IndexIntroductionIntelligent Tutoring SystemsStudent Clustering and ModelingStudent Performance PredictionEffect Detection and Student EngagementEducation and learning stand out among many cognitive computing application areas due to their practical appeal and for 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 greatly improve the quality of learning and education. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay IntroductionCognitive computing is a blend of computer science and cognitive science, which is the understanding of the human brain and how it works. 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 limited number of examples. Similarly, we can devise a computational framework for the same task if we are allowed to use a large number of examples. More 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 manually while in others they are discovered automatically by the algorithm itself. The important requirement here is a large amount of data, termed as Big Data. Educational Data Mining (EDM) and Learning Analytics (LA) are two main areas of cognitive computing that deal with education and learning. ApplicationsHere are some applications of cognitive computing and education and learning. Many of these may have appeared at the EDM and LAK conferences, which are the primary venues for publishing research on EDM and LA, 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 have been at the forefront of artificial intelligence research with a wide range of application areas ranging from physics and mathematics to adult education and nursing training. One of the most influential cognitive architectures behind ITS is Anderson's Adaptive Character Theory of Rational-Thinking (ACT-R). The central tenet of ACT-R theory is that human cognition is the result of interactions between numerous small, indivisible units of knowledge in certain ways. ACT-R theory provides details of how these units of knowledge interact. ACT-R is not an abstract theory of human cognition. It is rather a concrete structure similar to a programming language. Closely related to the improvement of an ITS is the evaluation of its adaptive tutoring functionality, which is widely applicable to ITSs for programming. Traditionally, machine learning-based evaluation schemes have been used, such as the Performance Factors Analysis (PFA) cognitive model. Clustering and Student Modeling Clustering is a common technique in EDM for aggregating student data to examine student behavior. This technique improves clustering stability for noisy data. Furthermore, it can be incorporated into any ITS as a black box. Another central problem in EDM is student modeling. One of these works concerns the modeling of learning curves. Using data from Duolingo (2016), Streeter (2015) has.