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Gyeonggeon Lee; Xiaoming Zhai – TechTrends: Linking Research and Practice to Improve Learning, 2025
Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…
Descriptors: Artificial Intelligence, Computer Software, Teaching Methods, Learning Processes
Pedro Isaias, Editor; Demetrios G. Sampson, Editor; Dirk Ifenthaler, Editor – Cognition and Exploratory Learning in the Digital Age, 2024
The Cognition and Exploratory Learning in the Digital Age (CELDA) conference focuses on discussing and addressing the challenges pertaining to the evolution of the learning process, the role of pedagogical approaches and the progress of technological innovation, in the context of the digital age. In each edition, CELDA, gathers researchers and…
Descriptors: Artificial Intelligence, Cognitive Processes, Discovery Learning, Teaching Methods
Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
Zhongdi Wu; Eric Larson; Makoto Sano; Doris Baker; Nathan Gage; Akihito Kamata – Grantee Submission, 2023
In this investigation we propose new machine learning methods for automated scoring models that predict the vocabulary acquisition in science and social studies of second grade English language learners, based upon free-form spoken responses. We evaluate performance on an existing dataset and use transfer learning from a large pre-trained language…
Descriptors: Prediction, Vocabulary Development, English (Second Language), Second Language Learning