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Data-driven learning (DDL) is an approach to foreign language learning. Whereas most language learning is guided by teachers and textbooks, data-driven
Data-driven_learning
Unit of information
journalism Data-driven testing Data-driven learning Data-driven science Data-driven control system Data-driven marketing Digital privacy Environmental data rescue
Data
Class of computational model
Data-driven models are a class of computational models that primarily rely on historical data collected throughout a system's or process' lifetime to
Data-driven_model
Field of study to extract knowledge from data
Dehmer, Matthias (2018). "Defining data science by a data-driven quantification of the community". Machine Learning and Knowledge Extraction. 1: 235–251
Data_science
Subset of artificial intelligence
foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical
Machine_learning
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
Error-driven_learning
Choosing based on factual information
process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation
Data-informed_decision-making
Data-driven instruction is an educational approach that relies on information to inform teaching and learning. The idea refers to a method teachers use
Data-driven_instruction
Overview of and topical guide to machine learning
Supervised learning, where the model is trained on labeled data Unsupervised learning, where the model tries to identify patterns in unlabeled data Reinforcement
Outline_of_machine_learning
Topics referred to by the same term
League, a Dutch offshoot of the English Defence League Data-driven learning, an approach to learning foreign languages Den Danske Landinspektørforening,
DDL
semi-supervised machine-learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
American mechanical engineer
Professor of AI & Data-Driven Engineering at the University of Washington, where his research focuses on applying machine learning to dynamical systems
Steven_L._Brunton
Tasks in machine learning
function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Software engineering approach to designing and developing information systems
and data science, which often involves machine learning. Making the data usable usually involves substantial computing and storage, as well as data processing
Data_engineering
intelligence (AI) to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy, and AI literacy
Artificial intelligence in education
Artificial_intelligence_in_education
Process of analyzing large data sets
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics
Data_mining
Domain driven data mining is a data mining methodology for discovering actionable knowledge and deliver actionable insights from complex data and behaviors
Domain_driven_data_mining
Computer program that constructs concordances from text corpora
corpora. Tim Johns at the University of Birmingham coined the term data-driven learning (DDL) around 1990 to describe a pedagogical approach in which language
Concordancer
Process of linking data objects in distinct models
purchase orders and invoices. Modern data mapping processes increasingly use artificial intelligence (AI) and machine learning (ML) techniques to automate and
Data_mapping
Algorithmically generated data that have a similar distribution as sampled data
mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications
Synthetic_data
Group of samples that have been tagged with one or more labels
unlabeled data. Algorithmic decision-making is subject to programmer-driven bias as well as data-driven bias. Training data that relies on bias labeled data will
Labeled_data
Interdisciplinary field of study
closely related to astrostatistics. Data-driven astronomy (DDA) refers to the use of data science and machine learning techniques to analyze large astronomical
Astroinformatics
Academic journal
research on machine learning, knowledge extraction and related areas of data-driven artificial intelligence. It is published by MDPI and was launched in
Machine Learning and Knowledge Extraction
Machine_Learning_and_Knowledge_Extraction
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
Software development process
"Comparing Domain-Driven Design with Model-Driven Engineering". Modeling Languages. Retrieved 2021-08-05. Learning Domain-Driven Design: Aligning Software
Domain-driven_design
Open source platform
large-scale data analysis and model deployment. H2O is primarily used by data scientists and developers for statistical modeling and data-driven decision-making
H2O_(software)
Javascript library for data visualization
js (also known as D3, short for Data-Driven Documents) is a JavaScript library for producing dynamic, interactive data visualizations in web browsers.
