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Zixuan Ke – ProQuest LLC, 2024
The essence of human intelligence lies in its ability to learn continuously, accumulating past knowledge to aid in future learning and problem-solving endeavors. In contrast, the current machine learning paradigm often operates in isolation, lacking the capacity for continual learning and adaptation. This deficiency becomes apparent in the face of…
Descriptors: Computational Linguistics, Computer Software, Barriers, Artificial Intelligence
Hayriye Nevin Genc; Nuriye Kocak – Journal of Education in Science, Environment and Health, 2024
Understanding the trends and developments in artificial intelligence research in science education, which has rapidly advanced in recent years, is crucial for technological innovations and applications in education. Therefore, this study examines research on artificial intelligence in science education conducted between 2019 and 2023 (the last…
Descriptors: Bibliometrics, Information Retrieval, Databases, Artificial Intelligence
Hongwen Guo; Matthew S. Johnson; Kadriye Ercikan; Luis Saldivia; Michelle Worthington – Journal of Learning Analytics, 2024
Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough--educators need to know why students got low scores, how students engaged…
Descriptors: Artificial Intelligence, Learning Analytics, Learning Management Systems, Measurement
Andrea Horbach; Joey Pehlke; Ronja Laarmann-Quante; Yuning Ding – International Journal of Artificial Intelligence in Education, 2024
This paper investigates crosslingual content scoring, a scenario where scoring models trained on learner data in one language are applied to data in a different language. We analyze data in five different languages (Chinese, English, French, German and Spanish) collected for three prompts of the established English ASAP content scoring dataset. We…
Descriptors: Contrastive Linguistics, Scoring, Learning Analytics, Chinese
Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
Ayse Alkan – International Journal of Technology in Education and Science, 2024
Artificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI)…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Students with Disabilities
Riordan Alfredo; Vanessa Echeverria; Linxuan Zhao; LuEttaMae Lawrence; Jie Xiang Fan; Lixiang Yan; Xinyu Li; Zachari Swiecki; Dragan Gaševic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can coevolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be…
Descriptors: Learning Analytics, Learning Management Systems, Artificial Intelligence, Computer Software
Yamauchi, Taisei; Flanagan, Brendan; Nakamoto, Ryosuke; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki – Smart Learning Environments, 2023
In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and…
Descriptors: Mathematics Instruction, Classification, Algorithms, Barriers
Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
Xavier Ochoa; Xiaomeng Huang; Yuli Shao – Journal of Learning Analytics, 2025
Generative AI (GenAI) has the potential to revolutionize the analysis of educational data, significantly impacting learning analytics (LA). This study explores the capability of non-experts, including administrators, instructors, and students, to effectively use GenAI for descriptive LA tasks without requiring specialized knowledge in data…
Descriptors: Learning Analytics, Artificial Intelligence, Computer Software, Scores
Guang Jiang; Jiahui Zhu; Yunsong Li; Pengcheng An; Yunlong Wang – Education and Information Technologies, 2024
Teacher-student interaction (TSI) is essential for learning efficiency and harmonious teacher-student interpersonal relationships. However, studies on TSI support tools often focus on teacher needs while neglecting student needs and autonomy. To enhance both lecturer competence in delivering interpersonal interaction and student autonomy in TSI,…
Descriptors: Teacher Student Relationship, Computer Software, Teaching Methods, Computer Simulation
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
Tanjea Ane; Tabatshum Nepa – Research on Education and Media, 2024
Precision education derives teaching and learning opportunities by customizing predictive rules in educational methods. Innovative educational research faces new challenges and affords state-of-the-art methods to trace knowledge between the teaching and learning ecosystem. Individual intelligence can only be captured through knowledge level…
Descriptors: Artificial Intelligence, Prediction, Models, Teaching Methods
Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
Eran Hadas; Arnon Hershkovitz – Journal of Learning Analytics, 2025
Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This…
Descriptors: Creativity, Evaluation Methods, Computer Assisted Testing, Artificial Intelligence