Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
| Since 2022 (last 5 years) | 3 |
| Since 2017 (last 10 years) | 6 |
| Since 2007 (last 20 years) | 8 |
Descriptor
| Automation | 8 |
| Error Correction | 8 |
| Natural Language Processing | 8 |
| Artificial Intelligence | 5 |
| Feedback (Response) | 5 |
| Error Patterns | 3 |
| Foreign Countries | 3 |
| Grading | 3 |
| Undergraduate Students | 3 |
| Classification | 2 |
| Computation | 2 |
| More ▼ | |
Source
| International Educational… | 2 |
| Research-publishing.net | 2 |
| ACM Transactions on Computing… | 1 |
| Education and Information… | 1 |
| International Journal of… | 1 |
| Pegem Journal of Education… | 1 |
Author
| Desmarais, Michel, Ed. | 1 |
| Garnier, Marie | 1 |
| Hang Li | 1 |
| Harry Shomer | 1 |
| Hui Liu | 1 |
| Ian Sanders | 1 |
| Jiliang Tang | 1 |
| Joseph Krajcik | 1 |
| Kaiqi Yang | 1 |
| Kam Meng Goh | 1 |
| Kevin Haudek | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 7 |
| Journal Articles | 4 |
| Speeches/Meeting Papers | 2 |
| Collected Works - Proceedings | 1 |
Education Level
| Higher Education | 4 |
| Postsecondary Education | 4 |
| Elementary Education | 1 |
| Grade 8 | 1 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Yucheng Chu; Hang Li; Kaiqi Yang; Harry Shomer; Yasemin Copur-Gencturk; Leonora Kaldaras; Kevin Haudek; Joseph Krajcik; Namsoo Shin; Hui Liu; Jiliang Tang – International Educational Data Mining Society, 2025
Open-text responses provide researchers and educators with rich, nuanced insights that multiple-choice questions cannot capture. When reliably assessed, such responses have the potential to enhance teaching and learning. However, scaling and consistently capturing these nuances remain significant challenges, limiting the widespread use of…
Descriptors: Grading, Automation, Artificial Intelligence, Natural Language Processing
Olaperi Okuboyejo; Sigrid Ewert; Ian Sanders – ACM Transactions on Computing Education, 2025
Regular expressions (REs) are often taught to undergraduate computer science majors in the Formal Languages and Automata (FLA) course; they are widely used to implement different software functionalities such as search mechanisms and data validation in diverse fields. Despite their importance, the difficulty of REs has been asserted many times in…
Descriptors: Automation, Feedback (Response), Error Patterns, Error Correction
Wai Tong Chor; Kam Meng Goh; Li Li Lim; Kin Yun Lum; Tsung Heng Chiew – Education and Information Technologies, 2024
The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy…
Descriptors: Artificial Intelligence, Engineering Education, Taxonomy, Educational Objectives
Vittorini, Pierpaolo; Menini, Stefano; Tonelli, Sara – International Journal of Artificial Intelligence in Education, 2021
Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper…
Descriptors: Artificial Intelligence, Formative Evaluation, Summative Evaluation, Data
Miranty, Delsa; Widiati, Utami – Pegem Journal of Education and Instruction, 2021
Automated Writing Evaluation (AWE) has been considered a potential pedagogical technique that exploits technology to assist the students' writing. However, little attention has been devoted to examining students' perceptions of Grammarly use in higher education context. This paper aims to obtain information regarding the writing process and the…
Descriptors: Foreign Countries, Technology Uses in Education, Writing (Composition), Student Attitudes
Pareja-Lora, Antonio – Research-publishing.net, 2016
For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to…
Descriptors: Electronic Learning, Automation, Error Correction, Natural Language Processing
Garnier, Marie – Research-publishing.net, 2012
According to recent studies, there is a persistence of adverb placement errors in the written productions of francophone learners and users of English at an intermediate to advanced level. In this paper, we present strategies for the automatic detection and correction of errors in the placement of manner adverbs, using linguistic-based natural…
Descriptors: Form Classes (Languages), Error Correction, Natural Language Processing, Feedback (Response)
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis

Peer reviewed
Direct link
