Table of ContentsIntroductionIntelligent Tutoring SystemsStudent Grouping and ModelingStudent Performance PredictionEffect Detection and Student EngagementEducation and learning stand out among the many application areas of cognitive computing cognitive computing because of their practical appeal as well as their research challenge. There are various applications of learning analytics, educational data mining and cognitive systems, which can enhance human learning as well as personalized learning and can significantly improve the quality of learning and education. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayIntroductionCognitive computing is a blend of computer science and cognitive science, that is, the understanding of the human brain and its operation. Through self-learning algorithms that use data mining, visual recognition and natural language processing, the computer is able to solve problems and thus optimize human processes. Humans learn from a very small number of examples. Similarly, we can design a computational framework for the same task if we are allowed to use a large number of examples. Specifically, we can “train” a machine learning algorithm with a large number of labeled examples, represented by various features. . In some applications these features are created by hand, while in others they are automatically discovered by the algorithm itself. The important requirement here is a large amount of data, called Big Data. Educational data mining (EDM) and learning analytics (LA) are two major areas of cognitive computing dealing with education and learning. ApplicationsHere are some applications of cognitive computing to education and learning. Many of these may have been published in the EDM and LAK conferences, which are the main venues for publishing research on EDM and AL, respectively. As an application of cognitive computing, we can also consider pedagogy, technology, human judgment, social factors and various contextual elements. Intelligent Tutoring Systems Since the 1970s, ITS has been at the forefront guard of artificial intelligence research with a wide range of application areas from physics and mathematics to adult education and nursing training. One of the most influential cognitive architectures behind ITS is the Anderson Adaptive. Character Theory of Thought-Rational (ACT-R). The central tenet of ACT-R theory is that human cognition is the result of interactions between many small units of knowledge that are indivisible in some way. ACT-R theory provides details on how these knowledge units interact. ACT-R is not an abstract theory of human cognition. Rather, it is a concrete framework similar to a programming language. Closely related to the improvement of an ITS is the evaluation of its adaptive tutoring functionality, which is very applicable to ITS for programming. Traditionally, machine learning-based assessment schemes have been used, such as the cognitive Performance Factor Analysis (PFA) model. Clustering and Student Modeling Clustering is a common technique in EDM for aggregating student data to examine student behavior. This technique improves the stability of clustering.