Publication Date
| In 2026 | 3 |
| Since 2025 | 323 |
| Since 2022 (last 5 years) | 1623 |
| Since 2017 (last 10 years) | 3459 |
| Since 2007 (last 20 years) | 7398 |
Descriptor
Source
Author
Publication Type
Education Level
Audience
| Practitioners | 824 |
| Teachers | 823 |
| Researchers | 200 |
| Students | 116 |
| Policymakers | 44 |
| Administrators | 34 |
| Parents | 26 |
| Community | 6 |
| Counselors | 3 |
| Media Staff | 2 |
Location
| Australia | 217 |
| Turkey | 141 |
| United States | 113 |
| China | 108 |
| Canada | 102 |
| United Kingdom | 79 |
| Indonesia | 70 |
| California | 67 |
| Taiwan | 67 |
| United Kingdom (England) | 67 |
| Spain | 66 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
| Meets WWC Standards without Reservations | 15 |
| Meets WWC Standards with or without Reservations | 22 |
| Does not meet standards | 6 |
Yang Haodong; Liu Jialin; Wang Gaofeng – Research in Higher Education, 2025
With the increasingly prominent characteristics of data-intensive and AI-driven scientific paradigms, computing power has become a crucial pillar of research activities. This study aims to examine the knowledge innovation effects of university supercomputing development by theoretically proposing two mechanisms: the efficiency effect (including…
Descriptors: Foreign Countries, Universities, Computers, Innovation
Kaitlyn G. Fitzgerald; Elizabeth Tipton – Journal of Educational and Behavioral Statistics, 2025
This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than…
Descriptors: Data Use, Computation, Effect Size, Meta Analysis
Pu Wang; Yifeng Lin; Tiesong Zhao – Education and Information Technologies, 2025
With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high…
Descriptors: Supervision, Artificial Intelligence, Technology Uses in Education, Automation
Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
J. S. Allison; L. Santana; I. J. H. Visagie – Teaching Statistics: An International Journal for Teachers, 2025
Given sample data, how do you calculate the value of a parameter? While this question is impossible to answer, it is frequently encountered in statistics classes when students are introduced to the distinction between a sample and a population (or between a statistic and a parameter). It is not uncommon for teachers of statistics to also confuse…
Descriptors: Statistics Education, Teaching Methods, Computation, Sampling
Wei Zhang; Xinyao Zeng; Lingling Song – Education and Information Technologies, 2025
Computational thinking (CT) assessment is crucial for testing the effectiveness of CT skills development. However, the exploration of CT assessment in the context of text-based programming is in its initial stages. The intrinsic relationship between the core skills of text-based programming and the core elements of CT isn't analyzed in depth in…
Descriptors: Mental Computation, Programming, College Students, Evaluation
Dana Christensen – Journal of Educational Computing Research, 2025
Increased technological advances within marine biology requires professionals to become versed in interdisciplinary computer-based skills. Computational thinking (CT) is a contemporary concept used in educational settings across the globe to meet this need. CT has been incorporated into many curricula; however, incorporation strategies are vague…
Descriptors: Computation, Thinking Skills, Marine Biology, Introductory Courses
Roy Levy; Daniel McNeish – Journal of Educational and Behavioral Statistics, 2025
Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…
Descriptors: Models, Statistical Analysis, Measurement, Data Interpretation
Victoria Macann; Aman Yadav – Education and Information Technologies, 2025
Computational Thinking (CT) is viewed as a set of foundation skills required to solve problems efficiently and effectively, with or without the use of technology. It has also been argued that CT can provide connections between computing and other core curriculum areas which can be beneficial for student learning outcomes. However, there are still…
Descriptors: Computation, Thinking Skills, Elementary School Teachers, Teaching Methods
Pablo A. Mitnik – Sociological Methods & Research, 2025
Although there is an extensive methodological literature on the measurement of intergenerational income mobility, there has been limited research on the conceptual interpretation of mobility measures and the methodological implications of those interpretations. In this article, I focus on the three measures of mobility most frequently used in the…
Descriptors: Social Mobility, Income, Correlation, Measurement Techniques
Amber Simpson; Rebecca Borowski; Ashleigh Colquhoun; Zhengqi Hu – Early Childhood Education Journal, 2025
With the increase of computational thinking (CT) tools in education, there are questions as to whether and how CT might support and/or hinder algebraic thinking of young children. Utilizing seeds of algebraic thinking, we add to this scholarly discussion by presenting examples from a CT activity with four-year old children in which we illustrate…
Descriptors: Preschool Children, Mathematics Education, Computation, Thinking Skills
Zübeyde ER; Seniye Renan Sezer – SAGE Open, 2025
This study was conducted to develop and evaluate the effectiveness of the Fermi problem approach in enhancing measurement estimation skills in mathematics. Employing an action research design, 24 seventh-grade students (13 girls and 11 boys) from a southern Turkish middle school were selected using criterion sampling. Quantitative data were…
Descriptors: Computation, Mathematics Skills, Middle School Students, Mathematics Instruction
Sarah Alahmadi; Christine E. DeMars – Journal of Educational Measurement, 2025
Inadequate test-taking effort poses a significant challenge, particularly when low-stakes test results inform high-stakes policy and psychometric decisions. We examined how rapid guessing (RG), a common form of low test-taking effort, biases item parameter estimates, particularly the discrimination and difficulty parameters. Previous research…
Descriptors: Guessing (Tests), Computation, Statistical Bias, Test Items
López-Barrientos, José Daniel; Silva, Eliud; Lemus-Rodríguez, Enrique – Teaching Statistics: An International Journal for Teachers, 2023
We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete-type random variable, and a confidence interval for a large sample. Moreover, we pay…
Descriptors: Statistics Education, Mathematical Concepts, Multiplication, Misconceptions
Masaya Okada; Koryu Nagata; Nanae Watanabe; Masahiro Tada – IEEE Transactions on Learning Technologies, 2024
A learner can autonomously acquire knowledge by experiencing the world, without necessarily being explicitly taught. The contents and ways of this type of real-world learning are grounded on his/her surroundings and are self-determined by computing real-world information. However, conventional studies have not modeled, observed, or understood a…
Descriptors: Computation, Learning Analytics, Experiential Learning, Self Management

Peer reviewed
Direct link
