Abstract
Machine learning are having a tremendous impact on the teaching industry. Teaching industry is adopting new technologies to predict the future of education system. It is Machine learning which predict the future nature of education environment by adapting new advanced intelligent technologies. This work explores the application of Machine Learning in teaching and learning for further improvement in the learning environment in higher education. We explore the application of machine learning in customized teaching and learning environment and explore further directions for research. Customized teaching and learning consider student background, individual student aptitude, learning speed and response of each student. This customized teaching and learning approach provide feedback to teacher after real-time processing of the data. This way a teacher can easily recognize student attention and take corrective measures. This will improve student participation and hence the overall results. Individual student concepts and goals can easily be track with the help of Machine learning by taking real-time feedback. Based on that feedback, curriculum, topics and methodology can be improved further. In simple terms, machine learning makes the process automatic for decision making process and analyzed the individual student data. Overall, the assessment process is made more streamlined, accurate and unbiased with the help of machine learning. In the near future, machine learning will be more efficient and produce even better results.
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J.J. Lu, L.A. Harris, in Congressional Reasearch Service (2018). www.crs.gov, In Focus
R. Luckin, W. Holmes, M. Griffiths, L.B. Focier, Intelligence Unleashed: An Argument for AI In Education, Pearson (2016)
M. Noorian, E. Bagheri, W. Du, Machine learning-based software testing: towards a classification framework, in Proceedings of the 23rd International Conference on Software Engineering & Knowledge Engineering (2011)
C. Murphy, G. Kaiser, M. Arias, An Approach to Software Testing of Machine Learning Applications. Columbia University Computer Science Technical Reports (2007)
S. Amershi, C. Conati, Unsupervised and supervised machine learning in user modeling for intelligent learning environments, in Proceedings of 12th International Conference on Intelligent User interfaces, pp. 72–81 (2007)
Y. Liu, L. Zhang, L. Nie, Y. Yan, D.S. Rosenblum, Fortune teller: predicting your career path, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016)
Y. Lou, R. Ren, Y. Zhao, A Machine Learning Approach for Future Career Planning
H.F. El-Sofany, N. Al-Jaidah, S. Ibra-him, S. Al-kubaisi, Web-based “Questions-Bank” system to improve E-Learning education in Qatari school. J. Comput. Sci. 5(2), 97–108 (2009), 2009 ISSN 1549-3636
M. Liu, R.A. Calvo, V. Rus, Automatic question generation for literature review writing support, in International Conference on Intelligent Tutoring Systems, Intelligent Tutoring Systems, pp. 45–54 (2010)
D. Liu, C. Lin, Sherlock: a semi-automatic quiz generation system using linked data, in 13th International Semantic Web Conference (2014)
K. Greff, R.K. Srivastava, J. Koutn´ık, B.R. Steunebrink, J. Schmidhuber, LSTM: A Search Space Odyssey, Transactions on Neural Networks and Learning Systems (2015)
H. Sak, A. Senior, F. Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling. Cornell University Library (2014)
T. Liu, S. Fang, Y. Zhao, P. Wang, J. Zhang, Implementation of Training Convolutional Neural Networks. Cornell University Library (2015)
V. Kalogeiton, Introduction to Convolutional Neural Networks, Reading Group on Deep Learning: Session 3 (2016)
I.E. Fattoh, Automatic multiple choice question generation system for semantic attributes using string similarity measures. Comput. Eng. Intell. Syst. 5(8) (2014), ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
L. Padrio, Statistical Methods for Natural Language Processing (2009)
M. Collins, Statistical Methods in Natural Language Processing. AT&T Labs-Research
A.A. Khan, O. Naseer, Fuzzy logic based multi user adaptive test system. Int. J. Soft Comput. Softw. Eng. 2(08) (2012), e-ISSN: 2251-7545
W. Huang, Z.-H. Wang, Design of examination paper generating system from item bank by using genetic algorithm, in Proceeding of International Conference on Computer Science and Software Engineering (2008)
A.V. Sharma, Review of evolutionary optimization algorithms for test case minimization. Int. J. Eng. Comput. Sci. 4(7), 13292–13297 (2015), ISSN:2319-7242
V.U.B. Challagulla, F.B. Bastani, I.-L. Yen, R.A. Paul, Empirical assessment of machine learning based software defect prediction techniques. Int. J. Artif. Intell. Tools 17(2) (2008)
J.F. Sowa, Conceptual graphs as a Universal knowledge representation. Comput. Math. Appl. 23(2–5), 75–93 (1992), ISSN -0097-4943/92
A. Jefferies, R. Hyde, Building the future students’ blended learning experiences from current research findings. Electron. J. e-Learning 8, 133–140 (2010)
F. Duzhin, A. Gustafsson, Machine learning-based app for self-evaluation of teacher-specific instructional style and tools’ education sciences. MDPI J. (2018)
M.A. Razek, C. Frasson, M. Kaltenbach, Toward more cooperative intelligent distance learning environments. Softw. Agents Coop. Hum. Activity (2002). http://www-perso.iro.umontreal.ca/˜abdelram. Accessed Feb 2003
H. Shi, Y. Shang, S. Chen, A multi-agent system for computer science education, in Proceedings of the 5th Annual SIGCSE/SIGCUE ITiCSE Conference on Innovation and Technology in Computer Science Education, pp. 1–4 (2000)
L. Aroyo, P. Kommers, Special issue preface, intelligent agents for educational computer-aided systems. Interact. Learn. Res. 10(3/4), 235–242 (1999)
R.S.J.D. Baker, K. Yacef, The state of educational data mining in 2009: a review and future visions. J. Educ. Data Min. 1(1), Fall (2009), Article 1
L. Kidzi nski et al., A tutorial on machine learning in educational science, in Proceedings of International Conference on Future Buildings and Districts—Sustainability from Nano to Urban Scale—Vol. II, Scartezzini, Jean-Louis (2015)
C.J. Burges, A tutorial on support vector machines for pattern recognition. Datamining Knowl. Discov. 2(2), 121–167 (1998)
H.M. Half, Instructional applications of artificial intelligence. Educ. Leadersh., 24–31 (1986)
A. Al-Ajlan, The comparison between forward and backward chaining. Int. J. Mach. Learn. Comput. 5(2) (2015)
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Asthana, P., Hazela, B. (2020). Applications of Machine Learning in Improving Learning Environment. In: Tanwar, S., Tyagi, S., Kumar, N. (eds) Multimedia Big Data Computing for IoT Applications. Intelligent Systems Reference Library, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-13-8759-3_16
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