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.

Highlights

  • Attention is the key to successful learning (Hanley et al, 2017)

  • Negative moods cause reduced attention (Mark et al, 2016c), while students with less sleep could become more distractible, both internally and externally. Smartphone overuse is another significant reason for attention deficits and sleep deprivations among college students (Lee et al, 2014)

  • The data collected during experiments will be analyzed and a model will be developed to predict the attentional state of a learner based on the interactions and the perceptual load

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Summary

INTRODUCTION

Attention is the key to successful learning (Hanley et al, 2017). In line with the advancements in technologies and the proliferation of digital devices, learning has undergone a lot of changes. We witness diversified learning environments in addition to traditional classrooms including, computer-assisted classrooms, smart classrooms, eLearning, mobile learning, and massive open and online courses (MOOCs). Whether these environments are conducive for learners to direct and maintain attention in learning is still questionable. Too much exposure to digital media causes learners to multitask more (Foehr, 2003), causing fragmented attention which impacts negatively on learning. We provide a summary of related work, which will be followed by the Conclusion section, we appraise the proposed approach and highlight the expected contributions. Harnessing Learners’ Perceptual Load, the Attentional States and Interactions in Digital Learning Environments Indika Karunaratne

BACKGROUND
PROPOSED APPROACH
Experimental Design
Proposed System Architecture
Evaluation of the Model
Effects and Impact of Interruptions and Multitasking
Technological Interventions in Capturing and Maintaining Attention
The Task
CONCLUSION

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