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Lynch, Collin; Ashley, Kevin D.; Pinkwart, Niels; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2009
In this paper we consider prior definitions of the terms "ill-defined domain" and "ill-defined problem". We then present alternate definitions that better support research at the intersection of Artificial Intelligence and Education. In our view both problems and domains are ill-defined when essential concepts, relations, or criteria are un- or…
Descriptors: Definitions, Artificial Intelligence, Problem Solving, Educational Research
Shute, Valerie J.; Zapata-Rivera, Diego – ETS Research Report Series, 2008
Recent advances in educational assessment, cognitive science, and artificial intelligence have made it possible to integrate valid assessment and instruction in the form of modern computer-based intelligent systems. These intelligent systems leverage assessment information that is gathered from various sources (e.g., summative and formative). This…
Descriptors: Educational Assessment, Intelligent Tutoring Systems, Artificial Intelligence, Summative Evaluation
Chien, Tsai Chen; Md. Yunus, Aida Suraya; Ali, Wan Zah Wan; Bakar, Ab. Rahim – Online Submission, 2008
In this experimental study, use of Computer Assisted Instruction (CAI) followed by use of an Intelligent Tutoring System (CAI+ITS) was compared to the use of CAI (CAI only) in tutoring students on the topic of Algebraic Expression. Two groups of students participated in the study. One group of 32 students studied algebraic expression in a CAI…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Mathematics Instruction, Algebra
Hausmann, Robert G. M.; VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2010
Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional "content" that does not exist in the instructional materials. Second, when compared to…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables
Chen, Chih-Ming – British Journal of Educational Technology, 2009
Developing personalised web-based learning systems has been an important research issue in e-learning because no fixed learning pathway will be appropriate for all learners. However, most current web-based learning platforms with personalised curriculum sequencing tend to emphasise the learner preferences and interests in relation to personalised…
Descriptors: Electronic Learning, Concept Mapping, Difficulty Level, Cognitive Processes
Papanikolaou, Kyparisia; Grigoriadou, Maria – Journal of Educational Multimedia and Hypermedia, 2009
In this article we investigate the design of educational hypermedia based on constructivist learning theories. According to the principles of project and case-based learning we present the design rational of an Adaptive Educational Hypermedia system prototype named MyProject; learners working with MyProject undertake a project and the system…
Descriptors: Constructivism (Learning), Hypermedia, Learning Processes, Learner Controlled Instruction
Cotos, Elena – CALICO Journal, 2011
This paper presents an empirical evaluation of automated writing evaluation (AWE) feedback used for L2 academic writing teaching and learning. It introduces the Intelligent Academic Discourse Evaluator (IADE), a new web-based AWE program that analyzes the introduction section to research articles and generates immediate, individualized, and…
Descriptors: Evidence, Feedback (Response), Academic Discourse, Writing (Composition)
Pontes, Elvis, Ed.; Silva, Anderson, Ed.; Guelfi, Adilson, Ed.; Kofuji, Sergio Takeo, Ed. – InTech, 2012
With the resources provided by communication technologies, E-learning has been employed in multiple universities, as well as in wide range of training centers and schools. This book presents a structured collection of chapters, dealing with the subject and stressing the importance of E-learning. It shows the evolution of E-learning, with…
Descriptors: Foreign Countries, Educational Technology, Virtual Classrooms, Program Effectiveness
Baschera, Gian-Marco; Gross, Markus – International Journal of Artificial Intelligence in Education, 2010
We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…
Descriptors: Foreign Countries, Spelling, Intelligent Tutoring Systems, Prediction
Feng, Mingyu; Beck, Joseph E.; Heffernan, Neil T. – International Working Group on Educational Data Mining, 2009
A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Sampling, Statistical Inference
Jeremic, Zoran; Jovanovic, Jelena; Gasevic, Dragan – Educational Technology & Society, 2009
The evaluation of intelligent tutoring systems (ITSs) is an important though often neglected stage of ITS development. There are many evaluation methods available but literature does not provide clear guidelines for the selection of evaluation method(s) to be used in a particular context. This paper describes the evaluation study of DEPTHS, an…
Descriptors: Evaluation Methods, Instructional Effectiveness, Guidelines, Student Attitudes
Li, Liang-Yi; Chen, Gwo-Dong – Educational Technology & Society, 2009
Students can practice skills and acquire knowledge by doing coursework. However, in conventional coursework activities, each student is assigned the same exercises, without considering learners' diversity. Moreover, students typically have difficulty in receiving assistance for completing their exercises after class. Therefore, some students…
Descriptors: Foreign Countries, Undergraduate Study, Computer Science Education, Intelligent Tutoring Systems
Reategui, E.; Boff, E.; Campbell, J. A. – Computers & Education, 2008
Traditional hypermedia applications present the same content and provide identical navigational support to all users. Adaptive Hypermedia Systems (AHS) make it possible to construct personalized presentations to each user, according to preferences and needs identified. We present in this paper an alternative approach to educational AHS where a…
Descriptors: Knowledge Representation, Hypermedia, Interaction, Profiles
Dogan, Buket; Camurcu, A. Yilmaz – Journal of Educational Technology Systems, 2008
Educational data mining is a very novel research area, offering fertile ground for many interesting data mining applications. Educational data mining can extract useful information from educational activities for better understanding and assessment of the student learning process. In this way, it is possible to explore how students learn topics in…
Descriptors: Intelligent Tutoring Systems, Program Effectiveness, Teaching Methods, Computer Uses in Education
Heift, Trude – Computer Assisted Language Learning, 2008
This article describes challenges and benefits of modeling learner variability in Computer-Assisted Language Learning. We discuss the learner model of "E-Tutor," a learner model that addresses learner variability by focusing on certain aspects and/or features of the learner's interlanguage. Moreover, we introduce the concept of phrase descriptors,…
Descriptors: Computer Assisted Instruction, Systems Approach, Educational Technology, Intelligent Tutoring Systems

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