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Hosseini, Roya; Akhuseyinoglu, Kamil; Brusilovsky, Peter; Malmi, Lauri; Pollari-Malmi, Kerttu; Schunn, Christian; Sirkiä, Teemu – International Journal of Artificial Intelligence in Education, 2020
This research is focused on how to support students' acquisition of program construction skills through worked examples. Although examples have been consistently proven to be valuable for student's learning, the learning technology for computer science education lacks program construction examples with interactive elements that could engage…
Descriptors: Programming, Computer Science Education, Problem Solving, Learner Engagement
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Matsuda, Noboru – International Journal of Artificial Intelligence in Education, 2022
This paper demonstrates that a teachable agent (TA) can play a dual role in an online learning environment (OLE) for learning by teaching--the teachable agent working as a synthetic peer for students to learn by teaching and as an interactive tool for cognitive task analysis when authoring an OLE for learning by teaching. We have developed an OLE…
Descriptors: Artificial Intelligence, Teaching Methods, Intelligent Tutoring Systems, Feedback (Response)
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Iria Estévez-Ayres; Patricia Callejo; Miguel Ángel Hombrados-Herrera; Carlos Alario-Hoyos; Carlos Delgado Kloos – International Journal of Artificial Intelligence in Education, 2025
The emergence of Large Language Models (LLMs) has marked a significant change in education. The appearance of these LLMs and their associated chatbots has yielded several advantages for both students and educators, including their use as teaching assistants for content creation or summarisation. This paper aims to evaluate the capacity of LLMs…
Descriptors: Artificial Intelligence, Natural Language Processing, Computer Mediated Communication, Technology Uses in Education
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Jennings, Jay; Muldner, Kasia – International Journal of Artificial Intelligence in Education, 2021
When students are first learning to program, they not only have to learn to write programs, but also how to trace them. Code tracing involves stepping through a program step-by-step, which helps to predict the output of the program and identify bugs. Students routinely struggle with this activity, as evidenced by prior work and our own experiences…
Descriptors: Scaffolding (Teaching Technique), Tutors, Tutoring, Programming
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Albó, Laia; Barria-Pineda, Jordan; Brusilovsky, Peter; Hernández-Leo, Davinia – International Journal of Artificial Intelligence in Education, 2022
Over the last 10 years, learning analytics have provided educators with both dashboards and tools to understand student behaviors within specific technological environments. However, there is a lack of work to support educators in making data-informed design decisions when designing a blended course and planning appropriate learning activities. In…
Descriptors: Learning Analytics, Visual Aids, Design, Learning Activities
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Price, Thomas W.; Dong, Yihuan; Zhi, Rui; Paaßen, Benjamin; Lytle, Nicholas; Cateté, Veronica; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2019
In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different…
Descriptors: Comparative Analysis, Programming Languages, Data Analysis, Evaluation Methods
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Yazdanian, Ramtin; West, Robert; Dillenbourg, Pierre – International Journal of Artificial Intelligence in Education, 2021
The Fourth Industrial Revolution has considerably sped up the pace of skill changes in many professional domains, with scores of new skills emerging and many old skills moving towards obsolescence. For these domains, identifying the new necessary skills in a timely manner is a difficult task, where existing methods are inadequate. Understanding…
Descriptors: Electronic Learning, Personnel Selection, Computer Software, Computer Mediated Communication
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Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
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Hutchins, Nicole M.; Biswas, Gautam; Zhang, Ningyu; Snyder, Caitlin; Lédeczi, Ákos; Maróti, Miklós – International Journal of Artificial Intelligence in Education, 2020
Driven by our technologically advanced workplaces and the surge in demand for proficiency in the computing disciplines, it is becoming imperative to provide computational thinking (CT) opportunities to all students. One approach for making computing accessible and relevant to learning and problem-solving in K-12 environments is to integrate it…
Descriptors: Computer Assisted Instruction, Problem Solving, Computation, Thinking Skills
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Dermeval, Diego; Paiva, Ranilson; Bittencourt, Ig Ibert; Vassileva, Julita; Borges, Daniel – International Journal of Artificial Intelligence in Education, 2018
Authoring tools have been broadly used to design Intelligent Tutoring Systems (ITS). However, ITS community still lacks a current understanding of how authoring tools are used by non-programmer authors to design ITS. Hence, the objective of this work is to review how authoring tools have been supporting ITS design for non-programmer authors. In…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Software, Evidence
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Bosch, Nigel; D'Mello, Sidney – International Journal of Artificial Intelligence in Education, 2017
Novice students (N = 99) participated in a lab study in which they learned the fundamentals of computer programming in Python using a self-paced computerized learning environment involving a 25-min scaffolded learning phase and a 10-min unscaffolded fadeout phase. Students provided affect judgments at approximately 100 points (every 15 s) over the…
Descriptors: Employees, Programming, Computers, Novices
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Rivers, Kelly; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2017
To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…
Descriptors: Programming, Coding, Computers, Data
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Green, Nancy L. – International Journal of Artificial Intelligence in Education, 2017
This paper describes an educational argument modeling system, GAIL (Genetics Argumentation Inquiry Learning). Using GAIL's graphical interface, learners can select from possible argument content elements (hypotheses, data, etc.) displayed on the screen with which to construct argument diagrams. Unlike previous systems, GAIL uses domain-independent…
Descriptors: Persuasive Discourse, Feedback (Response), Inquiry, Computer Assisted Instruction
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Gerdes, Alex; Heeren, Bastiaan; Jeuring, Johan; van Binsbergen, L. Thomas – International Journal of Artificial Intelligence in Education, 2017
Ask-Elle is a tutor for learning the higher-order, strongly-typed functional programming language Haskell. It supports the stepwise development of Haskell programs by verifying the correctness of incomplete programs, and by providing hints. Programming exercises are added to Ask-Elle by providing a task description for the exercise, one or more…
Descriptors: Intelligent Tutoring Systems, Automation, Feedback (Response), Programming Languages
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Sottilare, Robert A.; Shawn Burke, C.; Salas, Eduardo; Sinatra, Anne M.; Johnston, Joan H.; Gilbert, Stephen B. – International Journal of Artificial Intelligence in Education, 2018
The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or…
Descriptors: Meta Analysis, Teaching Methods, Teamwork, Outcomes of Education