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Wenchao Ma; Miguel A. Sorrel; Xiaoming Zhai; Yuan Ge – Journal of Educational Measurement, 2024
Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of…
Descriptors: Models, Misconceptions, Diagnostic Tests, Ability
Kubsch, Marcus; Krist, Christina; Rosenberg, Joshua M. – Journal of Research in Science Teaching, 2023
Machine learning (ML) has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students' work. Despite this promise, we and other scholars argue that machine learning has…
Descriptors: Science Education, Educational Research, Artificial Intelligence, Models
Jyoti Wadmare; Dakshita Kolte; Kapil Bhatia; Palak Desai; Ganesh Wadmare – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper highlights an innovative and impactful online operating system algorithms e-learning tool in engineering education. Background: Common teaching methodologies make it difficult to teach complex algorithms of operating systems. This paper presents a solution to this problem by providing simulations of different complex…
Descriptors: Engineering, Science Education, Material Development, Computer Simulation

Niaz, Mansoor – Science Education, 1995
Describes a study with the main objective of constructing models based on strategies students use to solve chemistry problems and to show that these models form sequences of progressive transitions termed "problemshifts" that increase the explanatory/heuristic power of the model. Results implies that the relationship between algorithmic…
Descriptors: Algorithms, Chemistry, Concept Formation, Models
Hinton, Geoffrey E. – Scientific American, 1992
Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…
Descriptors: Algorithms, Artificial Intelligence, Cognitive Processes, Experiments