Publication Date
In 2025 | 6 |
Since 2024 | 11 |
Since 2021 (last 5 years) | 44 |
Since 2016 (last 10 years) | 82 |
Since 2006 (last 20 years) | 163 |
Descriptor
Classification | 209 |
Error Patterns | 209 |
Foreign Countries | 67 |
Error Analysis (Language) | 39 |
English (Second Language) | 37 |
Second Language Learning | 36 |
Comparative Analysis | 31 |
Grammar | 29 |
Models | 29 |
Teaching Methods | 27 |
Second Language Instruction | 24 |
More ▼ |
Source
Author
Publication Type
Education Level
Audience
Researchers | 5 |
Practitioners | 2 |
Teachers | 2 |
Students | 1 |
Location
Spain | 7 |
China | 5 |
Greece | 5 |
France | 3 |
South Africa | 3 |
Australia | 2 |
Canada | 2 |
Indiana | 2 |
Indonesia | 2 |
Iran | 2 |
Malaysia | 2 |
More ▼ |
Laws, Policies, & Programs
Individuals with Disabilities… | 1 |
Assessments and Surveys
What Works Clearinghouse Rating
David Wees – Natural Sciences Education, 2024
Requiring students to create weed collections is a common technique for teaching weed identification. Data compiled over 18 years from students' weed collections in a college-level course included over 350 species of plants. Almost half of the specimens belonged to the Asteraceae or Poaceae. The 30 most frequently collected species accounted for…
Descriptors: Agricultural Education, Plants (Botany), Identification, Teaching Methods
Lauren A. Mason; Abigail Miller; Gregory Hughes; Holly A. Taylor – Cognitive Research: Principles and Implications, 2025
False alarming, or detecting an error when there is not one, is a pervasive problem across numerous industries. The present study investigated the role of elaboration, or additional information about non-error differences in complex visual displays, for mitigating false error responding. In Experiment 1, learners studied errors and non-error…
Descriptors: Error Correction, Error Patterns, Evaluation Methods, Visual Aids
Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
Chi Hong Leung; Winslet Ting Yan Chan – Asian Journal of Contemporary Education, 2025
This paper explores the efficacy of ChatGPT, a generative artificial intelligence in educational contexts, particularly concerning its potential to assist students in overcoming academic challenges while highlighting its limitations. ChatGPT is suitable for solving general problems. When a student comes across academic challenges, ChatGPT may…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Error Patterns
Cai, Zhiqiang; Marquart, Cody; Shaffer, David W. – International Educational Data Mining Society, 2022
Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex…
Descriptors: Coding, Classification, Artificial Intelligence, Engineering Education
Kwaku Adu-Gyamfi; Kayla Chandler; Anthony Thompson – School Science and Mathematics, 2025
The challenge posed by algebra story problems creates a significant hurdle for many students, transcending both the mathematical content of the problem and the specific instructional background received. This study offers a distinctive contribution to the existing literature by focusing on the cognitive conditions essential for comprehension in…
Descriptors: Algebra, Mathematics Instruction, Barriers, Cognitive Processes
Balqis Albreiki; Tetiana Habuza; Nishi Palakkal; Nazar Zaki – Education and Information Technologies, 2024
The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…
Descriptors: Multivariate Analysis, Graphs, Identification, At Risk Students
Ben-Yaacov, Anat; Hershkovitz, Arnon – Journal of Educational Computing Research, 2023
Block programming has been suggested as a way of engaging young learners with the foundations of programming and computational thinking in a syntax-free manner. Indeed, syntax errors--which form one of two broad categories of errors in programming, the other one being logic errors--are omitted while block programming. However, this does not mean…
Descriptors: Programming, Computation, Thinking Skills, Error Patterns
Sakyiwaa Boateng; Sizwe J. C. Masuku – Journal of Baltic Science Education, 2025
Electricity and magnetism are fundamental areas of physics and are integral to science curricula at various educational levels. However, this area has been reported to contain several concepts that students find challenging, leading to perspectives that diverge from scientifically accepted views. This study examines the errors made by physics…
Descriptors: Physics, Science Teachers, Preservice Teachers, Teacher Education Programs
Guozhu Ding; Xiangyi Shi; Shan Li – Education and Information Technologies, 2024
In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…
Descriptors: Programming, Computer Science Education, Classification, Graphs
Jiang, Shiyan; Nocera, Amato; Tatar, Cansu; Yoder, Michael Miller; Chao, Jie; Wiedemann, Kenia; Finzer, William; Rosé, Carolyn P. – British Journal of Educational Technology, 2022
To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K-12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created,…
Descriptors: High School Students, Data, Artificial Intelligence, Mathematical Models
Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Epp, Carrie Demmans – International Educational Data Mining Society, 2022
The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…
Descriptors: Online Courses, Group Discussion, Learner Engagement, Student Participation
Ehrlich, Sara Zadunaisky; Stavans, Anat; Seroussi, Batia – Elementary School Journal, 2022
Spelling is a significant parameter in establishing text quality. This study aims to describe the developmental path of spelling errors in expository texts, argumentative and descriptive--written by Hebrew speaking children from second through fifth grades. Bearing in mind different genre requirements, we aimed to determine whether genre type…
Descriptors: Semitic Languages, Spelling, Error Patterns, Classification
Steven J. Pentland; Christie M. Fuller; Lee A. Spitzley; Douglas P. Twitchell – International Journal of Social Research Methodology, 2023
The analysis of spoken language has been integral to a breadth of research in social science and beyond. However, for analyses to occur with efficiency, language must be in the form of computer-readable text. Historically, the speech-to-text process has occurred manually using human transcriptionists. Automated speech recognition (ASR) is…
Descriptors: Accuracy, Social Science Research, Classification, Reading Processes
Salem, Alexandra C.; Gale, Robert; Casilio, Marianne; Fleegle, Mikala; Fergadiotis, Gerasimos; Bedrick, Steven – Journal of Speech, Language, and Hearing Research, 2023
Purpose: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia's naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similarity to the target) are important for…
Descriptors: Semantics, Computer Software, Aphasia, Classification