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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
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Zi Xiang Poh; Ean Teng Khor – International Journal on E-Learning, 2024
Machine learning and data mining techniques have been widely used in educational settings to identify the important features that tend to influence students' learning performance and predict their future performance. However, there is little to no research done in the context of Singapore's education. Hence, this study aims to fill the gap by…
Descriptors: Learning Analytics, Goodness of Fit, Academic Achievement, Online Courses
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Bull, Susan – International Journal of Artificial Intelligence in Education, 2021
For the special issue of the International Journal of Artificial Intelligence in Education dedicated to the memory of Jim Greer, this paper highlights some of Jim's extensive and always-timely contributions to the field: from his early AI-focussed research on intelligent tutoring systems, through a variety of applications deployed to support…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Educational Research, College Students
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Cam, Emre; Ozdag, Muhammet Esat – Malaysian Online Journal of Educational Technology, 2021
This study aims at finding out students' course success in vocational courses of computer and instructional technologies department by means of machine learning algorithms. In the scope of the study, a dataset was formed with demographic information and exam scores obtained from the students studying in the Department of Computer Education and…
Descriptors: Artificial Intelligence, Academic Achievement, Mathematics, Computer Science Education
Taylor V. Williams – ProQuest LLC, 2022
Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…
Descriptors: Multivariate Analysis, Data Analysis, Student Evaluation, Large Group Instruction
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Okoye, Kingsley; Arrona-Palacios, Arturo; Camacho-Zuñiga, Claudia; Achem, Joaquín Alejandro Guerra; Escamilla, Jose; Hosseini, Samira – Education and Information Technologies, 2022
Recent trends in "educational technology" have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, "teaching analytics" is one of the promising and emerging methods within the Education domain that have proved to be useful, towards…
Descriptors: Learning Analytics, Student Evaluation of Teacher Performance, Information Retrieval, Educational Technology
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Lajoie, Susanne P. – International Journal of Artificial Intelligence in Education, 2021
I first met Jim Greer at the NATO Advanced Study Institute on Syntheses of Instructional Sciences and Computing Science for Effective Instructional Computing Systems in 1990 in Calgary, Canada. It was during this meeting that I came to realize that Jim was one of those rare individuals that could help "translate" computer science…
Descriptors: Models, Student Characteristics, Artificial Intelligence, Computer Uses in Education
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Cohausz, Lea – Journal of Educational Data Mining, 2022
Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…
Descriptors: Guidelines, Academic Achievement, Dropouts, Prediction
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Ong, Nathan; Zhu, Jiaye; Mossé, Daniel – International Educational Data Mining Society, 2022
Student grade prediction is a popular task for learning analytics, given grades are the traditional form of student performance. However, no matter the learning environment, student background, or domain content, there are things in common across most experiences in learning. In most previous machine learning models, previous grades are considered…
Descriptors: Prediction, Grades (Scholastic), Learning Analytics, Student Characteristics
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Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
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Khosravi, Hassan; Shabaninejad, Shiva; Bakharia, Aneesha; Sadiq, Shazia; Indulska, Marta; Gasevic, Dragan – Journal of Learning Analytics, 2021
Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…
Descriptors: Learning Analytics, Visual Aids, Artificial Intelligence, Information Retrieval