Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 5 |
Since 2006 (last 20 years) | 8 |
Descriptor
Classification | 8 |
Computer Uses in Education | 8 |
Natural Language Processing | 8 |
Artificial Intelligence | 4 |
Accuracy | 3 |
Data Analysis | 3 |
Automation | 2 |
Computer System Design | 2 |
Internet | 2 |
Physics | 2 |
Probability | 2 |
More ▼ |
Source
International Educational… | 2 |
CALICO Journal | 1 |
Grantee Submission | 1 |
International Journal of… | 1 |
International Journal of… | 1 |
Journal of Educational… | 1 |
Physical Review Physics… | 1 |
Author
Akbar, Shoaib | 1 |
Andrews-Todd, Jessica | 1 |
Cosma, Georgina | 1 |
D'Mello, Sidney K. | 1 |
Danielle S. McNamara | 1 |
Dong, Muyao | 1 |
Fischer, Frank | 1 |
Foster, Jennifer | 1 |
Gehringer, Edward | 1 |
Jennifer Campbell | 1 |
Jia, Qinjin | 1 |
More ▼ |
Publication Type
Reports - Research | 6 |
Journal Articles | 5 |
Speeches/Meeting Papers | 2 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Education Level
Elementary Secondary Education | 1 |
High Schools | 1 |
Higher Education | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Postsecondary Education | 1 |
Secondary Education | 1 |
Audience
Location
Ireland | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Jennifer Campbell; Katie Ansell; Tim Stelzer – Physical Review Physics Education Research, 2024
Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM's Watson, and test its agreement with human coders using two different studies that gathered text responses in which students…
Descriptors: Artificial Intelligence, Physics, Natural Language Processing, Computer Uses in Education
Xiao, Yunkai; Zingle, Gabriel; Jia, Qinjin; Akbar, Shoaib; Song, Yang; Dong, Muyao; Qi, Li; Gehringer, Edward – International Educational Data Mining Society, 2020
Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text…
Descriptors: Peer Evaluation, Problems, Identification, Natural Language Processing
Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild
Pugh, Samuel L.; Subburaj, Shree Krishna; Rao, Arjun Ramesh; Stewart, Angela E. B.; Andrews-Todd, Jessica; D'Mello, Sidney K. – International Educational Data Mining Society, 2021
We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…
Descriptors: Problem Solving, Cooperation, Middle School Students, High School Students
Mihai Dascalu; Scott A. Crossley; Danielle S. McNamara; Philippe Dessus; Stefan Trausan-Matu – Grantee Submission, 2018
A critical task for tutors is to provide learners with suitable reading materials in terms of difficulty. The challenge of this endeavor is increased by students' individual variability and the multiple levels in which complexity can vary, thus arguing for the necessity of automated systems to support teachers. This chapter describes…
Descriptors: Reading Materials, Difficulty Level, Natural Language Processing, Artificial Intelligence
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Mu, Jin; Stegmann, Karsten; Mayfield, Elijah; Rose, Carolyn; Fischer, Frank – International Journal of Computer-Supported Collaborative Learning, 2012
Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also,…
Descriptors: Semantics, Classification, Syntax, Coding
Mozgovoy, Maxim; Kakkonen, Tuomo; Cosma, Georgina – Journal of Educational Computing Research, 2010
The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to…
Descriptors: Plagiarism, Alphabets, Internet, Ethics
Wagner, Joachim; Foster, Jennifer; van Genabith, Josef – CALICO Journal, 2009
A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different…
Descriptors: Sentences, Language Processing, Natural Language Processing, Grammar