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Carvalho, Floran; Henriet, Julien; Greffier, Francoise; Betbeder, Marie-Laure; Leon-Henri, Dana – Journal of Education and e-Learning Research, 2023
This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this…
Descriptors: Learning Processes, Algorithms, Artificial Intelligence, Programming Languages
Mohamed, Mohamed Zulhilmi bin; Hidayat, Riyan; Suhaizi, Nurain Nabilah binti; Sabri, Norhafiza binti Mat; Mahmud, Muhamad Khairul Hakim bin; Baharuddin, Siti Nurshafikah binti – International Electronic Journal of Mathematics Education, 2022
The advancement of technology like artificial intelligence (AI) provides a chance to help teachers and students solve and improve teaching and learning performances. The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education. A systematic…
Descriptors: Artificial Intelligence, Mathematics Instruction, Meta Analysis, Databases
Gresse Von Wangenheim, Christiane; Da Cruz Alves, Nathalia; Rauber, Marcelo F.; Hauck, Jean C. R.; Yeter, Ibrahim H. – Informatics in Education, 2022
Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for…
Descriptors: Performance Based Assessment, Artificial Intelligence, Learning Processes, Scoring Rubrics
Qin, Fen; Li, Kai; Yan, Jianyuan – British Journal of Educational Technology, 2020
Artificial Intelligence (AI) has penetrated the field of education. Trust has long been regarded as a driver for the acceptance of technology. Netnography and interviews were used to investigate trust in AI-based educational systems from the perspective of users. We identified the factors influencing trust in AI-based educational systems and…
Descriptors: Trust (Psychology), Artificial Intelligence, Classification, Context Effect
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes
Aguilar, Jose; Cordero, Jorge; Buendía, Omar – Journal of Educational Computing Research, 2018
In this article, we propose the concept of "Autonomic Cycle Of Learning Analysis Tasks" (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic…
Descriptors: Data Analysis, Learning Processes, Decision Making, Instructional Improvement
Deliyska, Boryana; Manoilov, Peter – International Journal of Distance Education Technologies, 2010
The intelligent learning systems provide direct customized instruction to the learners without the intervention of human tutors on the basis of Semantic Web resources. Principal roles use ontologies as instruments for modeling learning processes, learners, learning disciplines and resources. This paper examines the variety, relationships, and…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Curriculum Development, Lesson Plans
Baschera, Gian-Marco; Gross, Markus – International Journal of Artificial Intelligence in Education, 2010
We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…
Descriptors: Foreign Countries, Spelling, Intelligent Tutoring Systems, Prediction
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection

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