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Zhang, Qiao; Maclellan, Christopher J. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…
Descriptors: Electronic Learning, Mastery Learning, Computer Simulation, Intelligent Tutoring Systems
Goldberg, Benjamin; Amburn, Charles; Ragusa, Charlie; Chen, Dar-Wei – International Journal of Artificial Intelligence in Education, 2018
The U.S. Army is interested in extending the application of intelligent tutoring systems (ITS) beyond cognitive problem spaces and into psychomotor skill domains. In this paper, we present a methodology and validation procedure for creating expert model representations in the domain of rifle marksmanship. GIFT (Generalized Intelligent Framework…
Descriptors: Psychomotor Skills, Intelligent Tutoring Systems, Program Validation, Models
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
Tack, Anaïs; Piech, Chris – International Educational Data Mining Society, 2022
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…
Descriptors: Artificial Intelligence, Dialogs (Language), Bayesian Statistics, Decision Making
Tunjera, Nyarai; Chigona, Agnes – International Journal of Information and Communication Technology Education, 2020
The study examined how teacher educators are appropriating technological, pedagogical, and content knowledge (TPACK) and substitution, augmentation, modification, redefinition (SAMR) frameworks in their pre-service teacher preparation programmes. To ensure rigor, quality, and preparedness of pre-service teachers, there is a need to articulate…
Descriptors: Teacher Educators, Technological Literacy, Pedagogical Content Knowledge, Models
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle; Wortha, Franz – Teachers College Record, 2017
Metacognition and emotions play a critical role in learners' ability to monitor and regulate their learning about 21st-century skills related to science, technology, engineering, and mathematics (STEM) content while using advanced learning technologies (ALTs; e.g., intelligent tutoring systems, serious games, hypermedia, augmented reality). In…
Descriptors: Metacognition, Psychological Patterns, STEM Education, Educational Technology
Hernández, Yasmin; Pérez-Ramírez, Miguel; Zatarain-Cabada, Ramon; Barrón-Estrada, Lucia; Alor-Hernández, Giner – Educational Technology & Society, 2016
Electrical tests involve high risk; therefore utility companies require highly qualified electricians and efficient training. Recently, training for electrical tests has been supported by virtual reality systems; nonetheless, these training systems are not yet adaptive. We propose a b-learning model to support adaptive and distance training. The…
Descriptors: Models, Computer Simulation, Distance Education, Teaching Methods
Clement, Benjamin; Roy, Didier; Oudeyer, Pierre-Yves; Lopes, Manuel – Journal of Educational Data Mining, 2015
We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two…
Descriptors: Learning Activities, Intelligent Tutoring Systems, Models, Teaching Methods
Min, Wookhee; Wiggins, Joseph B.; Pezzullo, Lydia G.; Vail, Alexandria K.; Boyer, Kristy Elizabeth; Mott, Bradford W.; Frankosky, Megan H.; Wiebe, Eric N.; Lester, James C. – International Educational Data Mining Society, 2016
Recent years have seen a growing interest in intelligent game-based learning environments featuring virtual agents. A key challenge posed by incorporating virtual agents in game-based learning environments is dynamically determining the dialogue moves they should make in order to best support students' problem solving. This paper presents a…
Descriptors: Prediction, Models, Intelligent Tutoring Systems, Computer Simulation
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Kim, Julia M.; Hill, Randall W., Jr.; Durlach, Paula J.; Lane, H. Chad; Forbell, Eric; Core, Mark; Marsella, Stacy; Pynadath, David; Hart, John – International Journal of Artificial Intelligence in Education, 2009
Negotiation skills are essential in everyday life, whether in a professional or personal context. Negotiation enables two parties to address misunderstandings and avoid conflicts through an exchange that depends as much on the interpersonal skills of the negotiators as the tactics employed. Acquiring these skills requires not only sound conceptual…
Descriptors: Foreign Countries, Concept Formation, Cultural Context, Intelligent Tutoring Systems
Laurillard, D. M. – 1983
This paper discusses the styles of computer-based learning and computer-based training in terms of their fundamental characteristics, including balance of control between program and learner, and the kinds of learning activity they induce. Learning and training are not considered to be intrinsically different in practice, and styles such as…
Descriptors: Cognitive Processes, Computer Assisted Instruction, Computer Simulation, Courseware
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