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Huang, Yun; Brusilovsky, Peter; Guerra, Julio; Koedinger, Kenneth; Schunn, Christian – Journal of Computer Assisted Learning, 2023
Background: Skill integration is vital in students' mastery development and is especially prominent in developing code tracing skills which are foundational to programming, an increasingly important area in the current STEM education. However, instructional design to support skill integration in learning technologies has been limited. Objectives:…
Descriptors: Intelligent Tutoring Systems, Coding, Programming, Skill Development
Peer reviewedConrad Borchers; Jeroen Ooge; Cindy Peng; Vincent Aleven – Grantee Submission, 2025
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Learner Controlled Instruction, Learning Analytics
Shakya, Anup; Rus, Vasile; Venugopal, Deepak – International Educational Data Mining Society, 2023
Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can…
Descriptors: Equal Education, Mathematics Education, Word Problems (Mathematics), Problem Solving
Conrad Borchers; Alex Houk; Vincent Aleven; Kenneth R. Koedinger – Grantee Submission, 2025
Active learning promises improved educational outcomes yet depends on students' sustained motivation to engage in practice. Goal setting can enhance learner engagement. However, past evidence of the effectiveness of setting goals tends to be limited to non-digital learning settings and does not scale well as it requires active teacher or parent…
Descriptors: Learner Engagement, Educational Benefits, Goal Orientation, Rewards
Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy Shaw; Adam Sales; Neil Heffernan – Grantee Submission, 2022
As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user…
Descriptors: Educational Trends, Electronic Learning, Educational Experience, Educational Experiments
Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy Shaw; Adam Sales; Neil Heffernan – International Educational Data Mining Society, 2022
As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user…
Descriptors: Educational Trends, Electronic Learning, Educational Experience, Educational Experiments
Meng Xia; Robin Schmucker; Conrad Borchers; Vincent Aleven – Grantee Submission, 2025
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused…
Descriptors: Mastery Learning, Skill Development, Intelligent Tutoring Systems, Technology Uses in Education
Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu – Interactive Learning Environments, 2021
Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Student Evaluation, Knowledge Level
Frick, Theodore W.; Myers, Rodney D.; Dagli, Cesur – Educational Technology Research and Development, 2022
In this naturalistic design-research study, we tracked 172,417 learning journeys of students who were interacting with an online resource, the Indiana University Plagiarism Tutorials and Tests (IPTAT) at https://plagiarism.iu.edu. IPTAT was designed using First Principles of Instruction (FPI; Merrill in Educ Technol Res Dev 50:43-59, 2002,…
Descriptors: Time, Educational Principles, Instructional Design, Instructional Effectiveness
Lee, Jungmin; Chow, Sy-Miin; Lei, Puiwa; Wijekumar, Kausalai; Molenaar, Peter C. M. – Educational Technology Research and Development, 2021
The intelligent tutoring system of structure strategy (ITSS) is a web-based digital tutoring system proven to be effective in helping students recognize and use text structures to comprehend and recall texts. However, little is known about the dynamic learning processes within the ITSS. This study aims to investigate the effects of feedback dosage…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Time Factors (Learning), Web Based Instruction
Zhang, Qiao; Maclellan, Christopher J. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…
Descriptors: Electronic Learning, Mastery Learning, Computer Simulation, Intelligent Tutoring Systems
Sales, Adam C.; Pane, John F. – International Educational Data Mining Society, 2020
The design of the Cognitive Tutor Algebra I (CTA1) intelligent tutoring system assumes that students work through sections of material following a pre-specified order, and only move on from one section to the next after mastering the first section's skills. However, the software gives teachers the flexibility to override that structure, by…
Descriptors: Student Placement, Intelligent Tutoring Systems, Algebra, Mathematics Instruction
Skromme, B. J.; Wong, M. L.; Redshaw, C. J.; O'Donnell, M. A. – IEEE Transactions on Education, 2022
Contribution: A new operational definition of series connections is given based on elements belonging to the same two meshes, which is properly dual to the usual definition of parallel elements being connected to the same two nodes. Furthermore, computer-based exercises have been developed and tested to teach students about such connections in…
Descriptors: Engineering Education, Electronic Equipment, Computer Assisted Instruction, Coding
Fancsali, Stephen E.; Li, Hao; Sandbothe, Michael; Ritter, Steven – International Educational Data Mining Society, 2021
Recent work describes methods for systematic, data-driven improvement to instructional content and calls for diverse teams of learning engineers to implement and evaluate such improvements. Focusing on an approach called "design-loop adaptivity," we consider the problem of how developers might use data to target or prioritize particular…
Descriptors: Instructional Development, Instructional Improvement, Data Use, Educational Technology
Meng, Qingquan; Jia, Jiyou; Zhang, Zhiyong – Interactive Technology and Smart Education, 2020
Purpose: The purpose of this study is to verify the effect of smart pedagogy to facilitate the high order thinking skills of students and to provide the design suggestion of curriculum and intelligent tutoring systems in smart education. Design/methodology/approach: A smart pedagogy framework was designed. The quasi-experiment was conducted in a…
Descriptors: Thinking Skills, Instructional Effectiveness, Technology Integration, Intelligent Tutoring Systems

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