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Tafazoli, Dara; María, Elena Gómez; Huertas Abril, Cristina A. – International Journal of Information and Communication Technology Education, 2019
Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to…
Descriptors: Intelligent Tutoring Systems, Computer Assisted Instruction, Teaching Methods, Interdisciplinary Approach
Mustafa, Ghulam; Abbas, Muhammad Azeem; Hafeez, Yaser; Khan, Sharifullah; Hwang, Gwo-Jen – Interactive Learning Environments, 2019
During early childhood, children start developing their cognitive, social, emotional, and behavioural skills, laying the foundation for life-long learning. Cognitive skills are usually taught in traditional classrooms through the use of textbooks and worksheets. The learning content in these textbooks and worksheets is static pre-authored content…
Descriptors: Cognitive Development, Preschool Children, Child Development, Skill Development
Ibili, Emin; Billinghurst, Mark – International Journal of Assessment Tools in Education, 2019
In this study, the relationship between the usability of a mobile Augmented Reality (AR) tutorial system and cognitive load was examined. In this context, the relationship between perceived usefulness, the perceived ease of use, and the perceived natural interaction factors and intrinsic, extraneous, germane cognitive load were investigated. In…
Descriptors: Cognitive Processes, Difficulty Level, Correlation, Usability
Cook, Joshua; Lynch, Collin F.; Hicks, Andrew G.; Mostafavi, Behrooz – International Educational Data Mining Society, 2017
BKT and other classical student models are designed for binary environments where actions are either correct or incorrect. These models face limitations in open-ended and data-driven environments where actions may be correct but non-ideal or where there may even be degrees of error. In this paper we present BKT-SR and RKT-SR: extensions of the…
Descriptors: Models, Bayesian Statistics, Data Use, Intelligent Tutoring Systems
Mizoguchi, Riichiro; Bourdeau, Jacqueline – International Journal of Artificial Intelligence in Education, 2016
This article reflects on the ontology engineering methodology discussed by the paper entitled "Using Ontological Engineering to Overcome AI-ED Problems" published in this journal in 2000. We discuss the achievements obtained in the last 10 years, the impact of our work as well as recent trends and perspectives in ontology engineering for…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Technology Uses in Education, Information Science
VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2016
This commentary suggests a generalization of the conception of the behavior of tutoring systems, which the target article characterized as having an outer loop that was executed once per task and an inner loop that was executed once per step of the task. A more general conception sees these two loops as instances of regulative loops, which…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Performance
Graf von Malotky, Nikolaj Troels; Martens, Alke – International Association for Development of the Information Society, 2016
Intelligent Tutoring System are state of the art in eLearning since the late 1980s. The earliest system have been developed in teams of psychologists and computer scientists, with the goal to investigate learning processes and, later on with the goal to intelligently support teaching and training with computers. Over the years, the eLearning hype…
Descriptors: Intelligent Tutoring Systems, Electronic Learning, Client Server Architecture, Computer Software
Cai, Zhiqiang; Gong, Yan; Qiu, Qizhi; Hu, Xiangen; Graesser, Art – Grantee Submission, 2016
AutoTutor uses conversational intelligent agents in learning environments. One of the major challenges in developing AutoTutor applications is to assess students' natural language answers to AutoTutor questions. We investigated an AutoTutor dataset with 3358 student answers to 49 AutoTutor questions. In comparisons with human ratings, we found…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Dialogs (Language), Programming
Guerrero-Roldán, Ana-Elena; Rodríguez-González, M. Elena; Bañeres, David; Elasri-Ejjaberi, Amal; Cortadas, Pau – International Journal of Educational Technology in Higher Education, 2021
Several tools and resources have been developed in the past years to enhance the teaching and learning process. Most of them are focused on the process itself, but few focus on the assessment process to detect at-risk learners for later acting through feedback to support them to succeed and pass the course. This research paper presents a case…
Descriptors: Intelligent Tutoring Systems, Electronic Learning, Technology Uses in Education, Virtual Universities
Prihar, Ethan; Heffernan, Neil – International Educational Data Mining Society, 2021
Similar content has tremendous utility in classroom and online learning environments. For example, similar content can be used to combat cheating, track students' learning over time, and model students' latent knowledge. These different use cases for similar content all rely on different notions of similarity, which make it difficult to determine…
Descriptors: Computer Software, Middle School Teachers, Mathematics Teachers, College Students
Kim, Byungsoo; Yu, Hangyeol; Shin, Dongmin; Choi, Youngduck – International Educational Data Mining Society, 2021
The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which…
Descriptors: Academic Achievement, Intelligent Tutoring Systems, Prediction, Scores
Skinner, Anna; Diller, David; Kumar, Rohit; Cannon-Bowers, Jan; Smith, Roger; Tanaka, Alyssa; Julian, Danielle; Perez, Ray – International Journal of STEM Education, 2018
Background: Contemporary work in the design and development of intelligent training systems employs task analysis (TA) methods for gathering knowledge that is subsequently encoded into task models. These task models form the basis of intelligent interpretation of student performance within education and training systems. Also referred to as expert…
Descriptors: Task Analysis, Feedback (Response), Intelligent Tutoring Systems, Comparative Analysis
Fletcher, J. D. – Technology, Instruction, Cognition and Learning, 2018
Computer technology has been used for over 50 years to tailor learning experiences to the needs and interests of individual learners at all levels of instruction. It provides adaptation and individualization that is difficult, if not impossible to apply in a classroom of 20-30 students. This article provides a brief background and discussion about…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Public Agencies, Information Technology
Whitehill, Jacob; Movellan, Javier – IEEE Transactions on Learning Technologies, 2018
We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [1], e.g., teaching students the meanings of words by showing images that exemplify their meanings à la Rosetta Stone [2] and Duo Lingo [3]. The approach is grounded in control theory and capitalizes on recent work by [4],…
Descriptors: Intelligent Tutoring Systems, Second Language Learning, Educational Policy, Comparative Analysis
Yanjin Long; Kenneth Holstein; Vincent Aleven – Grantee Submission, 2018
Accurately modeling individual students' knowledge growth is important in many applications of learning analytics. A key step is to decompose the knowledge targeted in the instruction into detailed knowledge components (KCs). We search for an accurate KC model for basic equation solving skills, using data from an intelligent tutoring system (ITS),…
Descriptors: Learning Processes, Mathematics Skills, Equations (Mathematics), Problem Solving

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