Introduction
Machine learning has assumed the center stage when it comes to the transformational role in the field of education. Machine learning makes it possible to evaluate students like never before. With the availability of machine learning technology, it is possible to spot the shortcomings in the understanding of students and devise ways and means for improving learning outcomes. Machine learning has its relevance at the primary level of education, the secondary level as well as the tertiary level. There are a large number of institutes that are desirous of making breakthroughs with the help of machine learning. The problem lies in technology transfer, in addition to the lack of expertise in handling machine learning technology. Nevertheless, machine learning training in Delhi is slowly coming under the spotlight and this may soon serve as a catalyst for transforming the educational system in India.
Primary level
At the primary level, machine learning can help in accentuating target-based intervention for improving learning outcomes. With the aid and advice of this technology, teachers can reach out to those students that are lagging behind so as to improve their performance. Home-based monitoring, grievance redressal mechanism, round-the-clock support, and online intervention tools can play a transformation role at the primary level of education. In addition to this, teachers can also provide personalized content to students who are in need of special intervention.
Secondary level
At the secondary level of education, machine learning allows us to bridge the gap between primary level and the higher level. It is at the secondary level that machine learning supplements the educational needs of students with the help of technologies like augmented reality, virtual reality and merged reality. The benefit of these technologies is that they clear the concepts of students and allow immersive learning. Machine learning is also crucial at the secondary stage as a shift is observed in learning pedagogy at the secondary level itself. For instance, a shift occurs at this level from social to natural science, quantitative to qualitative learning and nomothetic to idiographic methodology. This transition can be made much more swift with the help of machine learning technology.
Higher education level
Machine learning intervention at the higher education level revolves around research aspects, themes and directions. This means that machine learning can serve as an important catalyst for innovation in the long run.
The role of machine learning at the higher education level can be understood from different viewpoints. As per the first viewpoint, machine learning serves as the pathway to improve the functioning of different processes. These processes may not be central to machine learning but come under its scope due to increasing interdisciplinarity. As per the second viewpoint, machine learning itself serves as an important raw material for research.
Education startups
Various types of educational startups have achieved great success after they have harnessed the power of machine intelligence in the development of educational products. Let us briefly examine how machine learning has influenced education startups.
Firstly, machine learning has enabled startups to set up assessment platforms that not only track student performance but also monitor various gaps in their preparation. Consequently, the platforms powered by machine learning help in building concepts of students and improve their performance.
Secondly, educational startups make use of modern technologies like machine learning, artificial intelligence, virtual reality and augmented reality to provide a novel learning experience to students. For instance, Metaversity has been set up that provides all the facilities of traditional universities in a digital world.
Future transformation
The education system that we would see in the future would be completely different from the one that we are currently witnessing. Some of the glimpses of this future setup have already been witnessed in the form of changing pedagogy and institutional structure. The learning pedagogy that is currently operating is being questioned by various stakeholders. Some of the researchers have advocated the Libre approach while others have vouched for the Reggio Emilia model. Both these frameworks revolve around the idea of making the educational system innovative, modern, progressive and creative. The centre of focus in these new pedagogies is the interest of the student.
The aim is to conceive a pedagogical structure that is for the student and for the student as well. One example of this is the UNESCO Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) which adopts a socio-emotional form of pedagogy and stresses on an empathetic understanding of students.
Conclusion
The role of machine learning in transforming the future of education is important as the learning systems, frameworks and institutions are undergoing rapid change. To keep up with this change, it is important to bring technological reforms in the education sector and this is not possible without the aid of machine learning.