Behavioral economicsBehavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors in the decisions of individuals or institutions, and how these decisions deviate from those implied by classical economic theory. Behavioral economics is primarily concerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights from psychology, neuroscience and microeconomic theory. The study of behavioral economics includes how market decisions are made and the mechanisms that drive public opinion.
Learning management systemA learning management system (LMS) is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, materials or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems make up the largest segment of the learning system market. The first introduction of the LMS was in the late 1990s.
Bounded rationalityBounded rationality is the idea that rationality is limited when individuals make decisions, and under these limitations, rational individuals will select a decision that is satisfactory rather than optimal. Limitations include the difficulty of the problem requiring a decision, the cognitive capability of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution, with everything that they have at the moment rather than an optimal solution.
Choice architectureChoice architecture is the design of different ways in which choices can be presented to decision makers, and the impact of that presentation on decision-making. For example, each of the following: the number of choices presented the manner in which attributes are described the presence of a "default" can influence consumer choice. As a result, advocates of libertarian paternalism and asymmetric paternalism have endorsed the deliberate design of choice architecture to nudge consumers toward personally and socially desirable behaviors like saving for retirement, choosing healthier foods, or registering as an organ donor.
Computer-supported collaborative learningComputer-supported collaborative learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through the Internet. This kind of learning is characterized by the sharing and construction of knowledge among participants using technology as their primary means of communication or as a common resource. CSCL can be implemented in online and classroom learning environments and can take place synchronously or asynchronously.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
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.
Informal learningInformal learning is characterized "by a low degree of planning and organizing in terms of the learning context, learning support, learning time, and learning objectives". It differs from formal learning, non-formal learning, and self-regulated learning, because it has no set objective in terms of learning outcomes, but an intent to act from the learner's standpoint (e.g., to solve a problem). Typical mechanisms of informal learning include trial and error or learning-by-doing, modeling, feedback, and reflection.
Lifelong learningLifelong learning is the "ongoing, voluntary, and self-motivated" pursuit of knowledge for either personal or professional reasons. It is important for an individual's competitiveness and employability, but also enhances social inclusion, active citizenship, and personal development. In some contexts, the term "lifelong learning" evolved from the term "life-long learners", created by Leslie Watkins and used by Clint Taylor, professor at CSULA and Superintendent for the Temple City Unified School District, in the district's mission statement in 1993, the term recognizes that learning is not confined to childhood or the classroom but takes place throughout life and in a range of situations.
Behavioural change theoriesBehavioural change theories are attempts to explain why human behaviours change. These theories cite environmental, personal, and behavioural characteristics as the major factors in behavioural determination. In recent years, there has been increased interest in the application of these theories in the areas of health, education, criminology, energy and international development with the hope that understanding behavioural change will improve the services offered in these areas.
LearningLearning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.
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.
AutodidacticismAutodidacticism (also autodidactism) or self-education (also self-learning and self-teaching) is education without the guidance of masters (such as teachers and professors) or institutions (such as schools). Generally, autodidacts are individuals who choose the subject they will study, their studying material, and the studying rhythm and time. Autodidacts may or may not have formal education, and their study may be either a complement or an alternative to formal education. Many notable contributions have been made by autodidacts.
Massive open online courseA massive open online course (MOOC muːk) or an open online course is an online course aimed at unlimited participation and open access via the Web. In addition to traditional course materials, such as filmed lectures, readings, and problem sets, many MOOCs provide interactive courses with user forums or social media discussions to support community interactions among students, professors, and teaching assistants (TAs), as well as immediate feedback to quick quizzes and assignments.
Operant conditioningOperant conditioning, also called instrumental conditioning, is a learning process where behaviors are modified through the association of stimuli with reinforcement or punishment. In it, operants—behaviors that affect one's environment—are conditioned to occur or not occur depending on the environmental consequences of the behavior. Operant conditioning originated in the work of Edward Thorndike, whose law of effect theorised that behaviors arise as a result of whether their consequences are satisfying or discomforting.
Confirmation biasConfirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values. People display this bias when they select information that supports their views, ignoring contrary information, or when they interpret ambiguous evidence as supporting their existing attitudes. The effect is strongest for desired outcomes, for emotionally charged issues, and for deeply entrenched beliefs.
Behavior change (public health)Behavior change, in context of public health, refers to efforts put in place to change people's personal habits and attitudes, to prevent disease. Behavior change in public health can take place at several levels and is known as social and behavior change (SBC). More and more, efforts focus on prevention of disease to save healthcare care costs. This is particularly important in low and middle income countries, where supply side health interventions have come under increased scrutiny because of the cost.
Discrete trial trainingDiscrete trial training (DTT) is a technique used by practitioners of applied behavior analysis (ABA) that was developed by Ivar Lovaas at the University of California, Los Angeles (UCLA). DTT uses direct instruction and reinforcers to create clear contingencies that shape new skills. Often employed as an early intensive behavioral intervention (EIBI) for up to 30–40 hours per week for children with autism, the technique relies on the use of prompts, modeling, and positive reinforcement strategies to facilitate the child's learning.
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.
