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Warner-Richter, Mallory; Hirilall, Ashley; Li, Winnie; Tout, Kathryn – Child Trends, 2018
This report summarizes findings from an implementation and outcomes study of the Tutoring Partnership. The Tutoring Partnership was a Saint Paul and Minneapolis network of 28 community-based programs that provided academic interventions through tutoring. The common goal was improving students' academic outcomes. The Tutoring Partnership, operated…
Descriptors: Community Programs, Tutoring, School Community Programs, Program Effectiveness
Gobert, Janice D.; Moussavi, Raha; Li, Haiying; Sao Pedro, Michael; Dickler, Rachel – Grantee Submission, 2018
This chapter addresses students' data interpretation, a key NGSS inquiry practice, with which students have several different types of difficulties. In this work, we unpack the difficulties associated with data interpretation from those associated with warranting claims. We do this within the context of Inq-ITS (Inquiry Intelligent Tutoring…
Descriptors: Scaffolding (Teaching Technique), Data Interpretation, Intelligent Tutoring Systems, Science Instruction
Roscoe, Rod D.; Allen, Laura K.; Johnson, Adam C.; McNamara, Danielle S. – Grantee Submission, 2018
This study evaluates high school students' perceptions of automated writing feedback, and the influence of these perceptions on revising, as a function of varying modes of computer-based writing instruction. Findings indicate that students' perceptions of automated feedback accuracy, ease of use, relevance, and understandability were favorable.…
Descriptors: High School Students, Student Attitudes, Writing Evaluation, Feedback (Response)
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Kessler, Aaron M.; Stein, Mary Kay; Schunn, Christian D. – Technology, Knowledge and Learning, 2015
Model tracing tutors represent a technology designed to mimic key elements of one-on-one human tutoring. We examine the situations in which such supportive computer technologies may devolve into mindless student work with little conceptual understanding or student development. To analyze the support of student intellectual work in the model…
Descriptors: Learner Engagement, Cognitive Processes, Difficulty Level, Intelligent Tutoring Systems
Allen, Laura K. – International Educational Data Mining Society, 2015
The purpose of intelligent tutoring systems is to provide students with personalized instruction and feedback. The focus of these systems typically rests in the adaptability of the feedback provided to students, which relies on automated assessments of performance in the system. A large focus of my previous work has been to determine how natural…
Descriptors: Intelligent Tutoring Systems, Individual Differences, Natural Language Processing, Student Evaluation
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Olney, Andrew M.; Cade, Whitney L. – Grantee Submission, 2015
This paper proposes a methodology for authoring of intelligent tutoring systems using human computation. The methodology embeds authoring tasks in existing educational tasks to avoid the need for monetary authoring incentives. Because not all educational tasks are equally motivating, there is a tension between designing the human computation task…
Descriptors: Programming, Intelligent Tutoring Systems, Computation, Design
Christopher Thomas Alvin – ProQuest LLC, 2015
Many problems related to synthesis with intelligent tutoring may be phrased as program synthesis problems using AI-style search and formal reasoning techniques. The _x000C_first two results in this dissertation focus on problem synthesis as an aspect of intelligent tutoring systems applied to STEM-based education frameworks, specifically high…
Descriptors: Synthesis, Intelligent Tutoring Systems, Artificial Intelligence, Problem Solving
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Easterday, Matthew W.; Rees Lewis, Daniel; Gerber, Elizabeth M. – International Journal of Artificial Intelligence in Education, 2017
Intelligent tutors based on expert systems often struggle to provide formative feedback on complex, ill-defined problems where answers are unknown. Hybrid crowdsourcing systems that combine the intelligence of multiple novices in face-to-face settings might provide an alternate approach for providing intelligent formative feedback. The purpose of…
Descriptors: Intelligent Tutoring Systems, Formative Evaluation, Feedback (Response), Novices
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Davey, Kenneth – European Journal of Engineering Education, 2017
A detailed study of learning and teaching (L&T) of chemical engineering distillation to a mixed-ability small class of 13 students who are ordinarily full-time in-house employees in industry is reported. The course consisted of 9 × 2-h lectures (18 hours) and 9 × 2-h tutorials (18 hours). It was delivered over nine business days "in…
Descriptors: Chemical Engineering, Ability Grouping, Small Group Instruction, Foreign Countries
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Appelrouth, Jed I.; Zabrucky, Karen M.; Moore, DeWayne – Assessment in Education: Principles, Policy & Practice, 2017
Attaining successful outcomes on the SAT can have profound educational and financial consequences for college-bound students. Using archival data from a private tutoring centre, we investigated variables we hypothesised to contribute to SAT score increases. Our analyses revealed significant effects of time on task and rate of SAT homework…
Descriptors: College Entrance Examinations, Test Coaching, Scores, Time on Task
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Paneque, Juan J.; Cobo, Pedro; Fortuny, Josep M. – Technology, Knowledge and Learning, 2017
This ethnographical study aims to interpret how an intelligent tutorial system, geogebraTUTOR, mediates to the student's argumentative processes. Data consisted of four geometrical problems proposed to a group of four students aged 16-17. Qualitative analysis of two selected cases led to the identification of the development of argumentative…
Descriptors: Ethnography, Intelligent Tutoring Systems, Geometry, Mathematics Instruction
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Nam, SungJin; Frishkoff, Gwen; Collins-Thompson, Kevyn – International Educational Data Mining Society, 2017
We show how the novel use of a semantic representation based on Osgood's semantic differential scales can lead to effective features in predicting short- and long-term learning in students using a vocabulary learning system. Previous studies in students' intermediate knowledge states during vocabulary acquisition did not provide much information…
Descriptors: Predictor Variables, Vocabulary Development, Semantics, Intelligent Tutoring Systems
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
Franklin, Josette R. – ProQuest LLC, 2017
This quantitative research study analyzed archival data to determine if there was a significant difference in promotion rates from third to fourth grade between students in foster care who received one-to-one tutoring and those students in foster care who did not receive one-to-one tutoring over two school years. This study also analyzed student…
Descriptors: Grade 3, Foster Care, Tutoring, Statistical Analysis
Steven Dang; Michael Yudelson; Kenneth R. Koedinger – Grantee Submission, 2017
The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is…
Descriptors: Student Behavior, Intelligent Tutoring Systems, Educational Technology, Academic Achievement
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