Harnessing Learners' Perceptual Load, the Attentional States, and Interactions in Digital Learning Environments

Abstract

Learning effectiveness is highly subjective. It depends on how well an individual gets involved with learning: being attentive and engaged. It has been looked into from many different aspects: educational, psychological, and technological. Yet, we could not find appropriate mechanisms to perform an in-situ assessment of the attentional state of a learner. To address this deficiency, we initiate a blended approach that brings together the educational, psychological, and technological aspects related to attention in digital learning environments. We propose to predict the attentional state of a leaner by analyzing user interactions and the perceptual load presented by the learning activity. We suggest two ways to use these predictions: (1) Notify the user about wavering of attention so that the learner will be able to enhance his engagement with the learning activity (2) As an insight to the learning activity if many learners engaged with the activity find it difficult to focus on. This work is expected to make a positive impact by enhancing effectiveness in teaching and learning in digital learning environments.