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Ya Xiao; Khe Foon Hew – Interactive Learning Environments, 2024
In recent years, many studies have highlighted the need to go beyond the "one-size-fits-all" gamification approach to tailored or personalised gamification to optimise students' engagement based on their user attributes. However, little is known about its effectiveness on student engagement. To advance the understanding of personalized…
Descriptors: Individualized Instruction, Gamification, Student Participation, Learner Engagement
Huang, Tao; Hu, Shengze; Yang, Huali; Geng, Jing; Liu, Sannyuya; Zhang, Hao; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…
Descriptors: Educational Technology, Prediction, Electronic Learning, Intelligent Tutoring Systems
Cavanagh, Thomas; Chen, Baiyun; Lahcen, Rachid Ait Maalem; Paradiso, James R. – International Review of Research in Open and Distributed Learning, 2020
While adaptive learning is emerging as a promising technology to promote access and quality at a large scale in higher education (Becker et al., 2018), the implementation of adaptive learning in teaching and learning is still sporadic, and it is unclear how to best design and teach an adaptive learning course in a higher education context. As…
Descriptors: Instructional Design, College Instruction, Individualized Instruction, Educational Technology
Yujie Han; Sumin Hong; Zhenyan Li; Cheolil Lim – TechTrends: Linking Research and Practice to Improve Learning, 2025
This scoping review investigates the roles of intelligent learning companion systems (LCS) within educational settings, as well as the presences artificial intelligence (AI) embodies within these roles, and their application in education. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for…
Descriptors: Artificial Intelligence, Definitions, Classification, Technology Uses in Education
O. S. Adewale; O. C. Agbonifo; E. O. Ibam; A. I. Makinde; O. K. Boyinbode; B. A. Ojokoh; O. Olabode; M. S. Omirin; S. O. Olatunji – Interactive Learning Environments, 2024
With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to…
Descriptors: Cognitive Style, Individualized Instruction, Learning Processes, Preferences
Khaldi, Amina; Bouzidi, Rokia; Nader, Fahima – Smart Learning Environments, 2023
In recent years, university teaching methods have evolved and almost all higher education institutions use e-learning platforms to deliver courses and learning activities. However, these digital learning environments present significant dropout and low completion rates. This is primarily due to the lack of student motivation and engagement.…
Descriptors: Gamification, Electronic Learning, Higher Education, Recognition (Achievement)
Zachary Conrad – ProQuest LLC, 2021
In the past twenty-five years, assessment developers and educators have been seeking ways to make assessments more informative and to advance understandings of students' cognitive processes (Ercikan, 2006; Leighton & Gierl, 2007). Demands for personalized formative assessment feedback have stimulated interest in the application of diagnostic…
Descriptors: Evaluation, Test Validity, Feedback (Response), Individualized Instruction
Hemmler, Yvonne M.; Rasch, Julian; Ifenthaler, Dirk – TechTrends: Linking Research and Practice to Improve Learning, 2023
Educational recommender systems offer benefits for workplace learning by tailoring the selection of learning activities to the individual's learning goals. However, existing systems focus on the learner as the primary stakeholder of learning processes and do not consider the organization's perspective. We conducted a systematic review to develop a…
Descriptors: Workplace Learning, Educational Objectives, Educational Technology, Artificial Intelligence
Brendan Bartanen; Andrew Kwok; Andrew Avitabile; Brian Heseung Kim – Educational Researcher, 2025
Heightened concerns about the health of the teaching profession highlight the importance of studying the early teacher pipeline. This exploratory, descriptive article examines preservice teachers' expressed motivation for pursuing a teaching career. Using data from a large teacher education program in Texas, we use a natural language processing…
Descriptors: Career Choice, Teaching (Occupation), Preservice Teachers, Student Attitudes
Alawneh, Hiyam I. – ProQuest LLC, 2019
This research study examined the process by which non-English speaking students, whose first language is Arabic, learn mathematics in English. Teachers' adaptation was investigated to reflect the effect of change in instructional methods. The effect of communication between teachers, and exchanging ideas about the efficient practice procedure to…
Descriptors: Semitic Languages, Native Language, Mathematics Instruction, English (Second Language)
Colognesi, Stéphane; Gouin, Josée-Anne – Research Papers in Education, 2022
While some research has highlighted how teachers prepare their course materials, little is known about how future teachers design support for their students, and thus plan and anticipate what can and will happen in the classroom. We have therefore sought to investigate whether identifiable learner profiles emerge when regular primary school…
Descriptors: Elementary School Students, Individualized Instruction, Classification, Profiles
Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Education and Information Technologies, 2020
With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and…
Descriptors: Electronic Learning, Internet, Information Storage, Models
Pelanek, Radek – IEEE Transactions on Learning Technologies, 2020
Learning systems can utilize many practice exercises, ranging from simple multiple-choice questions to complex problem-solving activities. In this article, we propose a classification framework for such exercises. The framework classifies exercises in three main aspects: (1) the primary type of interaction; (2) the presentation mode; and (3) the…
Descriptors: Integrated Learning Systems, Classification, Multiple Choice Tests, Problem Solving
Sahba Akhavan Niaki – ProQuest LLC, 2018
The increasing amount of available subjective text data in internet such as product reviews, movie critiques and social media comments provides golden opportunities for information retrieval researchers to extract useful information out of such datasets. Topic modeling and sentiment analysis are two widely researched fields that separately try to…
Descriptors: Models, Classification, Content Analysis, Documentation
Fonseca, Samuel C.; Pereira, Filipe Dwan; Oliveira, Elaine H. T.; Oliveira, David B. F.; Carvalho, Leandro S. G.; Cristea, Alexandra I. – International Educational Data Mining Society, 2020
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for…
Descriptors: Programming, Introductory Courses, Computer Science Education, Automation