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Gerardo Quiroz Vieyra; Luis Fernando Muñoz González – European Journal of Education (EJED), 2023
Learning Management Systems (LMS) or Learning Content Management Systems (LCMS) are the core of e-learning platforms and have evolved according to the development of new information and communication technologies. In this type of software there are many products on the market, some developed by the institutions themselves (in-house), others are…
Descriptors: Online Systems, Electronic Learning, Learning Management Systems, Artificial Intelligence
Okubo, Fumiya; Shiino, Tetsuya; Minematsu, Tsubasa; Taniguchi, Yuta; Shimada, Atsushi – IEEE Transactions on Learning Technologies, 2023
In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are…
Descriptors: Learning Management Systems, Student Evaluation, Automation, Artificial Intelligence
Alez Lagos-Castillo; Andrés Chiappe; María-Soledad Ramirez-Montoya; Diego Fernando Becerra Rodríguez – Contemporary Educational Technology, 2025
It may seem that learning platforms and systems are a tired topic for the academic community; however, with the recent advancements in artificial intelligence, they have become relevant to both current and future educational discourse. This systematic literature review explored platforms and software supporting personalized learning processes in…
Descriptors: Technology Uses in Education, Classroom Environment, Individualized Instruction, Technological Advancement
Linghong Li; Wayne F. Patton – Journal of Educational Technology Systems, 2025
The evolving landscape of higher education demands integrating advanced technologies to foster engaging and inclusive learning environments. This paper examines the practical integration of Stellarium, a virtual planetarium software, and ChatGPT, an AI conversational agent, in an asynchronous online undergraduate astronomy course. Stellarium…
Descriptors: Electronic Learning, Astronomy, Science Education, Artificial Intelligence

Benjamin Motz; Harmony Jankowski; Jennifer Lopatin; Waverly Tseng; Tamara Tate – Grantee Submission, 2024
Platform-enabled research services will control, manage, and measure learner experiences within that platform. In this paper, we consider the need for research services that examine learner experiences "outside" the platform. For example, we describe an effort to conduct an experiment on peer assessment in a college writing course, where…
Descriptors: Educational Technology, Learning Management Systems, Electronic Learning, Peer Evaluation
Kuadey, Noble Arden; Mahama, Francois; Ankora, Carlos; Bensah, Lily; Maale, Gerald Tietaa; Agbesi, Victor Kwaku; Kuadey, Anthony Mawuena; Adjei, Laurene – Interactive Technology and Smart Education, 2023
Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology…
Descriptors: Foreign Countries, College Students, Learning Management Systems, Student Behavior
Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence
Tayebeh Sargazi Moghadam; Ali Darejeh; Mansoureh Delaramifar; Sara Mashayekh – Interactive Learning Environments, 2024
Learners' emotional states might change during the learning process, and unpredictable variations of a person's emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the…
Descriptors: Artificial Intelligence, Decision Making, Electronic Learning, Psychological Patterns
Jingyu Xiao; Goudarz Alibakhshi; Alireza Zamanpour; Mohammad Amin Zarei; Shapour Sherafat; Seyyed-Fouad Behzadpoor – International Review of Research in Open and Distributed Learning, 2024
Artificial intelligence (AI) has contributed to various facets of human lives for decades. Teachers and students must have competency in AI and AI-empowered applications, particularly when using online electronic platforms such as learning management systems (LMS). This study investigates the structural relationship between AI literacy, academic…
Descriptors: Artificial Intelligence, Technological Literacy, Educational Attainment, Electronic Learning
Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
Olga Ovtšarenko – Discover Education, 2024
Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…
Descriptors: Artificial Intelligence, Algorithms, Technology Uses in Education, Opportunities
Patric R. Spence; Renee Kaufmann; Kenneth A. Lachlan; Xialing Lin; Stephen A. Spates – Communication Education, 2024
As technologies such as artificial intelligence (AI) and other forms of machine communication become more popular and readily available, the opportunities for use in an online class increase. This replication and extension sought to understand and test the use of AI versus human communication in an online learning space--specifically the learning…
Descriptors: Online Courses, Artificial Intelligence, Technology Uses in Education, Electronic Learning
Randi Proska Sandra; Wu-Yuin Hwang; Afifah Zafirah; Uun Hariyanti; Engkizar Engkizar; Ahmaddul Hadi; Ahmad Fauzan – Journal of Educational Computing Research, 2024
Argumentative writing is a fundamental aspect of undergraduate students' academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced…
Descriptors: Persuasive Discourse, Writing (Composition), Undergraduate Students, Writing Skills
Hilpert, Jonathan C.; Greene, Jeffrey A.; Bernacki, Matthew – British Journal of Educational Technology, 2023
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform…
Descriptors: Learning Theories, Independent Study, Artificial Intelligence, Biology
Nguyen, Lan Thi; Tuamsuk, Kulthida – Cogent Education, 2022
This paper explored the characteristics of the digital learning ecosystem (DLE) in educational institutions based on the analysis of English scholarly discourse from various sources between 2002 and 2021. The content analysis method was used to examine core conceptual elements from the existing models. Researchers used Google Scholar and other…
Descriptors: Electronic Learning, Ecology, Discourse Analysis, Content Analysis
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