ACCENTS Transactions on Image Processing and Computer Vision (TIPCV) ISSN (O): 2455-4707 Vol - 6, Issue - 19, May 2020

  1. Google Scholar

  2. Citation

  3. Impact Factor
The role of computer vision in the development of knowledge-based systems for teaching and learning of English language education

George Chibuike Agbo and Philomina Akudo Agbo

Abstract

This study focused on the assessment of the use of computer vision in the teaching and learning of English language education. The use of computer vision in the teaching and learning of language education has become a novel approach that has attracted varying attentions in recent times. In this study, descriptive analysis was used to provide a coherent view of the use of computer vision in implementing knowledge-based systems for the teaching and learning of English Language Education. The language education tasks that were analysed comprised of image captioning, human-machine interaction, video captioning, visual attributes, visual retrieval and visual question answering. The study also looked at the approaches that could be used to incorporate computer vision and the teaching and learning of English language education courses, with particular reference to models as an integrated topic of distributional semantics. The researchers made an analysis of computer vision and English Language teaching and learning, using distributional semantics as both words and imagery. An integrated view of the subject matter was clearly presented and useful suggestions were made for promising future guidelines. The study also x-rayed the usefulness of computer vision for effective teaching and learning of language education and suggested novel approaches that could be used in incorporating computer vision in developing knowledge-based system for effective teaching and learning of the English language.

Keyword

Computer vision, English language education, Teaching and learning, Images.

Cite this article

Agbo GC, Agbo PA

Refference

[1][1]National Science Foundation (US). Directorate for Education, Human Resources. Shaping the future: New expectations for undergraduate education in science, mathematics, engineering, and technology. National Science Foundation, Division of Undergraduate Education; 1996.

[2][2]Coppula D. Integrating teaching and research. ASEE Prism. 1997; 7(4):18-22.

[3][3]National Research Council (US). National committee on science education standards, assessment. National science education standards: draft for review and comment only. National Research Council; 1994.

[4][4]Shah M, Bowyer K. Mentoring undergraduates in computer vision research. IEEE Transactions on Education. 2001; 44(3):252-7.

[5][5]Shah M, Bowyer K. Mentoring undergraduates in computer vision research. IEEE Transactions on Education. 2001; 44(3):252-7.

[6][6]Maxwell BA. A survey of computer vision education and text resources. International Journal of Pattern Recognition and Artificial Intelligence. 2001; 15(05):757-73.

[7][7]Sanchez A, Velez JF, Moreno AB, Esteban JL. Introducing algorithm design techniques in undergraduate digital image processing courses. International Journal of Pattern Recognition and Artificial Intelligence. 2001; 15(05):789-803.

[8][8]Grewe L. Effective computer vision instruction through experimental learning experiences. International Journal of Pattern Recognition And Artificial Intelligence. 2001; 15(05):805-21.

[9][9]Pridmore TP, Hales WM. Understanding images: an approach to the university teaching of computer vision. Engineering Science & Education Journal. 1995; 4(4):161-6.

[10][10]Glasgow N. A guide to student-centered, problem-based learning.1997.

[11][11]Duch BJ. Problem-based learning in undergraduate education. 2019.

[12][12]Novins K, McCane B. Incorporating primary source material into the undergraduate computer vision curriculum. International Journal of Pattern Recognition and Artificial Intelligence. 2001; 15(05):775-87.

[13][13]Donelan M, Wallace J. Peer assisted learning: a truly co-operative initiative. Students Supporting Students. 1998:11-22.

[14][14]Ploetzner R, Dillenbourg P, Preier M, Traum D. Learning by explaining to oneself and to others. Collaborative learning: Cognitive and Computational Approaches. 1999; 1:103-21.

[15][15]Jiménez-Peris R, Khuri S, Patiño-Martínez M. Adding breadth to CS1 and CS2 courses through visual and interactive programming projects. In the proceedings of the thirtieth SIGCSE technical symposium on computer science education 1999 (pp. 252-6).

[16][16]Fell HJ, Proulx VK. Exploring Martian planetary images: C++ exercises for CS1. In proceedings of the twenty-eighth SIGCSE technical symposium on computer science education 1997 (pp. 30-4).

[17][17]Astrachan O, Rodger SH. Animation, visualization, and interaction in CS 1 assignments. ACM SIGCSE Bulletin. 1998; 30(1):317-21.

[18][18]Montgomery RE. Image analysis: a group assignment in programming with breadth. In proceedings frontiers in education 1995 25th annual conference. engineering education for the 21st Century 1995 Nov 1 (pp. 4d3-12). IEEE.

[19][19]Stockman G, Enbody R. Teaching advanced students C++ with computer vision. In workshop on combined research-curriculum development in computer vision, Kauai, Hawaii 2001.

[20][20]Marks J, Freeman W, Leitner H. Teaching applied computing without programming: a case-based introductory course for general education. In proceedings of the thirty-second SIGCSE technical symposium on computer science education 2001 (pp. 80-4).

[21][21]Egbert D, Bebis G, McIntosh M, LaTouttette N, Mitra A. Computer vision research as a teaching tool in CS1. In32nd annual frontiers in education 2002 (pp. T4G-T4G). IEEE.

[22][22]Egbert D, Bebis G, Williams D. Computer vision research teaching modules for community college computer science and engineering courses. Age. 2003; 8:1.

[23][23]Sarkar S, Goldgof D. Integrating image computation in undergraduate level data-structure education. International Journal of Pattern Recognition and Artificial Intelligence. 1998; 12(08):1071-80.

[24][24]http://www.inf.unibz.it/~nutt/Teaching/DSA1415/dsa-assignments.html. Accessed 26 march 2020.

[25][25]Shirish TS. Research methodology in education. Lulu. com; 2012.

[26][26]Hoover A. Computer vision in undergraduate education: Modern embedded computing. IEEE Transactions on Education. 2003; 46(2):235-40.

[27][27]Fink E, Heath M. Image-processing projects for an algorithms course. International journal of pattern recognition and artificial intelligence. 2001; 15(05):859-68.

[28][28]Alhawiti KM. Natural language processing and its use in education. Computer Science Department, Faculty of Computers and Information technology, Tabuk University, Tabuk, Saudi Arabia. 2014.

[29][29]Wiriyathammabhum P, Summers-Stay D, Fermüller C, Aloimonos Y. Computer vision and natural language processing: recent approaches in multimedia and robotics. ACM Computing Surveys (CSUR). 2016; 49(4):1-44.