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Teaching method
Context-based learning (CBL) refers to the use of real-life and fictitious examples in teaching environments in order to learn through the actual, practical
Context-based_learning
Learner-centric pedagogy
Problem-based learning (PBL) is a teaching method in which students aim to learn about a subject through the experience of solving an open-ended problem
Problem-based_learning
Structuring text as input to generative artificial intelligence
changes, in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn"
Prompt_engineering
Form of active learning
Inquiry-based learning (also spelled as enquiry-based learning in British English) is a form of active learning that starts by posing questions, problems
Inquiry-based_learning
Learner centric pedagogy
Project-based learning is a teaching method that uses a dynamic classroom approach in which students acquire deeper knowledge through active exploration
Project-based_learning
Theory and philosophy of learning
education) – Theory of knowledge Context-based learning Contextual learning Cooperative education – Type of education Design-based learning – Learner centric pedagogy
Experiential_learning
Game genre
problem solving. Game-based learning (GBL) is a type of game play that has defined learning outcomes. Generally, game-based learning is designed to balance
Educational_game
Learner centric pedagogy
Design-based learning (DBL), also known as design-based instruction, is an inquiry-based form of learning, or pedagogy, that is based on integration of
Design-based_learning
Learner centric pedagogy
instead of in a subject-based approach. Phenomenon-based learning includes both topical learning (also known as topic-based learning or instruction), where
Phenomenon-based_learning
Pedagogical approach
paradigm. In "strong" task-based learning lessons, learners are responsible for selecting the appropriate language for any given context themselves. The instructors
Task-based_language_teaching
Use of technology in education to enhance learning and teaching
encompasses several domains, including learning theory, computer-based training, online learning, and mobile learning (m-learning). The Association for Educational
Educational_technology
Framework for teaching and assessment of learning
type of education based on predetermined "competencies," which focuses on outcomes and real-world performance. Competency-based learning is sometimes presented
Competency-based_learning
Protocol for communicating between LLMs and applications
The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize the way artificial
Model_Context_Protocol
Educational strategy
policies to secure learning that meets the need of the workplace. Work-based learning (WBL) is defined differently across contexts: System / policy strand:
Work-based_learning
Narrative-based learning is a learning model grounded in the theory that humans define their experiences within the context of narratives – which serve
Narrative-based_learning
Concept that some difficulty can assist learning
A desirable difficulty, in the context of learning, refers to conditions or aspects of learning that require more effort, but which ultimately improve
Desirable_difficulty
Subset of artificial intelligence
person's height based on factors like age and genetics or forecasting future temperatures based on historical data. Similarity learning is an area of supervised
Machine_learning
Educational psychology concept
Transfer of learning occurs when people apply information, strategies, and skills they have learned to a new situation or context. Transfer is not a discrete
Transfer_of_learning
Process of acquiring new knowledge
of the context of the facts learned. Evidence-based learning is the use of evidence from well designed scientific studies to accelerate learning. Evidence-based
Learning
Social context in understanding culture
high-context and low-context cultures are ends of a continuum of how explicit the messages exchanged in a culture are and how important the context is in
High-context and low-context cultures
High-context_and_low-context_cultures
Learning technique performed with flashcards
Spaced repetition is an evidence-based learning technique that is usually performed with flashcards. Newly introduced and more difficult flashcards are
Spaced_repetition
Machine learning paradigm
than relying on externally-provided labels. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships
Self-supervised_learning
Highlighting a theme for teaching purposes
place-based education, project-based education, and cooperative learning. When thematic instruction takes place along with cooperative learning, the advantages
Thematic_learning
Software engineering concept
contextually appropriate responses. In deep learning-based language models like GPT-4 or BERT, the context model is an inherent part of the architecture
Context_model
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data
Transformer_(deep_learning)
Artificial intelligence model developed by TypeSafe AI
transformer models, it can perform zero-shot learning of input for categories provided at inference time, based on contextual information provided. TypeSafe
Jev_(AI_model)
Memorization technique based on repetition
Rote learning is a memorization technique based on repetition. The method rests on the premise that the recall of repeated material becomes faster the
Rote_learning
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Computational model used in machine learning
late 1940s, D. O. Hebb proposed a learning hypothesis based on neural plasticity that became known as Hebbian learning. It was used in many early neural
Neural network (machine learning)
Neural_network_(machine_learning)
Educational practice of interaction among students
Boud describe peer learning as a way of moving beyond independent to interdependent or mutual learning among peers. In this context, it can be compared
Peer_learning
Educational philosophy
Place-based education, sometimes called pedagogy of place, place-based learning, experiential education, community-based education, environmental education
Place-based_education
Machine learning strategy
active learning, hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of machine learning (e.g
Active learning (machine learning)
Active_learning_(machine_learning)
Type of machine learning model
many contexts. They are the basis for many modern chatbots, such as ChatGPT, Claude, Vibe, Gemini, Grok, Qwen and DeepSeek. LLMs are typically based on
Large_language_model
Theory of learning
provide the proper context and facilitate learning. Situated learning was first proposed by Jean Lave and Etienne Wenger as a model of learning in a community
Situated_learning
Machine learning technique
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (PDF). Advances in Neural Information Processing Systems. Vol. 35.
