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Mohammad M. Khajah – Journal of Educational Data Mining, 2024
Bayesian Knowledge Tracing (BKT) is a popular interpretable computational model in the educational mining community that can infer a student's knowledge state and predict future performance based on practice history, enabling tutoring systems to adaptively select exercises to match the student's competency level. Existing BKT implementations do…
Descriptors: Students, Bayesian Statistics, Intelligent Tutoring Systems, Cognitive Development
Davy Tsz Kit Ng; Jiahong Su; Jac Ka Lok Leung; Samuel Kai Wah Chu – Interactive Learning Environments, 2024
Artificial intelligence (AI) literacy has emerged to equip students with digital skills for effective evaluation, communication, collaboration, and ethical use of AI in online, home, and workplace settings. Countries are increasingly developing AI curricula to support students' technological skills for future studies and careers. However, there is…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Secondary School Students
Mustafa, Ghulam; Abbas, Muhammad Azeem; Hafeez, Yaser; Khan, Sharifullah; Hwang, Gwo-Jen – Interactive Learning Environments, 2019
During early childhood, children start developing their cognitive, social, emotional, and behavioural skills, laying the foundation for life-long learning. Cognitive skills are usually taught in traditional classrooms through the use of textbooks and worksheets. The learning content in these textbooks and worksheets is static pre-authored content…
Descriptors: Cognitive Development, Preschool Children, Child Development, Skill Development
Walker, Andrew; Belland, Brian R.; Kim, Nam Ju; Lefler, Mason – AERA Online Paper Repository, 2017
Baeysian Network Meta-Analysis represents a rather unique challenge in assessing the quality of included studies. Prior efforts to synthesize computer based scaffolding are in need of a closer examination of research quality. This study examines two quality metrics for meta-analysis, study design, and risk of bias (Higgins et al., 2011). Lower…
Descriptors: Scaffolding (Teaching Technique), STEM Education, Research Design, Risk
Murray, Tom – International Journal of Artificial Intelligence in Education, 2016
Intelligent Tutoring Systems authoring tools are highly complex educational software applications used to produce highly complex software applications (i.e. ITSs). How should our assumptions about the target users (authors) impact the design of authoring tools? In this article I first reflect on the factors leading to my original 1999 article on…
Descriptors: Usability, Programming, Computer Software, Intelligent Tutoring Systems
Kim, Min Kyu – Educational Technology Research and Development, 2012
It is generally accepted that the cognitive development for a wide range of students can be improved through adaptive instruction-learning environments optimized to suit individual needs (e.g., Cronbach, Am Psychol 12:671-684, 1957; Lee and Park, in Handbook of research for educational communications and technology, Taylor & Francis Group,…
Descriptors: Expertise, Problem Solving, Cognitive Development, Student Evaluation
Laureano-Cruces, Ana Lilia; Ramirez-Rodriguez, Javier; Mora-Torres, Martha; de Arriaga, Fernando; Escarela-Perez, Rafael – Interactive Learning Environments, 2010
In this paper behavior during the teaching-learning process is modeled by means of a fuzzy cognitive map. The elements used to model such behavior are part of a generic didactic model, which emphasizes the use of cognitive and operative strategies as part of the student-tutor interaction. Examples of possible initial scenarios for the…
Descriptors: Cognitive Mapping, Educational Technology, Teaching Methods, Cognitive Development
Craig, Scotty D.; Graesser, Arthur C.; Sullins, Jeremiah; Gholson, Barry – Journal of Educational Media, 2004
The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent…
Descriptors: Learning Processes, Natural Language Processing, Correlation, Constructivism (Learning)
Hilem, Y.; Futtersack, M. – 1994
The use of numerical simulations to design and analyze new products requires both conceptual and operational knowledge. This paper describes COMPANION: an intelligent multimedia system for vocational training used by engineers and technicians. A key concept in terms of training methodology is that of "situated learning," or continuous…
Descriptors: Cognitive Development, Computer Assisted Instruction, Computer Simulation, Engineering
Takaoka, Ryo; Okamoto, Toshio – 1994
As a person learns, his problem solving ability improves and one reason for this is the increased acquisition of "macro-rules" which make problem solving more efficient. An intelligent computer assisted learning (ICAI) system is being developed which automatically acquires the useful knowledge from the domain experts; as experts give the learning…
Descriptors: Cognitive Development, Cognitive Processes, Computer Assisted Instruction, Computer System Design
Peer reviewedKoschmann, Timothy – Artificial Intelligence, 1996
Reviews Dreyfus's writings about human cognition and artificial intelligence (AI), and explains some of the implications of his position, particularly in education. Topics include Dreyfus' critique of AI, representationlaism and expertise, technology and its role in instruction, computer-assisted instruction, and intelligent tutoring systems. (JKP)
Descriptors: Artificial Intelligence, Cognitive Development, Cognitive Processes, Cognitive Psychology
Baylor, Amy – Educational Technology, 1999
Examines the educational potential for intelligent agents as cognitive tools. Discusses the role of intelligent agents: managing large amounts of information (information overload), serving as a pedagogical expert, and creating programming environments for the learner. (AEF)
Descriptors: Artificial Intelligence, Cognitive Development, Educational Media, Educational Technology

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