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Xueyu Sun; Ting Wang – International Journal of Information and Communication Technology Education, 2024
This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an "interest" parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's…
Descriptors: Models, Design, Algorithms, Individualized Instruction
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
Bellarhmouch, Youssra; Jeghal, Adil; Tairi, Hamid; Benjelloun, Nadia – Education and Information Technologies, 2023
Nowadays, the need for e-learning is amplified, especially after the COVID-19 pandemic. E-learning platforms present a solution for the continuity of the learning process. Learners are using different platforms and tools for learning. For this, it is necessary to model the learner for the personalization of the learning environment according to…
Descriptors: Electronic Learning, Educational Environment, Models, Individualized Instruction
Bingxue Zhang; Yang Shi; Yuxing Li; Chengliang Chai; Longfeng Hou – Interactive Learning Environments, 2023
The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system…
Descriptors: Electronic Learning, Models, Students, Individualized Instruction
Mao, Shun; Zhan, Jieyu; Wang, Yizhao; Jiang, Yuncheng – IEEE Transactions on Learning Technologies, 2023
For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Prediction, Prior Learning
Lincke, Alisa; Jansen, Marc; Milrad, Marcelo; Berge, Elias – Research and Practice in Technology Enhanced Learning, 2021
Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners' interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the…
Descriptors: Artificial Intelligence, Prediction, Electronic Learning, Repetition
Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Smart Learning Environments, 2022
Personalized learning systems use several components in order to create courses adapted to the learners'characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the…
Descriptors: Individualized Instruction, Technology Uses in Education, Electronic Learning, Mathematics
Baginda Anggun Nan Cenka; Harry B. Santoso; Kasiyah Junus – Interactive Learning Environments, 2023
Presently, learning is more flexible, personal and has richer learning resources. In the digital era, students use digital tools in almost all aspects of learning, such as seeking information, note-taking, discussion and communication, which is in line with personal learning environments. Therefore, this study proposes a conceptual model of the…
Descriptors: Educational Environment, Lifelong Learning, Educational Resources, Electronic Learning
Xiang Wu; Huanhuan Wang; Yongting Zhang; Baowen Zou; Huaqing Hong – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate…
Descriptors: Artificial Intelligence, Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
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
Robin Shaw Brandenburg – ProQuest LLC, 2023
Improving instructor and student satisfaction with training in a global semiconductor company was the focus of this dissertation in practice. This study followed the Performance Improvement/Human Performance Technology (HPT) model as the overarching framework (Van Tiem et al., 2012). Fifty students and four instructors participated in this…
Descriptors: Student Satisfaction, Training, Global Approach, Models
Tracy Bobko; Mikiko Corsette; Minjuan Wang; Erin Springer – IEEE Transactions on Learning Technologies, 2024
This article discusses the transformative impact of technology on knowledge acquisition and sharing, focusing on the emergence of the metaverse as a virtual community with vast potential for virtual learning. Learning in the metaverse is found to enhance engagement, motivation, and retention, while fostering 21st-century skills. It also offers…
Descriptors: Educational Innovation, Computer Simulation, Technology Uses in Education, Models
Troussas, Christos; Chrysafiadi, Konstantina; Virvou, Maria – Education and Information Technologies, 2021
Personalized computer-based tutoring demands learning systems and applications that identify and keep personal characteristics and features for each individual learner. This is achieved by the technology of student modeling. One prevalent technique of student modeling is stereotypes. Furthermore, individuals differ in how they learn. So, the way…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style, Stereotypes
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
Liu, Xinyang; Ardakani, Saeid Pourroostaei – Education and Information Technologies, 2022
The purpose of this study is to propose an e-learning system model for learning content personalisation based on students' emotions. The proposed system collects learners' brainwaves using a portable Electroencephalogram and processes them via a supervised machine learning algorithm, named K-nearest neighbours (KNN), to recognise real-time…
Descriptors: Foreign Countries, Undergraduate Students, Electronic Learning, Artificial Intelligence