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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
Direct linkConrad 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
Download full textEglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
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
Fancsali, Stephen E.; Holstein, Kenneth; Sandbothe, Michael; Ritter, Steven; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2020
Extensive literature in artificial intelligence in education focuses on developing automated methods for detecting cases in which students struggle to master content while working with educational software. Such cases have often been called "wheel-spinning," "unproductive persistence," or "unproductive struggle." We…
Descriptors: Artificial Intelligence, Automation, Persistence, Intelligent Tutoring Systems
Jackson, G. Tanner; McNamara, Danielle S. – Grantee Submission, 2017
By combining the benefits from Intelligent Tutoring Systems (ITSs) and video games, educational games offer the unique potential to capture learners' interest, compel them to persist with targeted tasks, and can result in improved mastery of educational content and skills. These benefits are described within the context of educational game…
Descriptors: Educational Games, Intelligent Tutoring Systems, Educational Benefits, Learning Motivation
