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Showing 1 to 15 of 23 results Save | Export
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Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
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Cukurova, Mutlu; Khan-Galaria, Madiha; Millán, Eva; Luckin, Rose – Journal of Learning Analytics, 2022
One-to-one online tutoring provided by human tutors can improve students' learning outcomes. However, monitoring the quality of such tutoring is a significant challenge. In this paper, we propose a learning analytics approach to monitoring online one-to-one tutoring quality. The approach analyzes teacher behaviours and classifies tutoring sessions…
Descriptors: Learning Analytics, Tutoring, Educational Quality, Behavior Patterns
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Chen, Guanliang; Ferreira, Rafael; Lang, David; Gasevic, Dragan – International Educational Data Mining Society, 2019
For the development of successful human-agent dialogue-based tutoring systems, it is essential to understand what makes a human-human tutorial dialogue successful. While there has been much research on dialogue-based intelligent tutoring systems, there have been comparatively fewer studies on analyzing large-scale datasets of human-human online…
Descriptors: Student Attitudes, Intelligent Tutoring Systems, Computer Software, Dialogs (Language)
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Jost, Patrick – International Association for Development of the Information Society, 2021
Educators are increasingly confronted with technology-driven learning scenarios. Even before the push from the current pandemic, digital learning apps became an integrated didactic tool. Advanced computing can thereby support the digital content creation for educational courses offered on mobile platforms. Computed media content such as natural…
Descriptors: Artificial Intelligence, Computer Software, Nonverbal Communication, Decision Making
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Amanova, Chynar – Quarterly Review of Distance Education, 2022
Current technologies for qualitative data analysis treat all types of data analysis as a homogeneous category, and for this reason, the value of other technologies for a discourse analysis of transcripts is not well examined. Therefore, the current study addresses how qualitative data can be analyzed by a learning analytic tool, such as Knowledge…
Descriptors: Student Attitudes, Foreign Students, Discourse Analysis, Computer Software
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Rodrigo, Ma. Mercedes T.; Baker, Ryan S. J. d.; McLaren, Bruce M.; Jayme, Alejandra; Dy, Thomas T. – International Educational Data Mining Society, 2012
In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to…
Descriptors: Thinking Skills, Educational Technology, Educational Research, Computer Software
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VanLehn, Kurt; Zhang, Lishan; Burleson, Winslow; Girard, Sylvie; Hidago-Pontet, Yoalli – IEEE Transactions on Learning Technologies, 2017
This project aimed to improve students' learning and task performance using a non-cognitive learning companion in the context of both a tutor and a meta-tutor. The tutor taught students how to construct models of dynamic systems and the meta-tutor taught students a learning strategy. The non-cognitive learning companion was designed to increase…
Descriptors: Metacognition, Learning Strategies, Nonverbal Communication, High School Students
Baker, Ryan S. J. d.; Gowda, Sujith M.; Wixon, Michael; Kalka, Jessica; Wagner, Angela Z.; Salvi, Aatish; Aleven, Vincent; Kusbit, Gail W.; Ocumpaugh, Jaclyn; Rossi, Lisa – International Educational Data Mining Society, 2012
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in…
Descriptors: Academic Achievement, Algebra, Tutors, Computer Software
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Charlebois, Conner; Hentschel, Nicholas; Frydenberg, Mark – Information Systems Education Journal, 2014
The Computer Information Systems Learning and Technology Sandbox (CIS Sandbox) opened as a collaborative learning lab during the fall 2011 semester at a New England business university. The facility employs 24 student workers, who, in addition to providing core tutoring services, are encouraged to explore new technologies and take on special…
Descriptors: Entrepreneurship, Business Schools, Information Systems, Information Science Education
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Lavoue, Elise; George, Sebastien; Prevot, Patrick – Behaviour & Information Technology, 2012
In this article, we present a co-adaptive design approach named TE-Cap (Tutoring Experience Capitalisation) that we applied for the development of an assistance environment for tutors. Since tasks assigned to tutors in educational contexts are not well defined, we are developing an environment which responds to needs which are not precisely…
Descriptors: Foreign Countries, Tutors, Tutoring, College Faculty
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Conard-Salvo, Tammy; Spartz, John M. – Writing Center Journal, 2012
This is a story of a failed study. In 2007, the authors set out to demonstrate that Kurzweil 3000, an adaptive text-to-speech software program, would help any student revise with its read-aloud function and numerous writing tools. During the course of the study, the authors confronted their misconceptions about students' technology use and…
Descriptors: Assistive Technology, Computer Software, Technology Uses in Education, Laboratories
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McLaren, Bruce M.; DeLeeuw, Krista E.; Mayer, Richard E. – Computers & Education, 2011
Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge),…
Descriptors: Feedback (Response), Test Items, Intervention, Intelligent Tutoring Systems
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Wood, Clare; Pillinger, Claire; Jackson, Emma – Computers & Education, 2010
This paper reports an extended analysis of the study reported in [Wood, C. (2005). "Beginning readers' use of 'talking books' software can affect their reading strategies." "Journal of Research in Reading, 28," 170-182.], in which five and six-year-old children received either six sessions using specially designed talking books or six sessions of…
Descriptors: Reading Strategies, Phonological Awareness, Interaction, Tutors
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Scerbinski, Jacqueline S. – Quarterly Review of Distance Education, 2009
This research brief addresses the quandary that arises when distance learners require both time and place utility, and prefer live interface. The development of the hybrid course, which incorporates elements of time delayed and instantaneous interaction, is seen as a response to instructor and student scheduling conflicts. Faculty and student…
Descriptors: Educational Strategies, Scheduling, Distance Education, Teaching Methods
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