Major Research
Cognitive Domains and Well Being
Singapore edecation academy asia pacific pte ltd
This domain includes the development of Working Memory, Episodic Memory, Executive Function, Attention, Processimg Time, etc.
This research focuses on Cognitive skills mastery and Lifelong learning skills like Critical Thinking, Problem Solving Approach, Mental Maths, Creativity, etc. Development in this area fosters self-confidence, personal & professional skills, and most important builds intrinsic motivation and helps in deciding long terms goals.
Master's Thesis
Indian institute of technology, Bombay (IIT B)
"An eye-tracking study to facilitate learning by automatically investigating negative emotions and informing the instructional strategies based on predicted learning outcomes"
Eye-Tracking technology: The application of eye-tracking technology in the field of education has a great potential to promote the application of technology and to improve educational standards.
Guide: Prof. Ritayan Mitra
My Master's projects have two major components: 'Refine the Phenomenological Base model (presented by Negi. S. & Mitra. R., 2020).' The model uses discontinuous fixation ranges to predict learning outcomes. Another is to 'check whether the relationship between eye gaze data and reported emotions exist?'. For this, I have conducted a study and collected eye gaze data using imotions software. The analysis of this data also helped me investigate the learners' Cognition and Affect at different stages during a learning task. It also revealed a correlation between learning media (Text-based, Pictorial, Subtitle, etc.) and scaffolding with learners' emotions. This entire study will scaffold in an automatic investigation of emotions and inform instructional strategies based on detected emotions (bored, stressed).
Abstract and Data Sample
Eye-tracking has been particularly useful in the study of learning processes, especially to identify cognitive and less commonly affective states of students. Average eye fixation is an important eye-tracking metric. Negi and Mitra, 2020, highlighted some of the issues associated with this metric and proposed a phenomenological eye fixation model to better study learning processes in general. This M.Tech project aims to refine and validate that model. Chapter 1 situates the thesis and provides the necessary background and information. Chapter 2 includes a brief literature review on the role of affective states in learning and the role of eye tracking in affective computing. Chapter 3 discusses one of the two primary goals of this project, namely, refinement of the regression model proposed by Negi and Mitra 2020. Refinement of the model was tried by incorporating saccade information in the pre-existing model. This did not lead to better model results indicating saccade information was not critical to the model. Chapter 4 discusses the second goal of this project, namely, validation of the phenomenological model. This objective was carried out in two ways. First, previously published data were reanalyzed to reproduce a time series of fixation. This data was correlated with the post-hoc appreciation of lecture content. Second, a new study was designed to understand the explanatory power of eye fixations for self-reported affective states. The results were mixed and are discussed in detail. Chapter 5 concludes the thesis and suggests further work.
Literature Review Sample