Fine-tuning_(deep_learning)
Type of large language model
used in generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large
Generative pre-trained transformer
Generative_pre-trained_transformer
Education focused on developing personal skills
central feature of LSBE pedagogy is its emphasis on experiential and context-based learning, in which educational activities are designed to reflect real-life
Life_skills-based_education
the sites where L2 learning occurs: the formal site (i.e. the educational context), and the informal site (i.e. the cultural context). Gardner argued that
Motivation in second-language learning
Motivation_in_second-language_learning
Class of statistical modeling methods
In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction
Conditional_random_field
Branch of machine learning
extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states
Deep_learning
Paradigm of the education field
Evidence-based education is related to evidence-based teaching, evidence-based learning, and school effectiveness research. The evidence-based education
Evidence-based_education
Theory that describes how students receive, process, and retain knowledge during learning
Geographical learning theory focuses on the ways that contexts and environments shape the learning process. Outside the realm of educational psychology
Learning_theory_(education)
Term in educational psychology
in learning, decisions are made based on properties alone and rely on simple criteria that do not require a lot of memory. Example of rule-based theory:
Concept_learning
Education practice
professional development and training settings. Since blended learning is highly context-dependent, a universal conception of it is difficult. Some reports
Blended_learning
Steps of using content based instruction for second language learners
that the curriculum is based on a certain subject matter and communicative competence is acquired in the context of learning about certain topics in
Content-based_instruction
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Term in education
learning environment can refer to an educational approach, cultural context, or physical setting (the learning space) in which teaching and learning occur
Learning_environment
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Study of religious thought and behavior
categories: the context-based model and content-based view of minimal counterintuitiveness. The context-based view emphasizes the role played by context in making
Cognitive_science_of_religion
Long-established customs traditionally used in schools
abandoned in favor of student centered and task-based approaches to learning. Depending on the context, the opposite of traditional education may be progressive
Traditional_education
Machine learning technique
In machine learning, attention is a method that determines the importance of, or "attends to", each component in a sequence relative to the other components
Attention_(machine_learning)
Educational software application
automated recommendations for courses based on a user's skill profile as well as extract metadata from learning materials to make such recommendations
Learning_management_system
American machine learning company
based in New York City. Its online platform allows users to share datasets and machine learning models. It also develops open-source machine learning
Hugging_Face
Set of learning techniques in machine learning
used, and not the ordering or entire set of context words. More recent transformer-based representation learning approaches attempt to solve this with word
Representation_learning
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
2020 text-generating language model
occupies 2 bytes. It has a context window size of 2,048 tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks.
GPT-3
Overview of and topical guide to machine learning
handling (GMDH) Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum
Outline_of_machine_learning
2017 paper conceptualizing the transformer-based deep learning architecture
research paper on machine learning authored by eight scientists and engineers working at Google. The paper introduced a new deep learning architecture known
Attention_Is_All_You_Need
Distance education using mobile device technology
M-learning, or mobile learning, is defined as "learning across multiple contexts, through social and content interactions, using personal electronic devices"
M-learning
is a timeline of machine learning. Major discoveries, achievements, milestones, and other major events in machine learning are included. History of artificial
Timeline_of_machine_learning
Approach to teaching children to read
It is based on the premise that learning to read English comes naturally to humans, especially young children, in the same way that learning to speak
Whole_language
Methods and principles in adult education
andragogy is based on a humanistic conception of self-directed and autonomous learners where teachers are defined as facilitators of learning. Although Malcolm
Andragogy
Improved recall when the context of a situation is the same
memory, state-dependent learning, cognitive context-dependent memory and mood-congruent memory. Research has also shown that context-dependence may play an
Context-dependent_memory
Paradigm of rule-based machine learning methods
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic
Learning_classifier_system
Theory of learning and behaviour
observing and imitating others. It states that learning is a cognitive process that occurs within a social context and can occur purely through observation
Social_learning_theory
Software agent which acts autonomously
expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired
Intelligent_agent
Intelligence in machines
to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is also
Artificial_intelligence
Educational technique
useful context in problem-based learning: Mark A Albanese, Laura C Dast (2013-10-22). Understanding Medical Education - Problem-based learning. doi:10
Active_learning
machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Deep learning architecture
integrate the entire sequence context and apply the most relevant expert for each token. Language modeling Transformer (machine learning model) State-space model
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Educational approach
