VisQuelle: Visual Question based Elementary Learning Companion – A System to Facilitate Learning Word Object Associations 
Sandhya Vidyashankar1, Rakshit Vahi2, Yash Karkhanis3, Gowri Srinivasa4
1Sandhya Vidyashankar*, PES Center for Pattern Recognition, PES University, Bengaluru, India and Department of Mechanical Engineering, SSN College of Engineering, Chennai (Tamil Nadu), India.
2Rakshit Vahi, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
3Yash Karkhanis, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
4Gowri Srinivasa, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
Manuscript received on November 15, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 41-49 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95991111121 | DOI: 10.35940/ijitee.A9599.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We present an automated, visual question answering based companion – Vis Quelle – to facilitate elementary learning of word-object associations. In particular, we attempt to harness the power of machine learning models for object recognition and the understanding of combined processing of images and text data from visual-question answering to provide variety and nuance in the images associated with letters or words presented to the elementary learner. We incorporate elements such as gamification to motivate the learner by recording scores, errors, etc., to track the learner’s progress. Translation is also provided to reinforce word-object associations in the user’s native tongue, if the learner is using Vis Quelle to learn a second language.
Keywords: Visual question answering; object recognition; question generation; question answering; word-object association.
Scope of the Article: Visual Analytics