i-manager Publications

Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review

Anuraj Malav*, Nehta Gupta**

* Junior Research Fellow, Department of Research and Development, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

** Professor and HOD, Department at School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

Periodicity:June - August'2018
DOI : https://doi.org/10.26634/jcom.6.2.15032

Abstract

Traditional teaching learning has transformed significantly towards offering a learner an experience that to a greater extent mimics a human tutor; while in a computer-based or valued learning environment, Machine Learning (ML) techniques implemented as algorithms have played a significant role. This paper is a review of different interventions of machine learning in selected types of teaching learning systems, presented as a descriptive analysis, recommendations emergent from this analysis have been presented. Further the possibility of applicability of these systems for supporting learning of individual with disabilities, has been explored and evidentially advocated machine learning algorithms hold tremendous potential in terms of enriching the systems, facilitating the learning of individuals with special needs by providing versatility and adoptive learning experiences learning effectiveness, and this thought has been further extended to a recommendation for individuals with a disability, essentially with the deemed design alternatives.

Keywords

Computer-Based Training (CBT), Disability, E-learning, Machine Learning (ML), Teaching-Learning, Web-based Training (WBT).

How to Cite this Article?

Malav ,A.,& Ahuja, N.J. (2018). Machine Learning Techniques for Effective Facilitation of Teaching And Learning: a narrative Review. i-manager’s Journal on Computer Science, 6(2), 42-47. https://doi.org/10.26634/jcom.6.2.15032

References

[1]. Bjekić, D., Obradović, S., Vučetić, M., & Bojović, M. (2014). E-teacher in inclusive e-education for students with specific learning disabilities. Procedia-Social and Behavioral Sciences, 128, 128-133.

[2]. Conlan, O., Wade, V., Bruen, C., & Gargan, M. (2002, May). Multi-model, metadata driven approach to adaptive hypermedia services for personalized e-learning. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 100- 111). Springer, Berlin, Heidelberg.

[3]. Chen, G. D., Liu, C. C., Ou, K. L., & Liu, B. J. (2000). Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research, 23(3), 305-332.

[4]. Gall, M., & Breeze, N. (2008). Music and eJay: An opportunity for creative collaborations in the classroom. International Journal of Educational Research, 47(1), 27- 40.

[5]. Hlosta, M., Zdrahal, Z., & Zendulka, J. (2017, March). Ouroboros: Early identification of at-risk students without models based on legacy data. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 6-15). ACM.

[6]. Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610-621.

[7]. Hollweck, T. (2014). Case Study Research Design and Methods (5^th Ed.). Book Reviews. https://doi.org/10.3138/ cjpe.30.1.108

[8]. Hwang, G. J., Hsu, T. C., Lai, C. L., & Hsueh, C. J. (2017). Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns. Computers & Education, 106, 26-42.

[9]. Keleş, A., Ocak, R., Keleş, A., & Gülcü, A. (2009). ZOSMAT: Web-based intelligent tutoring system for teaching–learning process. Expert Systems with Applications, 36(2), 1229-1239.

[10]. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3-24.

[11]. Liu S., & d'Aquin, M. (2017). Unsupervised learning for understanding student achievement in a distance learning setting. In IEEE Global Engineering Education Conference (EDUCON) (pp. 25-28).

[12]. Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2013). Foundations of Machine Learning. MIT Press https://doi.org/10.1017/CBO9781107415324.004

[13]. Mythili, M. S., & Shanavas, A. M. (2014). A novel approach to predict the learning skills of autistic children using SVM and Decision tree. International Journal of Computer Science and Information Technologies (IJCSIT), 5(6), 7288-7291.

[14]. Mi-Children.org. (2014, September). Mi-Children: Intelligence for Special Needs. [Online]. http://mi-children. orgl

[15]. McDermott, L. C. (1990). Research and computer-based instruction: Opportunity for interaction. American Journal of Physics, 58(5), 452-462.

[16]. Pan, Z., Cheok, A. D., Yang, H., Zhu, J., & Shi, J. (2006). Virtual reality and mixed reality for virtual learning environments. Computers & Graphics, 30(1), 20-28.

[17]. Register, D., Darrow, A. A., Swedberg, O., & Standley, J. (2007). The use of music to enhance reading skills of second grade students and students with reading disabilities. Journal of Music Therapy, 44(1), 23-37.

[18]. Richards, J. C., & Rodgers, T. S. (1987). Approaches and Methods in Language Teaching. Cambridge University Press.

[19]. Reich, Y. (1994). Towards Practical Machine Learning Techniques. In Computing in Civil Engineering (pp. 885- 892). ASCE.

[20]. Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge: MIT Press.

[21]. Sutherland, R., Armstrong, V., Barnes, S., Brawn, R., Breeze, N., Gall, M., ... & Wishart, J. (2004). Transforming teaching and learning: Embedding ICT into everyday classroom practices. Journal of Computer Assisted Learning, 20(6), 413-425.

[22]. Swathika, R., & Sharmila, T. S. (2016, August). Emergency exit sign detection system for visually impaired people. In Inventive Computation Technologies (ICICT), International Conference on (Vol. 1, pp. 1-7). IEEE

[23]. Vapnik, V. (1998). Statistical Learning Theory. New York: Wiley.

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Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review

Abstract

Keywords

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References

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