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Kuo, Yu-Chen; Chen, Yun-An – Education and Information Technologies, 2023
With the development of science and technology, the demand for programmers has increased. However, learning computer programs is not an easy task. It might cause a significant impact on programming if misconceptions exist at the beginning of the study. Hence, it is important to discover and correct them immediately. Chatbots are effective teaching…
Descriptors: Programming, Artificial Intelligence, Computer Science Education, Misconceptions
Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D. – IEEE Transactions on Learning Technologies, 2014
In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…
Descriptors: Artificial Intelligence, Concept Mapping, Teaching Methods, Student Evaluation
Fisher, Kathleen M. – 1985
A small set of relationships has been identified which appears to be sufficient for describing all molecular and cellular reactions and structures discussed in an introductory biology course. A precise definition has been developed for each relationship. These 20 relationships are of four types: (1) analytical; (2) spatial; (3) temporal; and (4)…
Descriptors: Artificial Intelligence, Biology, Cognitive Psychology, College Science
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
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
McAleese, Ray – Programmed Learning and Educational Technology, 1985
Summarizes findings of a collection of research studies at the University of Aberdeen (Scotland) aiding fundamental understanding of knowledge representation and its applications. Issues arising when a knowledge representation system is incorporated into an authoring language are discussed, including the problems of exteriorization, metacognition,…
Descriptors: Artificial Intelligence, Authoring Aids (Programing), 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

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