during digital learning. Further, we will develop intelligent just-in-time and just-in-place interventions that can enhance the digital learning experience and better support emotional wellbeing among children with SLDs. EngageMe will help the special educators and pedagogues in reaching an objective and reliable assessment of the child’s attention level during online learning. Given the ongoing pandemic scenario, currently, we are collecting data using an online portal. One can head over to https://www.specialeduneeds.com/ to know their attention level by performing a sequence of three different simple and interactive psychological tasks. We look forward to building new collaborations with researchers, special educators, care facilities working with children with SLDs working around the country. Thanks to the support provided by iHub Anubhuti, we are excited to bring this project to fruition and look forward to enabling the untapped potential of children with SLDs.

About the Principal Investigator:
Dr. Jainendra Shukla leads the Human-Machine Interaction (HMI) Lab [https://hmi.iiitd.edu.in/] at Indraprastha Institute of Information Technology, Delhi (IIIT-Delhi). He is an Assistant Professor at the Department of Computer Science and Engineering in joint affiliation with the department of Human-Centered Design. He is also serving the Centre for Design and New Media as the head and is associated with Infosys Centre for Artificial Intelligence. He is experienced in Affective Computing, Human-Computer Interaction, and Social Robotics.

[1] Singh, S., Sawani, V., Deokate, M., Panchal, S., Subramanyam, A. A., Shah, H. R., & Kamath, R. M. (2017). Specific learning disability: A 5-year study from India. Int J Contemp Pediatr, 4(3), 863-8.
[2] Ministry of Social Justice and Empowerment. Notification, 2018, Gazette of India (Extra-Ordinary) Department of Empowerment of Persons with Disabilities (Divyangjan) 2018. Jan 4, [Last accessed on 2021 Dec 12], https://groups.google.com/g/wethepwd/c/XuRiT0VdWsg
[3] Sahu, A., Patil, V., Sagar, R., & Bhargava, R. (2019). Psychiatric comorbidities in children with specific learning disorder-mixed type: A cross-sectional study. Journal of neurosciences in rural practice, 10(4), 617.
[4] Goldberg, P., Sümer, Ö., Stürmer, K., Wagner, W., Göllner, R., Gerjets, P., ... & Trautwein, U. (2019). Attentive or not? Toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educational Psychology Review, 1-23.