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MacLellan, Christopher J.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems are effective for improving students' learning outcomes (Pane et al. 2013; Koedinger and Anderson, "International Journal of Artificial Intelligence in Education," 8, 1-14, 1997; Bowen et al. "Journal of Policy Analysis and Management," 1, 94-111 2013). However, constructing tutoring systems that…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Instructional Design
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Carvalho, Paulo F.; McLaughlin, Elizabeth A.; Koedinger, Kenneth R. – Journal of Educational Psychology, 2022
In this article, we leverage data from over 1,000 students participating in two different online courses to investigate whether better learning outcomes are associated with student decisions to practice instead of (re-)reading. Consistent with laboratory and classroom findings, we find that students' decisions to practice are related to better…
Descriptors: Independent Study, Electronic Learning, Online Courses, Outcomes of Education
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Yannier, Nesra; Hudson, Scott E.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2020
Along with substantial consensus around the power of active learning, comes some lack of precision in what its essential ingredients are. New educational technologies offer vehicles for systematically exploring benefits of alternative techniques for supporting active learning. We introduce a new genre of Intelligent Science Station technology that…
Descriptors: Active Learning, Artificial Intelligence, STEM Education, Educational Technology
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Rivers, Kelly; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2017
To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…
Descriptors: Programming, Coding, Computers, Data
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Liu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
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Yannier, Nesra; Crowley, Kevin; Do, Youngwook; Hudson, Scott E.; Koedinger, Kenneth R. – Journal of the Learning Sciences, 2022
Background: Museum exhibits encourage exploration with physical materials typically with minimal signage or guidance. Ideally children get interactive support as they explore, but it is not always feasible to have knowledgeable staff regularly present. Technology-based interactive support can provide guidance to help learners achieve scientific…
Descriptors: Museums, Exhibits, Hands on Science, Artificial Intelligence
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Wiese, Eliane S.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2017
This paper proposes "grounded feedback" as a way to provide implicit verification when students are working with a novel representation. In grounded feedback, students' responses are in the target, to-be-learned representation, and those responses are reflected in a more-accessible linked representation that is intrinsic to the domain.…
Descriptors: Instructional Design, Feedback (Response), Evaluation Criteria, Instructional Effectiveness
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Koedinger, Kenneth R.; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2016
Our 1997 article in "IJAIED" reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students' learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research…
Descriptors: Intelligent Tutoring Systems, Technology Uses in Education, Educational Technology, Algebra
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Rosé, Carolyn P.; McLaughlin, Elizabeth A.; Liu, Ran; Koedinger, Kenneth R. – British Journal of Educational Technology, 2019
Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving…
Descriptors: Discovery Learning, Man Machine Systems, Artificial Intelligence, Models
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Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
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King, Emily C.; Benson, Max; Raysor, Sandra; Holme, Thomas A.; Sewall, Jonathan; Koedinger, Kenneth R.; Aleven, Vincent; Yaron, David J. – Journal of Chemical Education, 2022
This report showcases a new type of online homework system that provides students with a free-form interface and dynamic feedback. The ORCCA Tutor (Open-Response Chemistry Cognitive Assistance Tutor) is a production rules-based online tutoring system utilizing the Cognitive Tutoring Authoring Tools (CTAT) developed by Carnegie Mellon University.…
Descriptors: Intelligent Tutoring Systems, Chemistry, Homework, Feedback (Response)
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Walker, Erin; Rummel, Nikol; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2014
Adaptive collaborative learning support (ACLS) involves collaborative learning environments that adapt their characteristics, and sometimes provide intelligent hints and feedback, to improve individual students' collaborative interactions. ACLS often involves a system that can automatically assess student dialogue, model effective and…
Descriptors: Algebra, Peer Teaching, Tutoring, Cooperative Learning
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Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2016
Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student's request. The Help Tutor was a tutor agent that gave in-context, real-time feedback on students' help-seeking behavior, as they were learning with an ITS.…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Help Seeking, Feedback (Response)
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Pavlik, Philip I., Jr.; Yudelson, Michael; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
Efforts to improve instructional task design often make reference to the mental structures, such as "schemas" (e.g., Gick & Holyoak, 1983) or "identical elements" (Thorndike & Woodworth, 1901), that are common to both the instructional and target tasks. This component based (e.g., Singley & Anderson, 1989) approach…
Descriptors: Models, Measurement, Instructional Design, Transfer of Training
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Booth, Julie L.; Koedinger, Kenneth R. – British Journal of Educational Psychology, 2012
Background: High school and college students demonstrate a verbal, or textual, advantage whereby beginning algebra problems in story format are easier to solve than matched equations (Koedinger & Nathan, 2004). Adding diagrams to the stories may further facilitate solution (Hembree, 1992; Koedinger & Terao, 2002). However, diagrams may not…
Descriptors: Student Problems, Problem Solving, Grade 8, Grade 6
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