Deep Learning: The Future of Education

1. Educational Data Mining is an analysis field that focuses on the applying of machine learning, information mining and statistical methods to detect patterns in massive collections of instructional information. Various machine-learning techniques have been applied throughout this field over the years. Recently, Deep Learning has gained increasing attention in the academic domain. Deep Learning may well be a machine learning technique supported by neural network architectures with multiple layers of method units. It has been successfully applied to a broad set of problems in the areas of image recognition and language methods. Deep Learning techniques have been applied to Educational Data Mining (EDM), from its origins to the current day. The main goals of this article are to highlight the benefits of Deep Learning, discuss the challenges of implementing Deep Learning in education, the need for deep learning in education and the future of education with deep learning.

Benefits of Deep Learning

2. Deep learning has a significant impact on education, particularly in the context of sustainability (Warburton, 2003). It has been increasingly applied in Educational Data Mining (EDM), with a focus on detecting patterns in large educational datasets (Hernández-Blanco, 2019). The use of deep learning in eLearning has also been explored, with a focus on developing resources and creating personalized learning experiences (Muniasamy, 2020). These applications of deep learning in education have the potential to enhance learning outcomes and improve the overall educational experience.

Challenges of Implementing Deep Learning in Education

3. The implementation of deep learning in education is a complex and controversial issue, as highlighted by Perrotta (2019). The use of AI elements in online learning environments, while promising, is troubled with challenges such as flawed data, incomprehensible computational methods, and narrow educational knowledge. These issues are further compounded by the reductionist discourse of data science and economic interests. Sharma (2019) adds that the theoretical design and execution of experiments in deep learning are also challenging in implementing deep learning in education. Despite these obstacles, Warburton (2003) emphasizes the importance of deep learning in education for sustainability, suggesting that it can be inhibited by students' existing interests and backgrounds. Therefore, while the potential benefits of deep learning in education are significant, its implementation is hindered by a range of technical, methodological, and pedagogical challenges.

The Need for Deep Learning in Education

4. Perrotta (2019) and Kashyap (2020) both discuss the potential of deep learning in education, with Perrotta highlighting the controversy and economic interests surrounding its implementation, and Kashyap focusing on its application in educational data mining. Laevers (1993) provides a practical example of deep-level learning in the context of physical knowledge, emphasizing the importance of involvement in development. These studies collectively underscore the need for a critical and practical approach to the use of deep learning in education.

The Future of Education with Deep Learning

5. Deep learning, a subset of artificial intelligence, is poised to revolutionize the future of education, particularly in the realm of eLearning (Muniasamy, 2020). Its potential to enhance higher education by fostering connection and collaboration between students and educators is also highlighted (Weigel, 2001). In the field of Educational Data Mining, deep learning has gained significant attention, with its application to tasks such as pattern detection and language processing (Kashyap, 2020). Furthermore, deep learning is seen as a key strategy in education for sustainability, particularly in encouraging students to use interdisciplinary thinking and holistic insight (Warburton, 2003).

Refrence

1. Kashyap, Divya. “Deep Learning in the field of Education.” Journal of emerging technologies and innovative research 7 (2020): 1441-1446-1441-1446.

2. Muniasamy, Anandhavalli and Areej Alasiry. “Deep Learning: The Impact on Future eLearning.” Int. J. Emerg. Technol. Learn. 15 (2020): 188-199.