D3.js
strongly associated with the origins and development of data-driven learning (DDL), an approach to learning foreign languages which has learners use the output
Tim_Johns
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
Learning technique
use of concordancers in the language classroom with his concept of data-driven learning (DDL). DDL encourages learners to work out their own rules about
Computer-assisted language learning
Computer-assisted_language_learning
Field of machine learning
and unsupervised learning algorithms respectively attempt to discover patterns in labeled and unlabeled data, reinforcement learning involves training
Reinforcement_learning
Family of control systems
Data-driven control systems are a broad family of control systems, in which the identification of the process model and/or the design of the controller
Data-driven_control_system
Dynamic Data Driven Applications Systems (DDDAS) is a paradigm whereby the computation and instrumentation aspects of an application system are dynamically
Dynamic Data Driven Applications Systems
Dynamic_Data_Driven_Applications_Systems
Online project collecting example sentences
Tatoeba Corpus are not all authentic, they are sometimes used to build data-driven learning applications. BES (Basic English Sentence) Search is a non-commercial
Tatoeba
Journalistic process
Data journalism or data-driven journalism (DDJ) is journalism based on the filtering and analysis of large data sets for the purpose of creating or elevating
Data_journalism
Process supporting machine learning
processing, requires large volumes of annotated data. Annotation choices determine how machine learning algorithms recognize patterns and also drive the
Data_annotation
Prediction of flooding in a specific area
adaptive learning capabilities of data-driven models. An example of a hybrid model is coupling a hydrological model with a machine learning algorithm
Flood_forecasting
strengthen AI-driven research, innovation, and skill development. For public officials, the iGOT-AI Mission Karmayogi incorporates AI-driven learning recommendations
Artificial intelligence industry in India
Artificial_intelligence_industry_in_India
Branch of machine learning
Fundamentally, deep learning refers to a class of machine learning algorithms in which a hierarchy of layers is used to transform input data into a progressively
Deep_learning
Text corpus of British English
language leaner and is referred to as "data-driven learning" by Tim Johns. The corpus data used for data-driven learning is relatively smaller, and consequently
British_National_Corpus
Technique to solve partial differential equations
the data-driven discovery of PDEs results in computing the unknown state u(t,x) and learning model parameters λ that best describe the observed data: ut+𝒩[u;λ]=0
Physics-informed neural networks
Physics-informed_neural_networks
Mechanism for enabling artificial agents to exhibit curiosity
Empirical data from psychology were computationally simulated and accounted for using this model. Intrinsically motivated (or curiosity-driven) learning is an
Intrinsic motivation (artificial intelligence)
Intrinsic_motivation_(artificial_intelligence)
English academic and writer
communication. Evidence-based Health Communication (2006) — Advocates for data-driven learning in health communication. Madness in Post-1945 British and American
Paul_Crawford_(academic)
Type of air data system
air data when sensor fusion and real-time computing are required. Other non-conventional methods such as data-driven learning or machine learning based
Synthetic_air_data_system
Computational model used in machine learning
The system is driven by the interaction between cognition and emotion. Given the memory matrix, W =||w(a,s)||, the crossbar self-learning algorithm in
Neural network (machine learning)
Neural_network_(machine_learning)
Process of acquiring new knowledge
interacts with the e-learning environment, it is called augmented learning. By adapting to the needs of individuals, the context-driven instruction can be
Learning
Artificial intelligence in IT operations
the use of artificial intelligence, machine learning, and big data analytics to automate and enhance data center management. It helps organizations manage
AIOps
Form of advertising
Targeted advertising or data-driven marketing is a form of advertising, including online advertising, that is directed towards an audience with certain
Targeted_advertising
building up machine learning models based on data collected from numerical simulations or physical experiments. The machine learning models for fitness
Fitness_approximation
Organization of research institutions
from ICSPR data using an instructor-predefined subset of variables Data Driven Learning Guides – enhance teaching of core concepts in the social sciences
Inter-university Consortium for Political and Social Research
Inter-university_Consortium_for_Political_and_Social_Research
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Relationship between proficiency and experience
a learning curve Proficiency (test score)Experience (hours spent)01234503691215Proficiency (test score)Example of a steep learning curve A learning curve
Learning_curve
Capability that enables an organization to ensure high data quality
While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external
Data_governance
Investment fund using mathematical methods
that relies on systematic, data-driven methods, such as mathematical models, statistical techniques, AI, and machine learning, to make investment decisions
Quantitative_fund
Machine learning that combines deep learning and reinforcement learning
supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images)
Deep_reinforcement_learning
Computer analysis of aspects of food
field combining computational science with culinary studies. It applies data-driven techniques to analyze various aspects of food, including recipes, flavors
Computational_gastronomy
Type of machine learning model
A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing
Large_language_model
Use of technology in education to enhance learning and teaching
addition to the need for promoting learning on a larger scale. Over the years, a combination of cognitive science and data-driven techniques have enhanced the
Educational_technology
Title given to a small number of open-source software development leaders
Name Project Type Ref. Sylvain Benner Spacemacs Community-driven Emacs distribution Vitalik Buterin Ethereum Blockchain-based cryptocurrency [better source needed]
Benevolent_dictator_for_life
Facility used to house computer servers
received, a tension that has driven several states to reassess their incentive structures. In 2026, it was reported that data centers were becoming a target
Data_center
Sports betting analytics platform
a sports betting analytics platform that provides predictive tools, data-driven insights, news, and betting content for sports fans and bettors. Operating
Dimers
multidisciplinary field that studies history through machine learning and other data-driven, computational approaches. International Society for Computational
Computational_history
Automated recognition of patterns and regularities in data
statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern
Pattern_recognition
Educational technique
