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Direct linkZhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
Rui Yao; Meilin Tian; Chi-Un Lei; Dickson K. W. Chiu – Education and Information Technologies, 2024
Sustainable Development Goals (SDG) 4.7 aims to ensure learners acquire the knowledge and skills for promoting sustainable development by 2030. Yet, Open Educational Resources (OERs) that connect the public with SDGs are currently limitedly assigned and insufficient to promote SDG and sustainability education to support the achievement of SDG 4.7…
Descriptors: Sustainable Development, Open Educational Resources, Sustainability, Classification
Xiang Feng; Keyi Yuan; Xiu Guan; Longhui Qiu – Interactive Learning Environments, 2024
Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…
Descriptors: MOOCs, Psychological Patterns, Artificial Intelligence, Prediction
Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
David Arthur; Hua-Hua Chang – Journal of Educational and Behavioral Statistics, 2024
Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…
Descriptors: Algorithms, Models, Computation, Cognitive Measurement
Ferrara, Steve; Qunbar, Saed – Journal of Educational Measurement, 2022
In this article, we argue that automated scoring engines should be transparent and construct relevant--that is, as much as is currently feasible. Many current automated scoring engines cannot achieve high degrees of scoring accuracy without allowing in some features that may not be easily explained and understood and may not be obviously and…
Descriptors: Artificial Intelligence, Scoring, Essays, Automation
Ingrisone, Soo Jeong; Ingrisone, James N. – Educational Measurement: Issues and Practice, 2023
There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering…
Descriptors: Identification, Cooperation, Computer Assisted Testing, Artificial Intelligence
Yibei Yin – International Journal of Web-Based Learning and Teaching Technologies, 2023
In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The…
Descriptors: College Students, Student Employment, Data Analysis, Artificial Intelligence
Gani, Mohammed Osman; Ayyasamy, Ramesh Kumar; Sangodiah, Anbuselvan; Fui, Yong Tien – Education and Information Technologies, 2023
The automated classification of examination questions based on Bloom's Taxonomy (BT) aims to assist the question setters so that high-quality question papers are produced. Most studies to automate this process adopted the machine learning approach, and only a few utilised the deep learning approach. The pre-trained contextual and non-contextual…
Descriptors: Models, Artificial Intelligence, Natural Language Processing, Writing (Composition)
Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
Joan Li; Nikhil Kumar Jangamreddy; Ryuto Hisamoto; Ruchita Bhansali; Amalie Dyda; Luke Zaphir; Mashhuda Glencross – Australasian Journal of Educational Technology, 2024
Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning. Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated…
Descriptors: Higher Education, Artificial Intelligence, Grading, Scoring Rubrics
Laura E. Matzen; Zoe N. Gastelum; Breannan C. Howell; Kristin M. Divis; Mallory C. Stites – Cognitive Research: Principles and Implications, 2024
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and…
Descriptors: Artificial Intelligence, Error Patterns, Decision Making, Models
Reese Butterfuss; Harold Doran – Educational Measurement: Issues and Practice, 2025
Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…
Descriptors: Alignment (Education), Academic Standards, Content Analysis, Concept Mapping
Emmanuel Fokides; Eirini Peristeraki – Education and Information Technologies, 2025
This research analyzed the efficacy of ChatGPT as a tool for the correction and provision of feedback on primary school students' short essays written in both the English and Greek languages. The accuracy and qualitative aspects of ChatGPT-generated corrections and feedback were compared to that of educators. For the essays written in English, it…
Descriptors: Artificial Intelligence, Error Correction, Feedback (Response), Elementary School Students
Alison M. O'Connor; Jennifer Gongola; Kaila C. Bruer; Thomas D. Lyon; Angela D. Evans – Applied Cognitive Psychology, 2025
The accurate detection of children's truthful and dishonest reports is essential as children can serve as important providers of information. Research using automated facial coding and machine learning found that children who were asked to lie about an event were more likely to look surprised when hearing the first question during an interview…
Descriptors: Deception, Nonverbal Communication, Recognition (Psychology), Children
