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Direct linkHani Y. Ayyoub; Omar S. Al-Kadi – IEEE Transactions on Learning Technologies, 2024
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that…
Descriptors: Cognitive Style, Individualized Instruction, Learning Management Systems, Artificial Intelligence
Webb, Mary E.; Fluck, Andrew; Magenheim, Johannes; Malyn-Smith, Joyce; Waters, Juliet; Deschênes, Michelle; Zagami, Jason – Educational Technology Research and Development, 2021
Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in…
Descriptors: Artificial Intelligence, Learning, Adjustment (to Environment), Accountability
Cheuk, Tina – Science Education, 2021
Assessment developers are increasingly using the developing technology of machine learning in transforming how to assess students in their science learning. I argue that these algorithmic models further embed the structures of inequality that are pervasive in the development of science assessments in how they legitimize certain language practices…
Descriptors: Artificial Intelligence, Educational Technology, Racial Bias, Science Education
Download full textAmigud, Alexander; Arnedo-Moreno, Joan; Daradoumis, Thanasis; Guerrero-Roldan, Ana-Elena – International Review of Research in Open and Distributed Learning, 2017
This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students' patterns of language use from data,…
Descriptors: Data Collection, Data Analysis, Integrity, Electronic Learning
Geller, Shay A.; Gal, Kobi; Segal, Avi; Sripathi, Kamali; Kim, Hyunsoo G.; Facciotti, Marc T.; Igo, Michele; Hoernle, Nicholas; Karger, David – IEEE Transactions on Learning Technologies, 2021
This article provides computational and rule-based approaches for detecting confusion that is expressed in students' comments in couse forums. To obtain reliable, ground truth data about which posts exhibit student confusion, we designed a decision tree that facilitates the manual labeling of forum posts by experts. However, manual labeling is…
Descriptors: Identification, Misconceptions, Student Attitudes, Computer Mediated Communication
Ravi, M. S. – Australian Mathematics Education Journal, 2021
This article shows how the exciting new field of Machine Learning can be effectively introduced in a standard calculus sequence, once the students have been introduced to functions of several variables. This is an opportunity to engage students in a discussion of an application of calculus to an emerging field that the students encounter in their…
Descriptors: Mathematics Instruction, Calculus, Man Machine Systems, Educational Technology
Polyzou, Agoritsa; Karypis, George – International Educational Data Mining Society, 2018
Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches…
Descriptors: Low Achievement, Predictor Variables, Classification, Student Characteristics
Standen, Penelope J.; Brown, David J.; Taheri, Mohammad; Galvez Trigo, Maria J.; Boulton, Helen; Burton, Andrew; Hallewell, Madeline J.; Lathe, James G.; Shopland, Nicholas; Blanco Gonzalez, Maria A.; Kwiatkowska, Gosia M.; Milli, Elena; Cobello, Stefano; Mazzucato, Annaleda; Traversi, Marco; Hortal, Enrique – British Journal of Educational Technology, 2020
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student's affective state, to move them out of negative states that inhibit learning, into…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Trifa, Amal; Hedhili, Aroua; Chaari, Wided Lejouad – Education and Information Technologies, 2019
E-learning systems have gained nowadays a large student community due to the facility of use and the integration of one-to-one service. Indeed, the personalization of the learning process for every user is needed to increase the student satisfaction and learning efficiency. Nevertheless, the number of students who give up their learning process…
Descriptors: Educational Technology, Technology Uses in Education, Learning Processes, Student Needs
Conrad, Shawn; Clarke-Midura, Jody; Klopfer, Eric – International Journal of Game-Based Learning, 2014
Educational games offer an opportunity to engage and inspire students to take interest in science, technology, engineering, and mathematical (STEM) subjects. Unobtrusive learning assessment techniques coupled with machine learning algorithms can be utilized to record students' in-game actions and formulate a model of the students' knowledge…
Descriptors: Educational Games, Computer Uses in Education, Science Interests, STEM Education
Jia-qi Zheng; Kwok-cheung Cheung; Pou-seong Sit – Education and Information Technologies, 2024
With the rapid growth of education data in large-scale assessment, machine learning techniques are crucial to the interdisciplinary development of education and information. Although data mining tools are increasingly used to predict overall student performance, resilient students in the digital world remain unstudied. Our study aims to…
Descriptors: Educational Technology, Foreign Countries, Resilience (Psychology), Students
Zhai, Xiaoming; He, Peng; Krajcik, Joseph – Journal of Research in Science Teaching, 2022
Involving students in scientific modeling practice is one of the most effective approaches to achieving the next generation science education learning goals. Given the complexity and multirepresentational features of scientific models, scoring student-developed models is time- and cost-intensive, remaining one of the most challenging assessment…
Descriptors: Artificial Intelligence, Science Education, Models, Middle School Students
Jones, Joshua – Mathematics Teacher: Learning and Teaching PK-12, 2021
Despite the importance of artificial intelligence in our daily lives, it has yet to be integrated into K-12 classrooms in a meaningful way. This article explores a lesson in which geometry students use Euclidean distance to implement a functional machine learning algorithm in Google Sheets™. The assignment requires students to apply the distance…
Descriptors: Geometry, Mathematics Instruction, Artificial Intelligence, Geometric Concepts
Jones, Joshua – Mathematics Teacher: Learning and Teaching PK-12, 2021
Aside from being culturally relevant, artificial intelligence is also supporting companies in making business decisions. Consequently, "workforce needs have shifted rapidly," resulting in a demand for applicants who are skilled in "data, analytics, machine learning, and artificial intelligence" (Miller and Hughes 2017). This…
Descriptors: Man Machine Systems, Artificial Intelligence, Educational Technology, Technology Uses in Education
Shruti Priya; Shubhankar Bhadra; Sridhar Chimalakonda; Akhila Sri Manasa Venigalla – Interactive Learning Environments, 2024
Owing to the predominant role of Machine Learning(ML) across domains, it is being introduced at multiple levels of education, including K-12. Researchers have leveraged games, augmented reality and other ways to make learning ML concepts interesting. However, most of the existing games to teach ML concepts either focus on use-cases and…
Descriptors: Artificial Intelligence, Secondary School Students, Video Games, Visual Aids
