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Cody, Christa; Maniktala, Mehak; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2022
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation…
Descriptors: Comparative Analysis, Prior Learning, Intelligent Tutoring Systems, Problem Solving
Daniel Weitekamp III; Erik Harpstead; Kenneth R. Koedinger – Grantee Submission, 2020
Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Instructional Design, Simulation
Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics
Katz, Sandra; Albacete, Patricia; Chounta, Irene-Angelica; Jordan, Pamela; McLaren, Bruce M.; Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…
Descriptors: Physics, Science Instruction, Pretests Posttests, Scores
Scaffolded Self-Explanation with Visual Representations Promotes Efficient Learning in Early Algebra
Tomohiro Nagashima; Anna N. Bartel; Stephanie Tseng; Nicholas A. Vest; Elena M. Silla; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2021
Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self- explanation, a novel form of instructional scaffolding in which visual representations are used to…
Descriptors: Visual Aids, Scaffolding (Teaching Technique), Mathematics Instruction, Algebra
Khayi, Nisrine Ait; Rus, Vasile – International Educational Data Mining Society, 2019
In this paper, we applied a number of clustering algorithms on pretest data collected from 264 high-school students. Students took the pre-test at the beginning of a 5-week experiment in which they interacted with an intelligent tutoring system. The primary goal of this work is to identify clusters of students exhibiting similar knowledge…
Descriptors: High School Students, Cluster Grouping, Prior Learning, Intelligent Tutoring Systems
Nagashima, Tomohiro; Bartel, Anna N.; Yadav, Gautam; Tseng, Stephanie; Vest, Nicholas A.; Silla, Elena M.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2021
Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design…
Descriptors: Teaching Methods, Mathematics Instruction, Algebra, Middle School Students
Nagashima, Tomohiro; Bartel, Anna N.; Silla, Elena M.; Vest, Nicholas A.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2020
Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students…
Descriptors: Algebra, Teaching Methods, Visual Aids, Concept Formation
Chen, Xingliang; Mitrovic, Antonija; Mathews, Moffat – International Journal of Artificial Intelligence in Education, 2019
Agency refers to the level of control the student has over learning. Most studies on agency in computer-based learning environments have been conducted in the context of educational games and multimedia learning, while there is little research done in the context of learning with Intelligent Tutoring Systems (ITSs). We conducted a study in the…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Educational Games, Independent Study
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
Lavbic, Dejan; Matek, Tadej; Zrnec, Aljaž – Interactive Learning Environments, 2017
Today's software industry requires individuals who are proficient in as many programming languages as possible. Structured query language (SQL), as an adopted standard, is no exception, as it is the most widely used query language to retrieve and manipulate data. However, the process of learning SQL turns out to be challenging. The need for a…
Descriptors: Evaluation Methods, Information Systems, Intelligent Tutoring Systems, Computer Science Education
Hooshyar, D.; Ahmad, R. B.; Yousefi, M.; Yusop, F. D.; Horng, S.-J. – Journal of Computer Assisted Learning, 2015
Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one-to-one personalized and…
Descriptors: Flow Charts, Intelligent Tutoring Systems, Educational Technology, Teaching Methods
Roll, Ido; Baker, Ryan S. J. d.; Aleven, Vincent; Koedinger, Kenneth R. – Journal of the Learning Sciences, 2014
Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the…
Descriptors: Help Seeking, Comparative Analysis, Behavior Patterns, Intelligent Tutoring Systems
Bringula, Rex P.; Basa, Roselle S.; Dela Cruz, Cecilio; Rodrigo, Ma. Mercedes T. – Journal of Educational Computing Research, 2016
This study attempted to determine the influence of prior knowledge in mathematics of students on learner-interface interactions in a learning-by-teaching intelligent tutoring system. One hundred thirty-nine high school students answered a pretest (i.e., the prior knowledge in mathematics) and a posttest. In between the pretest and posttest, they…
Descriptors: Mathematics, Tutoring, Mathematics Instruction, Foreign Countries
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Peer reviewedWaern, Yvonne; Ramberg, Robert – Computers in Human Behavior, 1996
Discusses users' perceptions of computers versus human beings as advice givers in problem-solving situations based on two studies conducted at Stockholm University (Sweden). Examines people's self-confidence and perception of advice, and concludes that perception relates to existing attitudes, experience, and domain knowledge. (Author/LRW)
Descriptors: Analysis of Variance, Comparative Analysis, Foreign Countries, Higher Education
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