Publication Date
| In 2025 | 0 |
| Since 2024 | 2 |
| Since 2021 (last 5 years) | 2 |
| Since 2016 (last 10 years) | 3 |
| Since 2006 (last 20 years) | 6 |
Descriptor
| Natural Language Processing | 6 |
| Reliability | 6 |
| Semantics | 6 |
| Computer Software | 4 |
| Computational Linguistics | 3 |
| Artificial Intelligence | 2 |
| Classification | 2 |
| Coding | 2 |
| Essays | 2 |
| Man Machine Systems | 2 |
| Scoring | 2 |
| More ▼ | |
Source
| Grantee Submission | 2 |
| Discourse Processes: A… | 1 |
| European Journal of Education | 1 |
| Journal of Computer Assisted… | 1 |
| ProQuest LLC | 1 |
Author
Publication Type
| Journal Articles | 4 |
| Reports - Research | 4 |
| Dissertations/Theses -… | 1 |
| Reports - Descriptive | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
| Netherlands | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkDadi Ramesh; Suresh Kumar Sanampudi – European Journal of Education, 2024
Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…
Descriptors: Scoring, Essays, Writing Evaluation, Memory
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
Download full textCai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan; Shaffer, David Williamson; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2019
Coding is a process of assigning meaning to a given piece of evidence. Evidence may be found in a variety of data types, including documents, research interviews, posts from social media, conversations from learning platforms, or any source of data that may provide insights for the questions under qualitative study. In this study, we focus on text…
Descriptors: Semantics, Computational Linguistics, Evidence, Coding
Olmos, Ricardo; Jorge-Botana, Guillermo; León, José Antonio; Escudero, Inmaculada – Discourse Processes: A multidisciplinary journal, 2014
This study presents a new approach for transforming the latent representation derived from a Latent Semantic Analysis (LSA) space into one where dimensions have nonlatent meanings. These meanings are based on lexical descriptors, which are selected by the LSA user. The authors present three analyses that provide examples of the utility of this…
Descriptors: Natural Language Processing, Methods, Validity, Reliability
Crossley, Scott A.; Kyle, Kristopher; McNamara, Danielle S. – Grantee Submission, 2015
This study investigates the relative efficacy of using linguistic micro-features, the aggregation of such features, and a combination of micro-features and aggregated features in developing automatic essay scoring (AES) models. Although the use of aggregated features is widespread in AES systems (e.g., e-rater; Intellimetric), very little…
Descriptors: Essays, Scoring, Feedback (Response), Writing Evaluation
Tan, Lin – ProQuest LLC, 2009
Commenting source code has long been a common practice in software development. This thesis, consisting of three pieces of work, made novel use of the code comments written in natural language to improve software reliability. Our solution combines Natural Language Processing (NLP), Machine Learning, Statistics, and Program Analysis techniques to…
Descriptors: Computer Software, Coding, Reliability, Improvement
