NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Does not meet standards1
Showing 1 to 15 of 38 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Scruggs, Richard; Baker, Ryan S.; Pavlik, Philip I., Jr.; McLaren, Bruce M.; Liu, Ziyang – Educational Technology Research and Development, 2023
Despite considerable advances in knowledge tracing algorithms, educational technologies that use this technology typically continue to use older algorithms, such as Bayesian Knowledge Tracing. One key reason for this is that contemporary knowledge tracing algorithms primarily infer next-problem correctness in the learning system, but do not…
Descriptors: Algorithms, Prediction, Knowledge Level, Video Games
Peer reviewed Peer reviewed
Direct linkDirect link
Lixiang Xu; Zhanlong Wang; Suojuan Zhang; Xin Yuan; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT) is an intelligent educational technology used to model students' learning progress and mastery in adaptive learning environments for personalized education. Despite utilizing deep learning models in KT, current approaches often oversimplify students' exercise records into knowledge sequences, which fail to explore the rich…
Descriptors: Knowledge Level, Educational Technology, Intelligent Tutoring Systems, Individualized Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Qinggui Qin; Shuhan Zhang – Education and Information Technologies, 2025
Artificial Intelligence (AI) plays a vital role in the growth and progress of education. Therefore, there is a need to scientifically explore the application of Artificial Intelligence in Education (AIED) and systematically analyze the development trends and research hotspots of AIED to provide reference for researchers. In this study, 1356…
Descriptors: Artificial Intelligence, Knowledge Level, Visual Aids, Concept Mapping
Jia Tracy Shen; Michiharu Yamashita; Ethan Prihar; Neil Heffernan; Xintao Wu; Sean McGrew; Dongwon Lee – Grantee Submission, 2021
Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success.…
Descriptors: Mathematics Education, Knowledge Level, Video Technology, Educational Technology
Peer reviewed Peer reviewed
Direct linkDirect link
Shin, Dongjo – International Journal of Science and Mathematics Education, 2022
Intelligent tutoring systems (ITSs) have drawn researchers' attention as a means of providing personalized learning content, adaptive feedback, and instructional strategies based on students' characteristics and learning needs. Few studies, however, have explored how prospective and practicing teachers integrate ITSs into their lessons. This study…
Descriptors: Intelligent Tutoring Systems, Preservice Teachers, Student Attitudes, Educational Technology
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Grubišic, Ani; Žitko, Branko; Stankov, Slavomir – Journal of Technology and Science Education, 2020
In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is…
Descriptors: Electronic Learning, Educational Technology, Models, Intelligent Tutoring Systems
Peer reviewed Peer reviewed
Direct linkDirect link
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
Shi, Genghu; Wang, Lijia; Zhang, Liang; Shubeck, Keith; Peng, Shun; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2021
Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation…
Descriptors: Intelligent Tutoring Systems, Adult Literacy, Adult Students, Reading Comprehension
Fesler, Lily; Gu, Anna; Chojnacki, Greg – Mathematica, 2023
Air Tutors is an online tutoring organization that provides one-on-one and small group tutoring for K-12 students. Air Tutors uses live, online tutoring and an online platform that incorporates video conferencing and interactive whiteboards; recruits high-quality paid tutors with tutoring experience and engaging online personalities; and engages…
Descriptors: Tutoring, Elementary Secondary Education, Educational Technology, Videoconferencing
Peer reviewed Peer reviewed
Direct linkDirect link
Yuan, Chia-Ching; Li, Cheng-Hsuan; Peng, Chin-Cheng – Interactive Learning Environments, 2023
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization…
Descriptors: Artificial Intelligence, Man Machine Systems, Technology Uses in Education, Educational Technology
Streeter, Matthew – International Educational Data Mining Society, 2015
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…
Descriptors: Probability, Error Patterns, Learning Processes, Models
MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…
Descriptors: Factor Analysis, Regression (Statistics), Knowledge Level, Markov Processes
Peer reviewed Peer reviewed
Direct linkDirect link
Arroyo, Ivon; Burleson, Winslow; Tai, Minghui; Muldner, Kasia; Woolf, Beverly Park – Journal of Educational Psychology, 2013
We provide evidence of persistent gender effects for students using advanced adaptive technology while learning mathematics. This technology improves each gender's learning and affective predispositions toward mathematics, but specific features in the software help either female or male students. Gender differences were seen in the students' style…
Descriptors: Gender Differences, Educational Technology, Technology Uses in Education, Mathematics Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Burrmann, Nicola J.; Moore, John W. – Journal of Chemical Education, 2015
The implementation of a web-based stereochemistry tutorial, which allows students to select their preferred structural representation and method for making stereochemical comparisons between molecules, is discussed. The tutorial was evaluated by students in three different introductory organic chemistry courses at a large midwestern university.…
Descriptors: Science Instruction, Web Based Instruction, Chemistry, Molecular Structure
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Icoz, Kutay; Sanalan, Vehbi A.; Ozdemir, Esra Benli; Kaya, Sukru; Cakar, Mehmet Akif – Educational Sciences: Theory and Practice, 2015
Ontologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by…
Descriptors: Electronic Learning, Technology Uses in Education, Educational Technology, Knowledge Level
Previous Page | Next Page »
Pages: 1  |  2  |  3