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Showing 1 to 15 of 24 results Save | Export
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Vannaprathip, Narumol; Haddawy, Peter; Schultheis, Holger; Suebnukarn, Siriwan – International Journal of Artificial Intelligence in Education, 2022
Virtual reality simulation has had a significant impact on training of psychomotor surgical skills, yet there is still a lack of work on its use to teach surgical decision making. This is particularly noteworthy given the recognized importance of decision making in achieving positive surgical outcomes. With the objective of filling this gap, we…
Descriptors: Intelligent Tutoring Systems, Decision Making, Surgery, Teaching Methods
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Cody, Christa; Maniktala, Mehak; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2022
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation…
Descriptors: Comparative Analysis, Prior Learning, Intelligent Tutoring Systems, Problem Solving
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Katz, Sandra; Albacete, Patricia; Chounta, Irene-Angelica; Jordan, Pamela; McLaren, Bruce M.; Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…
Descriptors: Physics, Science Instruction, Pretests Posttests, Scores
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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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Chen, Xingliang; Mitrovic, Antonija; Mathews, Moffat – International Journal of Artificial Intelligence in Education, 2019
Agency refers to the level of control the student has over learning. Most studies on agency in computer-based learning environments have been conducted in the context of educational games and multimedia learning, while there is little research done in the context of learning with Intelligent Tutoring Systems (ITSs). We conducted a study in the…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Educational Games, Independent Study
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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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Fabic, Geela Venise Firmalo; Mitrovic, Antonija; Neshatian, Kourosh – International Journal of Artificial Intelligence in Education, 2019
The overarching goal of our project is to design effective learning activities for PyKinetic, a smartphone Python tutor. In this paper, we present a study using a variant of Parsons problems we designed for PyKinetic. Parsons problems contain randomized code which needs to be re-ordered to produce the desired effect. In our variant of Parsons…
Descriptors: Telecommunications, Handheld Devices, Cues, Intelligent Tutoring Systems
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Worsley, Marcelo; Blikstein, Paulo – International Journal of Artificial Intelligence in Education, 2018
This paper presents three multimodal learning analytic approaches from a hands-on learning activity. We use video, audio, gesture and bio-physiology data from a two-condition study (N = 20), to identify correlations between the multimodal data, experimental condition, and two learning outcomes: design quality and learning. The three approaches…
Descriptors: Multimedia Materials, Correlation, Outcomes of Education, Design
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Passonneau, Rebecca J.; Poddar, Ananya; Gite, Gaurav; Krivokapic, Alisa; Yang, Qian; Perin, Dolores – International Journal of Artificial Intelligence in Education, 2018
Development of reliable rubrics for educational intervention studies that address reading and writing skills is labor-intensive, and could benefit from an automated approach. We compare a main ideas rubric used in a successful writing intervention study to a highly reliable wise-crowd content assessment method developed to evaluate…
Descriptors: Computer Assisted Testing, Writing Evaluation, Content Analysis, Scoring Rubrics
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Vajjala, Sowmya – International Journal of Artificial Intelligence in Education, 2018
Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in…
Descriptors: Computer Software, Essays, Writing Evaluation, Scoring
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Fratamico, Lauren; Conati, Cristina; Kardan, Samad; Roll, Ido – International Journal of Artificial Intelligence in Education, 2017
Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a…
Descriptors: Computer Simulation, Models, Educational Environment, Comparative Analysis
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Rau, Martina Angela – International Journal of Artificial Intelligence in Education, 2017
Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…
Descriptors: Knowledge Representation, Models, Competence, Learning Processes
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Tansomboon, Charissa; Gerard, Libby F.; Vitale, Jonathan M.; Linn, Marcia C. – International Journal of Artificial Intelligence in Education, 2017
Supporting students to revise their written explanations in science can help students to integrate disparate ideas and develop a coherent, generative account of complex scientific topics. Using natural language processing to analyze student written work, we compare forms of automated guidance designed to motivate productive revision and help…
Descriptors: Automation, Guidance, Revision (Written Composition), Natural Language Processing
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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
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Dennis, Matt; Masthoff, Judith; Mellish, Chris – International Journal of Artificial Intelligence in Education, 2016
As feedback is an important part of learning and motivation, we investigate how to adapt the feedback of a conversational agent to learner personality (as well as to learner performance, as we expect an interaction effect between personality and performance on feedback). We investigate two aspects of feedback. Firstly, we investigate whether the…
Descriptors: Feedback (Response), Personality Traits, Academic Achievement, Literary Styles
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