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Nicola M. Hodkowski; Carolyn Carhart-Quezada – Mathematics Teacher: Learning and Teaching PK-12, 2023
Open tasks are mathematical problems "that promote mathematical reasoning and problem solving and allow multiple entry points and varied solution strategies" (National Council of Teachers of Mathematics [NCTM], 2014, p. 17). Open tasks can have more than one right answer, solution, or outcome. Facilitation of open tasks offers learning…
Descriptors: Mathematics Instruction, Mathematical Logic, Problem Solving, Learning Activities
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)
Sales, Adam C.; Pane, John F. – Journal of Research on Educational Effectiveness, 2021
Randomized evaluations of educational technology produce log data as a bi-product: highly granular data on student and teacher usage. These datasets could shed light on causal mechanisms, effect heterogeneity, or optimal use. However, there are methodological challenges: implementation is not randomized and is only defined for the treatment group,…
Descriptors: Educational Technology, Use Studies, Randomized Controlled Trials, Mathematics Curriculum
Mamcenko, Jelena; Kurilovas, Eugenijus; Krikun, Irina – Informatics in Education, 2019
The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for…
Descriptors: Case Method (Teaching Technique), Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
How, Meng-Leong; Hung, Wei Loong David – Education Sciences, 2019
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved…
Descriptors: Stakeholders, Artificial Intelligence, Bayesian Statistics, Probability
Kolonko, Erin M.; Kolonko, Kristopher J. – Journal of Chemical Education, 2019
Strategies for teaching NMR spectral interpretation in the undergraduate organic chemistry curriculum are often faculty-centered and can lead to student reliance on rote memorization and "guess and check" methods rather than critical-thinking skills for structure determination. This article describes a student-focused methodology for the…
Descriptors: Spectroscopy, Inquiry, Active Learning, Organic Chemistry
Lloyd, Courtney A.; Manigo, Jocelyn A.; Jones, Tiffany E.; Crouse-Machcinski, Kaitlyn M. – Learning Assistance Review, 2020
Peer tutors at West Chester University's Learning Assistance and Resource Center have the opportunity to advance in their professional roles by becoming Peer Tutor Coordinators (PTCs). PTCs supervise peer tutors within the learning center and are instrumental to its overall success. PTCs acquire time management, communication, problem-solving,…
Descriptors: Peer Teaching, Coordinators, Supervision, Learning Resources Centers
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
Mitrovic, Antonija; Suraweera, Pramuditha – International Journal of Artificial Intelligence in Education, 2016
Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and…
Descriptors: Database Design, Intelligent Tutoring Systems, Problem Solving, Semantics
VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2016
Although the Andes project produced many results over its 18 years of activity, this commentary focuses on its contributions to understanding how a goal-free user interface impacts the overall design and performance of a step-based tutoring system. Whereas a goal-aligned user interface displays relevant goals as blank boxes or empty locations that…
Descriptors: Computer Interfaces, Intelligent Tutoring Systems, Technology Uses in Education, Performance
Aravind, Vasudeva Rao; McConnell, Marcella Kay – World Journal on Educational Technology: Current Issues, 2018
Educating our future citizens in science and engineering is vitally important to ensure future advancement. Presently, in the light of environmental sustainability, it is critical that students learn concepts relating to energy, its consumption and future demands. In this article, we harness the state of the educational technology, namely…
Descriptors: Intelligent Tutoring Systems, Science Instruction, Energy, Instructional Design
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
an de Sande, Brett – International Educational Data Mining Society, 2016
Learning curves have proven to be a useful tool for understanding how a student learns a given skill as they progress through a curriculum. A learning curve for a given Knowledge Component (KC) is a plot of some measure of competence as a function of the number of opportunities the student has had to apply that KC. Consider the case where each…
Descriptors: Learning Processes, Knowledge Level, Problem Solving, Homework
Walkington, Candace; Bernacki, Matthew L. – Journal of Experimental Education, 2018
Instruction can be made relevant to students when it draws upon and utilizes their interests, experiences, and "funds of knowledge" in productive ways to support classroom learning. This approach has been referred to as "context personalization." In this paper, we discuss the cognitive basis of personalization interventions,…
Descriptors: Individualized Instruction, Instructional Design, Relevance (Education), Cognitive Processes
Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil – International Educational Data Mining Society, 2015
A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…
Descriptors: Models, Student Behavior, Intelligent Tutoring Systems, Data Analysis