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Ziyan Yang; Jia Hu; Shaochun Zhong; Lan Yang; Geyong Min – Education and Information Technologies, 2025
Intelligent technology plays a pivotal role in revolutionizing learning assessments, overcoming the constraints of traditional assessment methods and driving educational innovation. Knowledge tracing (KT) emerges as a critical component for assessing students' learning states and forecasting their future performance. However, existing graph-based…
Descriptors: Learning Processes, Artificial Intelligence, Graphs, Concept Mapping
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Liu, Chunhong; Zhang, Haoyang; Zhang, Jieyu; Zhang, Zhengling; Yuan, Peiyan – International Journal of Information and Communication Technology Education, 2023
Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally…
Descriptors: Educational Technology, Artificial Intelligence, Electronic Learning, Concept Mapping
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Cheng, Yu-Ping; Cheng, Shu-Chen; Huang, Yueh-Min – International Review of Research in Open and Distributed Learning, 2022
Online learning has been widely discussed in education research, and open educational resources have become an increasingly popular way to help learners acquire knowledge. However, these resources contain massive amounts of information, making it difficult for learners to identify Web articles that refer to computer science knowledge. This study…
Descriptors: Internet, Online Searching, Information Retrieval, Artificial Intelligence
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Knabe, Melina L.; Vlach, Haley A. – First Language, 2020
Ambridge argues that there is widespread agreement among child language researchers that learners store linguistic abstractions. In this commentary the authors first argue that this assumption is incorrect; anti-representationalist/exemplar views are pervasive in theories of child language. Next, the authors outline what has been learned from this…
Descriptors: Child Language, Children, Language Acquisition, Models
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Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – Educational Technology Research and Development, 2013
Betty's Brain is an open-ended learning environment in which students learn about science topics by teaching a virtual agent named Betty through the construction of a visual causal map that represents the relevant science phenomena. The task is complex, and success requires the use of metacognitive strategies that support knowledge acquisition,…
Descriptors: Artificial Intelligence, Computer Simulation, Computer Mediated Communication, Intelligent Tutoring Systems
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Carifio, James; Perla, Rocco J. – Interchange: A Quarterly Review of Education, 2009
This article presents a critical review and analysis of key studies that have been done in science education and other areas on the effects and effectiveness of using diagrams, graphs, photographs, illustrations, and concept maps as "adjunct visual aids" in the learning of scientific-technical content. It also summarizes and reviews those studies…
Descriptors: Concept Mapping, Maps, Graphs, Artificial Intelligence
Lim, Kyu Yon – ProQuest LLC, 2008
The purpose of this study was to investigate the effectiveness of concept mapping strategies with different levels of generativity in terms of knowledge acquisition and knowledge representation. Also, it examined whether or not learners' self-regulated learning (SRL) skills influenced the effectiveness of concept mapping strategies with different…
Descriptors: Concept Mapping, Undergraduate Students, Knowledge Representation, Program Effectiveness
Mueller, Richard J.; Mueller, Christine L. – 1995
The cognitive revolution began in the 1950s as researchers began to move away from the study of knowledge acquisition and behaviorism to the study of information and the way it is processed. Four factors are discussed in chapter 1 as contributing to the increase in popularity of the "cognitive revolution" (increasing enthusiasm for the…
Descriptors: Artificial Intelligence, Behavioral Science Research, Cognitive Processes, Concept Formation
Smith, Karl A. – Engineering Education, 1987
Differentiates between learning efficiency (enhancing the rate of learning) and learning effectiveness (enhancing the mastery and retention of facts, concepts, and relationships). Discusses some of the contributions of knowledge engineering to metalearning. Provides a concept map for constructing knowledge bases, along with some possible…
Descriptors: Artificial Intelligence, College Science, Concept Formation, Concept Mapping
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers