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How Learner Control and Explainable Learning Analytics on Skill Mastery Shape Student Desires to Finish and Avoid Loss in Tutored Practice
Peer reviewed Peer reviewed
Conrad 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
Overcoming the Research-to-Practice Gap: A Randomized Trial with Two Brief Homework and Organization Interventions for Students with ADHD as Implemented by School Mental Health Providers
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Joshua M. Langberg; Melissa R. Dvorsky; Stephen J. Molitor; Elizaveta Bourchtein; Laura D. Eddy; Zoe R. Smith; Lauren E. Oddo; Hana-May Eadeh – Grantee Submission, 2017
Objective: To evaluate the effectiveness of 2 brief school-based interventions targeting the homework problems of adolescents with attention-deficit/hyperactivity disorder (ADHD)--the Homework, Organization, and Planning Skills (HOPS) intervention and the Completing Homework by Improving Efficiency and Focus (CHIEF) intervention, as implemented by…
Descriptors: Randomized Controlled Trials, Theory Practice Relationship, Intervention, Homework
Detecting Learning Moment-by-Moment
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Baker, Ryan S. J. D.; Goldstein, Adam B.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Probability, Knowledge Level
Feasibility and Preliminary Efficacy of an After-School Program for Middle Schoolers with ADHD: A Randomized Trial in a Large Public Middle School
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Molina, Brooke S. G.; Flory, Kate; Bukstein, Oscar G.; Greiner, Andrew R.; Baker, Jennifer L.; Krug, Vicky; Evans, Steven W. – Journal of Attention Disorders, 2008
Objective: This pilot study tests the feasibility and preliminary efficacy of an after-school treatment program for middle schoolers with ADHD using a randomized clinical trial design. Method: A total of 23 students with ADHD (25% female, 48% African American) from a large public middle school were randomly assigned to a 10-week program or to…
Descriptors: Middle School Students, Homework, School Activities, Academic Achievement
Proceedings of the International Conference on Educational Data Mining (EDM) (9th, Raleigh, North Carolina, June 29-July 2, 2016)
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Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019)
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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