utilizing learning strategies that can include small-group work, role-play and simulations, data collection and analysis, active learning is purported
Active_learning
Specialized data centers designed for artificial intelligence
and running artificial intelligence (AI) and machine learning models. Unlike general-purpose data centers, they are often optimized for the parallel processing
AI_data_center
Disciplines of managing data as a resource
from data. Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics
Data_management
Change of statistical properties over time
predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens
Concept_drift
Subfield of artificial intelligence
computational models of learning from data. At the same time, it seeks to address deep learning’s main limitations: lack of reliability, data and energy efficiency
Neuro-symbolic_AI
Engineering applied to artificial intelligence
language. The process begins with text preprocessing to prepare data for machine learning models. Recent advancements, particularly transformer-based models
Artificial intelligence engineering
Artificial_intelligence_engineering
Visual representation of data
data, explore the structures and features of data, and assess outputs of data-driven models. Data and information visualization can be part of data storytelling
Data and information visualization
Data_and_information_visualization
Use of artificial intelligence in the automation of electronic design
design cycles. AI Driven Design Automation uses several methods, including machine learning, expert systems, and reinforcement learning. These are used
AI-driven_design_automation
Cloud-based service and infrastructure
series of modular cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. It runs on the
Google_Cloud_Platform
Software for understanding biological data
prediction, this proved difficult. Machine learning techniques such as deep learning can learn features of data sets rather than requiring the programmer
Machine_learning_in_biology
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
through data-driven machine learning are application scenarios from the Machinery & Equipment application area. In real-world production processes, data is
Artificial intelligence in industry
Artificial_intelligence_in_industry
Development of artificial intelligence
their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research
Causal_AI
Machine learning method
Logic Learning Machine. Also, an LLM version devoted to regression problems was developed. Like other machine learning methods, LLM uses data to build
Logic_learning_machine
AI whose outputs can be understood by humans
Transactions on Machine Learning Research. arXiv:2211.08425. Retrieved 2025-11-13. Martens, David; Provost, Foster (2014). "Explaining data-driven document classifications"
Explainable artificial intelligence
Explainable_artificial_intelligence
Cloud computing platform by Microsoft
is a fully managed cloud data warehouse. Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for
Microsoft_Azure
Open-source software library developed by Yandex
best machine learning tools". InfoWorld. "State of Data Science and Machine Learning 2020". "State of Data Science and Machine Learning 2021". "PyPI Stats
CatBoost
British autonomous vehicle technology company
learns to drive predominantly using camera data and machine learning. The company refers to its AI-driven driving software as an “Embodied AI” or AI Driver
Wayve
Measure of required computing power
of computing power or computational resources required to train machine learning and large language models. The term "compute" has also been more broadly
Compute_(machine_learning)
Computer scientist and professor
Stanford, where his doctoral research focused on robot learning, optimal control, and data-driven methods for acquiring control policies in complex robotic
Sergey_Levine
Data-driven algorithm
identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. Given a series of snapshots of a dynamical
Sparse identification of non-linear dynamics
Sparse_identification_of_non-linear_dynamics
AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning.
Applications of artificial intelligence
Applications_of_artificial_intelligence
Data analysis techniques for fraud detection
these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions
Data analysis for fraud detection
Data_analysis_for_fraud_detection
deep learning models for edge devices TensorRT-LLM — Nvidia toolkit for optimizing and deploying large language models on GPUs EDLUT – event-driven neural
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
error learning methods neural networks detect patterns existing within a data set ignoring data that is not significant while emphasizing the data which
Artificial intelligence in marketing
Artificial_intelligence_in_marketing
Class of artificial neural network
probability of the data. Given a lot of learnable predictability in the incoming data sequence, the highest level RNN can use supervised learning to easily classify
Recurrent_neural_network
Form of active learning
science could be a flexible and multi-directional inquiry driven process of thinking and learning. Schwab believed that science in the classroom should more
Inquiry-based_learning
American neuroscientist
initiative established under his tenure is the BrainHealth Databank, a data-driven learning health system integrating AI and computational models with mental
Sean_Hill_(scientist)
Biomedical informatics leader and NLM Director
"Algorithmovigilance—Advancing Methods to Analyze and Monitor Artificial Intelligence–Driven Health Care for Effectiveness and Equity". JAMA Network Open. 4 (4): e214622
Peter_Embí
Software creating a unified customer database accessible to other systems
the recency of their engagement. Predictive and AI-driven segmentation, which uses machine learning models to identify high-value customers, assess churn
Customer_data_platform
Hardware acceleration unit for artificial intelligence tasks
applications include algorithms for robotics, Internet of things, and data-intensive or sensor-driven tasks. They are often manycore or spatial designs and focus
Neural_processing_unit
Methods in artificial intelligence research
search algorithms for Boolean satisfiability are WalkSAT, conflict-driven clause learning, and the DPLL algorithm. For adversarial search when playing games
Symbolic artificial intelligence
Symbolic_artificial_intelligence
during the AI boom of the 2020s. Organized opposition to AI data centers has been driven by concerns about their impact on nearby residents, in the form
Opposition_to_AI_data_centers
RapidMiner provides integrated text mining, data preparation, predictive analytics, machine learning and deep learning. Data analysis frameworks provide a structured
Data_analysis
Programming language
Carolina State University. Its primary applications include data mining and machine learning. The SAS language runs under compilers such as the SAS System
SAS_language
Extremely large or complex datasets
collection, big data has low cost per data point, applies analysis techniques via machine learning and data mining, and includes diverse and new data sources
Big_data
American private school network
2014. The network uses a proprietary instructional model called 2 Hour Learning, which replaces traditional teachers with "guides" and relies on software-based
Alpha_School
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