Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Student teacherA student teacher or prac teacher (practice teacher) is a college, university or graduate student who is teaching under the supervision of a certified teacher in order to qualify for a degree in education. The term is also often used interchangeably with pre-service teacher. It is a much broader term to include those students that are studying the required coursework in pedagogy, as well as their specialty, but have not entered the supervised teaching portion of their training.
Educational neuroscienceEducational neuroscience (or neuroeducation, a component of Mind Brain and Education) is an emerging scientific field that brings together researchers in cognitive neuroscience, developmental cognitive neuroscience, educational psychology, educational technology, education theory and other related disciplines to explore the interactions between biological processes and education. Researchers in educational neuroscience investigate the neural mechanisms of reading, numerical cognition, attention and their attendant difficulties including dyslexia, dyscalculia and ADHD as they relate to education.
Teacher educationTeacher education or teacher training refers to programs, policies, procedures, and provision designed to equip (prospective) teachers with the knowledge, attitudes, behaviors, approaches, methodologies and skills they require to perform their tasks effectively in the classroom, school, and wider community. The professionals who engage in training the prospective teachers are called teacher educators (or, in some contexts, teacher trainers). There is a longstanding and ongoing debate about the most appropriate term to describe these activities.
StudentA student is a person enrolled in a school or other educational institution. In the United Kingdom and most commonwealth countries, a "student" attends a secondary school or higher (e.g., college or university); those in primary or elementary schools are "pupils". In Nigeria, education is classified into four systems known as a 6-3-3-4 system of education. It implies six years in primary school, three years in junior secondary, three years in senior secondary and four years in the university.
FeedbackFeedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop. The system can then be said to feed back into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems: Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument.
Students' unionA students' union, also known by many other names, is a student organization present in many colleges, universities, and high schools. In higher education, the students' union is often accorded its own building on the campus, dedicated to social, organizational activities, representation, and academic support of the membership. In the United States, student union often only refers to a physical building owned by the university with the purpose of providing services for students without a governing body.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Educational technologyEducational technology (commonly abbreviated as edutech, or edtech) is the combined use of computer hardware, software, and educational theory and practice to facilitate learning. When referred to with its abbreviation, edtech, it often refers to the industry of companies that create educational technology. In addition to the practical educational experience, educational technology is based on theoretical knowledge from various disciplines such as communication, education, psychology, sociology, artificial intelligence, and computer science.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Negative feedbackNegative feedback (or balancing feedback) occurs when some function of the output of a system, process, or mechanism is fed back in a manner that tends to reduce the fluctuations in the output, whether caused by changes in the input or by other disturbances. Whereas positive feedback tends to lead to instability via exponential growth, oscillation or chaotic behavior, negative feedback generally promotes stability. Negative feedback tends to promote a settling to equilibrium, and reduces the effects of perturbations.
Deep belief networkIn machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors. After this learning step, a DBN can be further trained with supervision to perform classification.