authentic learning is an instructional approach that allows students to explore, discuss, and meaningfully construct concepts and relationships in contexts that
Authentic_learning
Concept in education and psychology
Contemporary theories emphasize the role of social and cultural contexts in children's learning and development. Rousseau's work on children's rights and the
Learning_through_play
scope that learners undertake in a particular context in order to attain specific learning outcomes A learning nugget task will take a prescribed length of
Learning_nugget
Education technology specification by IMS Global Learning Consortium
external systems. The LTI will also share learner information and the learning context shared by the LMS with the external systems. Like its predecessor SCORM
Learning Tools Interoperability
Learning_Tools_Interoperability
Type of data compression algorithm
active area of research in machine learning.[citation needed] The PAQ series of data compression programs use context mixing to assign probabilities to
Context_mixing
Education with small learning units
skill-based education but also for sustainable socioeconomic development, and without taking care of micro-perspectives in the context of learning, education
Microlearning
Use of computational tools for the study and application of linguistics
a limitation for the models at the time because the now available deep learning models were not available in late 1980s. It has been shown that languages
Computational_linguistics
Ongoing, voluntary, and self-motivated pursuit of knowledge
learning. Informal lifelong learning communities also exist around the world. In some contexts, the term "lifelong learning" evolved from the term "life-long
Lifelong_learning
Educational learning method using computer algorithms and AI
customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out"
Adaptive_learning
Instructional strategy and educational philosophy
student needs. Mastery learning (or, as it was initially called, "learning for mastery"; also known as "mastery-based learning") is an instructional strategy
Mastery_learning
Machine-learning process
approach is based on distributional learning. Algorithms using these approaches have been applied to learning context-free grammars and mildly context-sensitive
Grammar_induction
Reinforcement learning method
In reinforcement learning, error-driven learning is a method for adjusting a model's (intelligent agent's) parameters based on the difference between its
Error-driven_learning
Anglican theological college in the United Kingdom
the Diocese of Manchester, the college has pioneered patterns of context-based learning and innovative approaches to contextual theology for over twenty
Westcott_House,_Cambridge
Theory and practice of education
skills are imparted in an educational context, and it considers the interactions that take place during learning. Both the theory and practice of pedagogy
Pedagogy
Research field that lies at the intersection of machine learning and computer security
automatically craft binaries to evade learning-based detectors while preserving malicious functionality. Optimization-based attacks such as GAMMA use genetic
Adversarial_machine_learning
Overview of and topical guide to deep learning
and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks
Outline_of_deep_learning
Broadbent's Sugar Production Factory task[clarification needed]. The Instance-Based Learning Theory (IBLT) is a theory of how humans make decisions in dynamic tasks
Dynamic_decision-making
Theories that aim to account for differences in individuals' learning
Learning styles refer to a range of theories that aim to account for differences in individuals' learning. Although there is ample evidence that individuals
Learning_styles
Learning that occurs through observing the behaviour of others
Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various
Observational_learning
Training methods used after LLM pretraining
(2024-05-31). "YaRN: Efficient Context Window Extension of Large Language Models". International Conference on Learning Representations. 2024: 31932–31951
Training of large language models
Training_of_large_language_models
Branch of analytics
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and
Learning_analytics
Collecting and analyzing telemetry from AI systems in production
comes from control theory and software engineering, to systems based on machine learning models, and in particular to large language models (LLMs) and
AI_observability
Method in natural language processing
models" to reduce the high dimensionality of word representations in contexts by "learning a distributed representation for words". A study published in NeurIPS
Word_embedding
Model of learning
summary is based upon Rousse and Dreyfus, "Revisiting the Six Stages of Skill Acquisition." Stage 1: Novice Novices rely heavily on context-free rules
Dreyfus model of skill acquisition
Dreyfus_model_of_skill_acquisition
ELearning software
evaluation in e-learning context: Selecting the appropriate tasks and reporting usability problems. International Conference on eLearning (ICEL), 2007 Tesseris
EFront_(eLearning_software)
2023 text-generating language model
aligned by reinforcement learning from human feedback (RLHF), suggestions to assassinate people on a list were elicited from the base model by a red team investigator
GPT-4
Methods to achieve fluency in a target language
of the planning, organization and evaluation of the learning process; Compensatory—using context to make up for missing information in reading and writing;
Language_learning_strategies
Class of artificial neural network
forward and a learning rule is applied. The fixed back-connections save a copy of the previous values of the hidden units in the context units (since they
Recurrent_neural_network
Educational approach aiming to promote learning by using video game design and elements
Similarly, in learning contexts, the unique needs of each set of learners, along with the specific learning objectives relevant to that context must inform
Gamification_of_learning
A learning object is "a collection of content items, practice items, and assessment items that are combined based on a single learning objective". The
Learning_object
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