NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
Program for International…1
What Works Clearinghouse Rating
Showing all 11 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
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
Peer reviewed Peer reviewed
Direct linkDirect link
Petscher, Yaacov; Compton, Donald L.; Steacy, Laura; Kinnon, Hannah – Annals of Dyslexia, 2020
Models of word reading that simultaneously take into account item-level and person-level fixed and random effects are broadly known as explanatory item response models (EIRM). Although many variants of the EIRM are available, the field has generally focused on the doubly explanatory model for modeling individual differences on item responses.…
Descriptors: Item Response Theory, Reading Skills, Individual Differences, Models
Morley, Patricia; Zmood, Simone – Mathematics Education Research Group of Australasia, 2015
Participation in society is increasingly dependent on educational achievement. Accordingly, society as a whole is committing more resources to education to prevent the adverse outcome of students moving through the school system only to emerge without the knowledge and skills that they might be expected to attain. In this paper, we explore the…
Descriptors: Mathematics Education, Educational Practices, Models, Mathematics Curriculum
Gropper, George L. – Educational Technology, 2015
This article takes a contrarian position: an "instructional design" or "teacher training" model, because of the sheer number of its interconnected parameters, is too complex to assess or to compare with other models. Models may not be the way to go just yet. This article recommends instead prior experimental research on limited…
Descriptors: Instructional Design, Models, Teacher Education, Differences
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Tulis, Maria; Steuer, Gabriele; Dresel, Markus – Frontline Learning Research, 2016
Errors bear the potential to improve knowledge acquisition, provided that learners are able to deal with them in an adaptive and reflexive manner. However, learners experience a host of different--often impeding or maladaptive--emotional and motivational states in the face of academic errors. Research has made few attempts to develop a theory that…
Descriptors: Error Patterns, Metacognition, Learning Processes, Learning Motivation
Gropper, George L. – Educational Technology, 2015
Instructional design can be more effective if it is as fixedly dedicated to the accommodation of individual differences as it currently is to the accommodation of subject matters. That is the hypothesis. A menu of accommodation options is provided that is applicable at each of three stages of instructional development or administration: before,…
Descriptors: Instructional Design, Individual Differences, Student Needs, Remedial Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Mayor, Julien; Plunkett, Kim – Psychological Review, 2010
We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…
Descriptors: Generalization, Vocabulary Development, Classification, Language Acquisition
Peer reviewed Peer reviewed
Mumaw, Randall J.; Pellegrino, James W. – Journal of Educational Psychology, 1984
An information-processing model was tested for a laboratory visualization task that represents one adaptation of a standardized spatial ability test. The pattern of results suggests that individual differences are a function of differences in the accuracy and/or quality of the mental representation, not just speed of processing. (Author/BW)
Descriptors: Cognitive Processes, Difficulty Level, Encoding (Psychology), Error Patterns
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection