Technology integrationEditing technology is the use of technology tools in general content areas in education in order to allow students to apply computer and technology skills to learning and problem-solving. Generally speaking, the curriculum drives the use of technology and not vice versa. Technology integration is defined as the use of technology to enhance and support the educational environment. Technology integration in the classroom can also support classroom instruction by creating opportunities for students to complete assignments on the computer rather than with normal pencil and paper.
Learning analyticsLearning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis.
Educational data miningEducational data mining (EDM) is a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings.
Digital learningDigital learning is any type of learning that is accompanied by technology or by instructional practice that makes effective use of technology. It encompasses the application of a wide spectrum of practices, including blended and virtual learning. Digital learning is sometimes confused with online learning or e-learning; digital learning encompasses the aforementioned concepts. Digital learning has become widespread during the COVID-19 pandemic.
Bayesian Knowledge TracingBayesian Knowledge Tracing is an algorithm used in many intelligent tutoring systems to model each learner's mastery of the knowledge being tutored. It models student knowledge in a Hidden Markov Model as a latent variable, updated by observing the correctness of each student's interaction in which they apply the skill in question. BKT assumes that student knowledge is represented as a set of binary variables, one per skill, where the skill is either mastered by the student or not.
