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Abdessamad Chanaa; Nour-eddine El Faddouli – Smart Learning Environments, 2024
The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate…
Descriptors: Artificial Intelligence, Prerequisites, Metadata, Electronic Learning
Prihar, Ethan; Vanacore, Kirk; Sales, Adam; Heffernan, Neil – International Educational Data Mining Society, 2023
There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…
Descriptors: Electronic Learning, Intervention, Instructional Effectiveness, Data Collection
Monsalve-Pulido, Julian; Aguilar, Jose; Montoya, Edwin – Education and Information Technologies, 2023
The adaptation of traditional systems to service-oriented architectures is very frequent, due to the increase in technologies for this type of architecture. This has led to the construction of frameworks or methodologies for adapting computational projects to service-oriented architecture (SOA) technology. In this work, a framework for adaptation…
Descriptors: Artificial Intelligence, Information Technology, Design, Governance
Senthil Kumaran, V.; Malar, B. – Interactive Learning Environments, 2023
Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…
Descriptors: Electronic Learning, Dropouts, Accuracy, Classification
Danielle Kearns-Sixsmith – Mentoring & Tutoring: Partnership in Learning, 2024
Tutoring promotes student achievement, academic independence, and the reduction of anxiety. While ample studies support tutoring for enhancing student success, few address how to evaluate tutoring. This quandary led to research in building and testing a meta-model that identified the hallmarks of one-on-one high-quality online tutoring.…
Descriptors: Electronic Learning, Tutoring, Higher Education, Educational Quality
Adil Boughida; Mohamed Nadjib Kouahla; Yacine Lafifi – Education and Information Technologies, 2024
In e-learning environments, most adaptive systems do not consider the learner's emotional state when recommending activities for learning difficulties, blockages, or demotivation. In this paper, we propose a new approach of emotion-based adaptation in e-learning environments. The system will allow recommendation resources/activities to motivate…
Descriptors: Psychological Patterns, Electronic Learning, Educational Environment, Models
Ishfaq Majid; Y. Vijaya Lakshmi – Online Submission, 2024
The models of E-learning Readiness (ELR) are basically designed to understand the process of obtaining the basic information necessary for measuring ELR among participants. They help organizations to identify the requirements for designing, developing and implementing E-learning. These models not only help the organizations to identify the degree…
Descriptors: Electronic Learning, Models, Readiness, Content Analysis
Yujie Zhou; Ge Cao; Xiao-Liang Shen – Education and Information Technologies, 2024
Online learning communities play a crucial role in delivering high-quality courses to a large number of learners. However, to maintain an economically sustainable and constantly evolving online learning ecosystem, it is essential to create a virtuous cycle from knowledge production to knowledge consumption by charging learners to incentivize…
Descriptors: Electronic Learning, Economics, Sustainability, Models
Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
Chamba-Eras, Luis; Arruarte, Ana; Elorriaga, Jon A. – IEEE Transactions on Learning Technologies, 2023
In the context of virtual learning communities (VLCs), where the participants may not know each other, it is necessary to have a mechanism to help when deciding who to work with and what reliable contents and information sources are. This study aims to design a generic trust model, named T-VLC, applicable to VLCs, which can be adapted to different…
Descriptors: Communities of Practice, Electronic Learning, Trust (Psychology), Models
Xiaoyu Wang; Nurhasmiza Sazalli; Wan Nur Asyura Wan Adnan – International Journal of Adult Education and Technology, 2023
In this study, 210 articles were reviewed from two databases--Web of Science and Scopus--using the systematic literature review method, following the PRISMA (preferred reporting items for systematic reviews and meta-analyses) selection and analysis process. Through systematic literature review and statistical analysis of empirical research…
Descriptors: Blended Learning, Learning Strategies, Models, Student Attitudes
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
Zirou Lin; Hanbing Yan; Li Zhao – Journal of Computer Assisted Learning, 2024
Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop…
Descriptors: Peer Evaluation, Automation, Grading, Models
Kongpha, Rattanakul; Chatwattana, Pinanta – Higher Education Studies, 2023
The virtual interactive learning model using imagineering process as a tool to promote happy learning for digital age learners. The concept is based on the combination of virtual learning environment and metaverse in order to create learning experience via virtual community. The objectives of this research are (1) to study and synthesise the…
Descriptors: Electronic Learning, Interaction, Computer Simulation, Instructional Development
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