Computers are being utilized in field in education for many years. In last few decades, research within the field of artificial intelligence (AI) is positively affecting educational application. Advanced machine learning and deep learning techniques could be used for extracting knowledgeable information from crude information. In this chapter, the authors have analysed the impact of artificial intelligence in the education domain. The authors will discuss how with the development of machine learning techniques in last few decades, machine learning models can anticipate student performance. By learning about every student, models can identify the shortcomings. Then the authors will propose different approaches to improve student performance. Teachers can also use this model to understand student perception levels in a better way so that they can modulate their lectures according to student perception levels.
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Computers are being utilized in field in Education for many years. In the last few years research within the domain of Artificial Intelligence (AI) is positively affecting educational application. Artificial Intelligence is a mixture of Big data, Machine Learning, Combinatorial Optimization, and Natural Language Processing. It is the new generation of technology which will help out modern society and education in many ways. Different technologies are already using these techniques to provide more functionality in their applications. Advanced machine learning and deep learning techniques could be used for extracting knowledgeable information from crude information. By learning about every student, models can identify the shortcomings and then authors will propose different approaches to improve student performance. Teachers can also use this model to understand students' perception level in a better way so that they can modulate their lectures according to the student's perception level.
Artificial Intelligence is progressing at an accelerated pace, it already fabricates the intelligent nature of services in the education domain. Some AI solutions are dependent on programming, whereas some have capability to find patterns and make predictions regarding the given problem. Example: Recently French song writer Benoît Carré has worked together with an AI music program called Flow Machines to create an EuroPop Album. Authors describe Machine Learning as a subfield of AI that joins programming prepared to perceive designs, make predictions, and apply recently discovered examples to conditions that were rejected or secured by their underlying plan. To Understand Artificial Intelligence better let us know about the basics of Artificial Intelligence:
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Big Data:- Large data sets that are analyzed by using patter revelation, trends, associations which will help in relating to human behaviour and interactions. It deals with very large data sets that are too complex to be solved with traditional data processing software.
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Machine Learning: - It’s an application of artificial intelligence that deliversintelligence to computers so that they learn repeatedly from training sets and test the predictions on test data to check accuracy and along with other statistical parameters. It mainly focuses on applications that grant rights to use data and use it for machine learning tasks.
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Combinatorial Optimization:- it is an emerging field of theoretical computer science that uses combinatorial techniques that aims to solve discrete optimization problems. It finds the best possible solution from a finite set of problems.
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Natural Language Processing (NLP):- It involves machines or robots to understand and replicate the language that humans speak. It also tries to understand the emotions and reactions of human beings.
The future of cutting edge education systems is naturally associated with improvement of new advancements and computing capacity of new astute machines. Machine learning can help to fill the needs gaps in learning and teaching. Teachers can predict student’s performance and can also classify confusion levels of students whether a particular topic will be clear to the cluster of students or not.
In this chapter, authors have analysed the impact of Artificial Intelligence in the education domain. Authors will discuss how with the development of Machine Learning techniques in last few decades, machines learning models can anticipate student performance. The objective of our chapter is to study the student’s performance based on current real world data (viz. student grade, demographic area data, social activity features and school related features). In this chapter, several machine learning models (Support Vector Machine, Logistic Regression, Decision Tree Classifier, Naive Bayes) by (Islam & Zhang, 2018) are applied to investigate the performance of students. (Kharb, Aggarwal & Chahal, 2020)Big data faces various difficulties, for example, data storage, data capturing, data analysis, sharing data reports, searching, transferable data, data visualization, tossing queries on it, updation, security. Dataset is collected from the University of California Irvine (UCI) machine learning repository in (Namayanja & Janeja 2012) and (Frank & Asuncion, 2010); it holds 649 instances and 33 attributes.