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Poitras, Eric G.; Lajoie, Susanne P.; Doleck, Tenzin; Jarrell, Amanda – Educational Technology & Society, 2016
Learner modeling, a challenging and complex endeavor, is an important and oft-studied research theme in computer-supported education. From this perspective, Educational Data Mining (EDM) research has focused on modeling and comprehending various dimensions of learning in computer-based learning environments (CBLE). Researchers and designers are…
Descriptors: Intelligent Tutoring Systems, Data, Data Analysis, Medical Evaluation
Dimitrova, Vania; Brna, Paul – International Journal of Artificial Intelligence in Education, 2016
STyLE-OLM (Dimitrova 2003 "International Journal of Artificial Intelligence in Education," 13, 35-78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can "inspect," "discuss" and "alter" the learner model that has been jointly…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intelligent Tutoring Systems, Interaction
Ohlsson, Stellan – International Journal of Artificial Intelligence in Education, 2016
The ideas behind the constraint-based modeling (CBM) approach to the design of intelligent tutoring systems (ITSs) grew out of attempts in the 1980's to clarify how declarative and procedural knowledge interact during skill acquisition. The learning theory that underpins CBM was based on two conceptual innovations. The first innovation was to…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Learning Theories
Graesser, Arthur C. – Grantee Submission, 2016
AutoTutor helps students learn by holding a conversation in natural language. AutoTutor is adaptive to the learners' actions, verbal contributions, and in some systems their emotions. Many of AutoTutor's conversation patterns simulate human tutoring, but other patterns implement ideal pedagogies that open the door to computer tutors eclipsing…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Communication Strategies, Dialogs (Language)
Allen, Laura K.; Jacovina, Matthew E.; Dascalu, Mihai; Roscoe, Rod D.; Kent, Kevin M.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2016
This study investigates how and whether information about students' writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students' writing sessions. A corpus of prompt-based…
Descriptors: Essays, Writing Processes, Writing (Composition), Writing Instruction
Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil – Grantee Submission, 2016
In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…
Descriptors: Intelligent Tutoring Systems, Data, Randomized Controlled Trials, Electronic Learning
Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel – International Educational Data Mining Society, 2016
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally…
Descriptors: Intelligent Tutoring Systems, Markov Processes, Models, Teaching Methods
Jordan, Pamela; Albacete, Patricia; Katz, Sandra – Grantee Submission, 2016
We explore the effectiveness of a simple algorithm for adaptively deciding whether to further decompose a step in a line of reasoning during tutorial dialogue. We compare two versions of a tutorial dialogue system, Rimac: one that always decomposes a step to its simplest sub-steps and one that adaptively decides to decompose a step based on a…
Descriptors: Algorithms, Decision Making, Intelligent Tutoring Systems, Scaffolding (Teaching Technique)
Sottilare, Robert A. – Technology, Instruction, Cognition and Learning, 2018
This article is intended as a companion document to the more focused report provided by the author at the 2017 American Education Research Association (AERA) Conference as part of the Technology, Instruction, Cognition & Learning Special Interest Group's Symposium on Intelligent Tutoring Systems (ITSs). Both the AERA talk and this article…
Descriptors: Literature Reviews, Goal Orientation, Integrated Learning Systems, Instructional Design
Nielen, Thijs M. J.; Smith, Glenn G.; Sikkema-de Jong, Maria T.; Drobisz, Jack; van Horne, Bill; Bus, Adriana G. – Journal of Educational Computing Research, 2018
In this digital era, a fundamental challenge is to design digital reading materials in such a way that they improve children's reading skills. Since reading books is challenging for many fifth graders--particularly for those genetically susceptible to attention problems--the researchers hypothesized that guidance from a digital Pedagogical Agent…
Descriptors: Grade 5, Reading Motivation, Incidental Learning, Vocabulary
Liu, Ran; Stamper, John; Davenport, Jodi – Grantee Submission, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Ní Chiaráin, Neasa; Ní Chasaide, Ailbhe – Research-publishing.net, 2018
This paper details the motivation for and the main characteristics of "An Scéalaí" ('The Storyteller'), an intelligent Computer Assisted Language Learning (iCALL) platform for autonomous learning that integrates the four skills; writing, listening, speaking, and reading. A key feature is the incorporation of speech technology. Speech…
Descriptors: Computer Assisted Instruction, Language Acquisition, Independent Study, Assistive Technology
Pearson, 2018
Pearson sought to explore whether the use of Mastering Physics, an online tutorial system used in higher education introductory physics courses, is related to students' results in exams and external standardized tests. This Research Report presents findings from two research studies we conducted with Penn State University, a school known for…
Descriptors: Physics, Science Instruction, Introductory Courses, Science Tests
Olsen, Jennifer K.; Belenky, Daniel M.; Aleven, Vincent; Rummel, Nikol; Sewall, Jonathan; Ringenberg, Michael – Grantee Submission, 2014
Authoring tools have been shown to decrease the amount of time and resources needed for the development of Intelligent Tutoring Systems (ITSs). Although collaborative learning has been shown to be beneficial to learning, most of the current authoring tools do not support the development of collaborative ITSs. In this paper, we discuss an extension…
Descriptors: Intelligent Tutoring Systems, Programming, Cooperative Learning, Problem Solving
Pandarova, Irina; Schmidt, Torben; Hartig, Johannes; Boubekki, Ahcène; Jones, Roger Dale; Brefeld, Ulf – International Journal of Artificial Intelligence in Education, 2019
Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner's evolving language…
Descriptors: Intelligent Tutoring Systems, Difficulty Level, Cues, Second Language Learning

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