Intelligent CAI: Old wine in new bottles, or a new vintage?
Artificial intelligence and instruction: Applications and methods
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
ICCAL '90 Proceedings of the third international conference on Computer assisted learning
Learner control in computer-based instruction: a current literature review
Educational Technology
On the effectiveness of a neural network for adaptive external pacing
Journal of Artificial Intelligence in Education
Journal of Interactive Learning Research
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Empirical Evaluation of User Models and User-Adapted Systems
User Modeling and User-Adapted Interaction
Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor
User Modeling and User-Adapted Interaction
Constraint-Based Tutors: A Success Story
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Incorporating Learning Characteristics into an Intelligent Tutor
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE
User Modeling and User-Adapted Interaction
Coalescing individual and collaborative learning to model user linguistic competences
User Modeling and User-Adapted Interaction
Intelligent agent supported personalization for virtual learning environments
Decision Support Systems
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Where's the proof? A review of literature on effectiveness of information technology in education
FIE '98 Proceedings of the 28th Annual Frontiers in Education - Volume 01
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
The relative impact of student affect on performance models in a spoken dialogue tutoring system
User Modeling and User-Adapted Interaction
Personalization in an interactive learning environment through a virtual character
Computers & Education
Adaptive Navigation Support, Learner Control and Open Learner Models
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Automatic Detection of Learner's Affect From Gross Body Language
Applied Artificial Intelligence
Some Unusual Open Learner Models
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Web-based adaptive training simulator system for cardiac life support
Artificial Intelligence in Medicine
A comparative analysis of cognitive tutoring and constraint-based modeling
UM'03 Proceedings of the 9th international conference on User modeling
Using local and global self-evaluations to predict students’ problem solving behaviour
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Towards simplifying learning systems: a critical review
Proceedings of the 31st ACM international conference on Design of communication
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The development of learner models takes an active part in upcoming adaptive learning environments. The purpose of learner models is to drive personalization based on learner and learning characteristics that are considered as important for the learning process, such as cognitive, affective and behavioral variables. Despite the huge amount of theoretical propositions of learner characteristics considered as relevant for learner models, practical payoffs are rather sparse. This study aims to overview the empirical research on the mere value of learner models in the development of adaptive learning environments. The results show that a lot of high-quality studies are situated in a rather shattered research field, building few bridges from theory to practice. We conclude with the call for a theory or framework integrating current and past research results that is able to guide theory-based and systematic empirical research having concrete hypotheses on the merits of learner characteristics in adaptive learning environments.