Virtual reality: an aid as cognitive learning environment—a case study of Hindi language

The objective of this research is to propose a dynamic composition of behaviour-rich interactive 3D scene as a virtual environment for cognitive support using visual and linguistic analytics. Based on the constructivist theory of learning through visual cognition by Winn (A conceptual basis for educational applications of virtual reality, Technical report TR 93-9, Washington Technology University, 1993), we propose our work with a grounding that visual data are easy to comprehend for a person having linguistic learning difficulties. Virtual reality provides an environment for learners to actively pursue their knowledge needs by applying their theories in the ‘real world’. Therefore, we focus our work on generating an interactive virtual environment. It decreases cognitive load for the person with difficulties in comprehension, especially in language reading, e.g. dyslexia. Our prior work related to the proposed research is named as Preksha—an automatic Hindi text visualizer. To the best of the knowledge of the authors, Preksha is the only known visualization work for an Indian Language, viz. Hindi. Belonging to morphologically-rich and free-word order Indian languages, this work on the Hindi Language is a novel interdisciplinary approach to develop a virtual environment for cognitive support. Application of an automatic text visualization with a suitable learning paradigm in a virtual environment is another novelty of this research.

[1]  EarleyJay An efficient context-free parsing algorithm , 1970 .

[2]  RoussouMaria Learning by doing and learning through play , 2004 .

[3]  Virendrakumar C. Bhavsar,et al.  Vishit: A Visualizer for Hindi Text , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[4]  Peratham Wiriyathammabhum,et al.  Computer Vision and Natural Language Processing , 2016, ACM Comput. Surv..

[5]  Zhuowen Tu,et al.  Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  William I. Grosky,et al.  Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Copy right Idea Grou p Inc . Chapter II Bridging the Semantic Gap in Image Retrieval , 2018 .

[7]  Abhinav Gupta,et al.  Beyond Nouns and Verbs , 2009 .

[8]  Ellin Oliver Keene,et al.  Mosaic of Thought: Teaching Comprehension in a Reader's Workshop , 1997 .

[9]  Alfred Bork,et al.  Multimedia in Learning , 2001 .

[10]  Valerie J. Shute,et al.  Understanding spatial ability , 1984 .

[11]  John Funge,et al.  Cognitive modeling: knowledge, reasoning and planning for intelligent characters , 1999, SIGGRAPH.

[12]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[13]  Sabri Serkan Gulluoglu,et al.  The future Of technology based education on different branches of science: A conceptual basis for educational applications of virtual reality , 2010 .

[14]  Nadine-Estelle Kaltheuner The Virtual University. , 2018, European heart journal.

[15]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[16]  Yingxu Wang,et al.  The OAR Model for Knowledge Representation , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[17]  Ajai Kumar,et al.  Knowledge acquisition for language description from scene understanding , 2015, 2015 International Conference on Computer, Communication and Control (IC4).

[18]  Lindelani E Mnguni,et al.  The theoretical cognitive process of visualization for science education , 2014, SpringerPlus.

[19]  Aravind K. Joshi,et al.  Tree Adjunct Grammars , 1975, J. Comput. Syst. Sci..

[20]  R. Mayer,et al.  Multimedia Learning: The Promise of Multimedia Learning , 2001 .

[21]  Yi Li,et al.  Using VRML and JAVA to Build Virtual Game-Based Learning Environment for Addition and Subtraction Operation , 2006, ICWL.

[22]  Ajai Kumar,et al.  Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System , 2018, 2018 3rd International Conference for Convergence in Technology (I2CT).

[23]  Michael Zyda,et al.  Networked virtual environments - desgin and implementation , 1999 .

[24]  Yiannis Aloimonos,et al.  A Language for Human Action , 2007, Computer.

[25]  Virendrakumar C. Bhavsar,et al.  Spatial intelligence from hindi language text for scene generation , 2017, 2017 2nd International Conference for Convergence in Technology (I2CT).

[26]  Jay Earley,et al.  An efficient context-free parsing algorithm , 1970, Commun. ACM.

[27]  Timothy K. Shih,et al.  Distributed Multimedia Databases: Techniques and Applications , 2001 .

[28]  Maria Roussou,et al.  Learning by doing and learning through play: an exploration of interactivity in virtual environments for children , 2004, CIE.

[29]  Virendrakumar C. Bhavsar,et al.  Cognitive support by language visualization: A case study with hindi language , 2017, 2017 2nd International Conference for Convergence in Technology (I2CT).

[30]  G Riva,et al.  Virtual reality in telemedicine. , 2000, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[31]  Priyanka Jain,et al.  VRML for automatic generation of 3D Scene , 2018 .

[32]  Ali Farhadi,et al.  Recognition using visual phrases , 2011, CVPR 2011.

[33]  Wolfgang Schnotz,et al.  Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning , 2005 .

[34]  Albert Y. Zomaya,et al.  A Survey of Mobile Device Virtualization , 2016, ACM Comput. Surv..

[35]  G. Beauchamp,et al.  Learning disabilities: update comment on the visual system. , 1987, Pediatric clinics of North America.