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How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences
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Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
How to Optimize Student Learning Using Student Models That Adapt Rapidly to Individual Differences
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Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Exploring Individual Differences as Factors to Maximize Interactive Learning Environments for Future Learning
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Goh, Joelene; Truman, Barbara; Barber, Daniel – Interactive Learning Environments, 2019
Identifying characteristics of individuals who will be negatively impacted by interactive learning environments (ILE) was explored in a field study conducted among learners whose high-stakes performance depended upon grasping training scenarios quickly. Gauging the appropriateness of computer-based simulations for individuals became a pedagogical…
Descriptors: Individual Differences, Computer Assisted Instruction, Educational Technology, Self Efficacy
Working Memory Capacity and Mobile Multimedia Learning Environments: Individual Differences in Learning While Mobile
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Doolittle, Peter E.; Mariano, Gina J. – Journal of Educational Multimedia and Hypermedia, 2008
The present study examined the effects of individual differences in working memory capacity (WMC) on learning from an historical inquiry multimedia tutorial in stationary versus mobile learning environments using a portable digital media player (i.e., iPod). Students with low (n = 44) and high (n = 40) working memory capacity, as measured by the…
Descriptors: Physical Activities, Recall (Psychology), Short Term Memory, Multimedia Instruction
The Impact of Learner's Prior Knowledge on Their Use of Chemistry Computer Simulations: A Case Study
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Liu, Han-Chin; Andre, Thomas; Greenbowe, Thomas – Journal of Science Education and Technology, 2008
It is complicated to design a computer simulation that adapts to students with different characteristics. This study documented cases that show how college students' prior chemistry knowledge level affected their interaction with peers and their approach to solving problems with the use of computer simulations that were designed to learn…
Descriptors: Science Instruction, Individual Differences, Knowledge Level, Prior Learning
New Aptitudes For Adaptive Instruction: A Computer Simulation of a Learning Environment Individualized By Human Information Processes and Reinforcement Contingencies. Working Paper Number Four.
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Kribs, H. Dewey – 1973
A description is provided of a computer-based simulation of an instructional system which adapts the learning environment to the individual's unique attributes for processing information and for being motivated. The main purposes of the simulation are: 1) to introduce, as attributes for individualizing instruction, information processing variables…
Descriptors: Behavioral Science Research, Cognitive Processes, Computer Assisted Instruction, Individual Characteristics