Learning theory describes how students receive, process, and retain knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills retained.
Distributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.
Instructional scaffolding is the support given to a student by an instructor throughout the learning process. This support is specifically tailored to each student; this instructional approach allows students to experience student-centered learning, which tends to facilitate more efficient learning than teacher-centered learning. This learning process promotes a deeper level of learning than many other common teaching strategies.
In psychology, cognitivism is a theoretical framework for understanding the mind that gained credence in the 1950s. The movement was a response to behaviorism, which cognitivists said neglected to explain cognition. Cognitive psychology derived its name from the Latin cognoscere, referring to knowing and information, thus cognitive psychology is an information-processing psychology derived in part from earlier traditions of the investigation of thought and problem solving.
Situated cognition is a theory that posits that knowing is inseparable from doing by arguing that all knowledge is situated in activity bound to social, cultural and physical contexts.
Kinesthetic learning, kinaesthetic learning, or tactile learning is a learning style in which learning takes place by the students carrying out physical activities, rather than listening to a lecture or watching demonstrations. As cited by Favre (2009), Dunn and Dunn define kinesthetic learners as students who require whole-body movement to process new and difficult information.
Meta learning is a branch of metacognition concerned with learning about one's own learning and learning processes.
Self-regulated learning (SRL) is one of the domains of self-regulation, and is aligned most closely with educational aims. Broadly speaking, it refers to learning that is guided by metacognition, strategic action, and motivation to learn. A self-regulated learner "monitors, directs, and regulates actions toward goals of information acquisition, expanding expertise, and self-improvement”. In particular, self-regulated learners are cognizant of their academic strengths and weaknesses, and they have a repertoire of strategies they appropriately apply to tackle the day-to-day challenges of academic tasks. These learners hold incremental beliefs about intelligence and attribute their successes or failures to factors within their control.
An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher, or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.
The term virtual patient is used to describe interactive computer simulations used in health care education. The special focus is targeted on the simulation of clinical processes with virtual patients. Virtual patients combine scientific excellence, modern technologies and the innovative concept of game-based learning. Virtual patients allow the learner to take the role of a health care professional and develop clinical skills such as making diagnoses and therapeutic decisions. Virtual patients have also been considered computer-based simulations designed to complement clinical training. The use of virtual patient programmes is increasing in healthcare, partly in response to increasing demands on health care professionals and education of students but also because they allow opportunity for students to practice in a safe environment. There are many different formats a virtual patient may take. However the overarching principle is that of interactivity—a virtual patient will have mechanisms for the learner to interact with the case and material or information is made available to the learner as they complete a range of learning activities. Interactivity is often included with questions, specific decision-making tasks, text-composition etc. and is non-sequential. Most systems provide quantitative and qualitative feedback.
Practice is the act of rehearsing a behaviour and over, or engaging in an activity again and again, for the purpose of improving or mastering it, as in the phrase 'practice makes perfect'. It is important to note that practise is a verb and should not be confused with the noun practice. Sports teams practise to prepare for actual games. Playing a musical instrument well takes much practice. It is a method of learning and of acquiring experience. The word derives from the Greek "πρακτική" (praktike), feminine of "πρακτικός" (praktikos), "fit for or concerned with action, practical", and that from the verb "πράσσω" (prasso), "to achieve, bring about, effect, accomplish". In English, practice is the noun and practise is the verb, but in American-English it is now common for practice to be used both as a noun and a verb.
E-learning theory describes the cognitive science principles of effective multimedia learning using electronic educational technology.
The apprentice perspective is an educational theory of apprenticeship concerning the process of learning through physical integration into the practices associated with the subject, such as workplace training. By developing similar performance to other practitioners, an apprentice will come to understand the tacit duties of the position. In the process of creating this awareness, the learner also affect their environment; as they are accepted by master practitioners, their specific talents and contributions within the field are taken into account and integrated into the overall practice.
Neo-Piagetian theories of cognitive development criticize and build upon Jean Piaget's theory of cognitive development.
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.
Differentiated instruction and assessment, also known as differentiated learning or, in education, simply, differentiation, is a framework or philosophy for effective teaching that involves providing all students within their diverse classroom community of learners a range of different avenues for understanding new information in terms of: acquiring content; processing, constructing, or making sense of ideas; and developing teaching materials and assessment measures so that all students within a classroom can learn effectively, regardless of differences in their ability. Students vary in culture, socioeconomic status, language, gender, motivation, ability/disability, learning styles, personal interests and more, and teachers must be aware of these varieties as they plan in accordance with the curricula. By considering varied learning needs, teachers can develop personalized instruction so that all children in the classroom can learn effectively. Differentiated classrooms have also been described as ones that respond to student variety in readiness levels, interests, and learning profiles. It is a classroom that includes and allows all students to be successful. To do this, a teacher sets different expectations for task completion for students, specifically based upon their individual needs.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion".
What Video Games Have to Teach Us About Learning and Literacy is a book by James Paul Gee that focuses on the learning principles in video games and how these principles can be applied to the K-12 classroom. Video games can be used as tools to challenge players, when they are successful. They motivate players to persevere and simultaneously teach players how to play the game. These games give a glimpse into how one might create new and more powerful ways to learn in schools, communities, and workplaces. Gee began his work in video games by identifying thirty-six learning principles that are present in—but not exclusive to—the design of good video games. He further argues for the application of these principles into the classroom environment. What Video Games Teach Us about Learning and Literacy is a call to educators, teachers, parents and administrators to change the approach to pedagogy.
Judit Kormos is a Hungarian-born British linguist. She is a professor and the Director of Studies for the MA TESOL Distance programme at the Department of Linguistics and English Language at Lancaster University, United Kingdom. She is renowned for her work on motivation in second language learning, and self-regulation in second language writing. Her current interest is in dyslexia in second language learning.
Basu, Satabdi, Kinnebrew, John S., Dickes, Amanda, Farris, Amy Voss, Sengupta, Pratim, Winger, Jaymes, & Biswas, Gautam. (2012). A science learning environment using a computational thinking approach. Paper presented at the Proceedings of the 20th International Conference on Computers in Education, Singapore.
Beek, Wouter, Bredeweg, B., & Lautour, Sander. (2011). Context-dependent help for the DynaLearn modelling and simulation workbench In G. Biswas (Ed.), Artificial Intelligence in Education (pp. 4200–4422). Berlin: Springer-Verlag. Biswas, Gautam, Jeong, H., Kinnebrew, John S., Sulcer, Brian, & Roscoe, Rod D. (in press, 2012).
Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology Enhanced Learning. Biswas, Gautam, Leelawong, Krittaya, Schwartz, Daniel L., & Vye, N. J. (2005). Learning by teaching:
