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LF Zhang (2001)
Thinking styles and personality types revisitedJ Personal Individ Differ, 31
V Yannibelli, D Godoy, A Amandi (2006)
A genetic algorithm approach to recognise students’ learning stylesInteract Learn Environ, 14
L Chittaro, M Serra (2004)
Behavioral programming of autonomous characters based on probabilistic automata and personalityJ Comput Anim Virtual Worlds, 15
E Sadler-Smith (2001)
The relationship between learning style and cognitive styleJ Personal Individ Differ, 30
P Hartmann (2006)
The five-factor model: psychometric, biological and practical perspectivesNord Psychol, 58
S Rushton, J Morgan, M Richard (2007)
Teacher’s Myers–Briggs personality profiles: identifying effective teacher personality traitsJ Teach Teach Educ, 23
M Poznanski, P Thagard (2005)
Changing personalities: towards realistic virtual charactersJ Exp Theor Artif Intell, 17
M Rani, R Nayak, OP Vyas (2015)
An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storageKnowl Based Syst, 90
K Lee, Y Choi, DJ Stonier (2012)
Evolutionary algorithm for a genetic robot’s personality based on the Myers–Briggs Type IndicatorJ Robot Auton Syst, 60
S Rakap (2010)
Impacts of learning styles and computer skills on adult students’ learning onlineTurk Online J Educ Technol TOJET, 9
R Santos, G Marreiros, C Ramos, J Neves, J Bulas-Cruz (2011)
Personality, emotion, and mood in agent-based group decision makingJ Intell Syst, 26
L Moshkina (2006)
An integrative framework for time-varying affective agent behavior
Z Zeng, M Pantic, GI Roisman, TS Huang (2009)
A survey ofaffect recognition methods: audio, visual, and spontaneous expressionsIEEE Trans Pattern Anal Mach Intell, 31
A Vincent, D Ross (2001)
Personalize training: determine learningstyles, personality types and multiple intelligences onlineLearn Organ, 8
RR McCrae, OP John (1992)
An introduction to the five-factor model and its applicationsJ Personal, 60
S Graf (2013)
Dynamic student modelling of learning styles for advanced adaptivity in learning management systemsInt J Inf Syst Social Change (IJISSC), 4
E Abrahamian, J Weinberg, M Grady, C Michael Stanton (2004)
The effect of personality-aware computer–human interfaces on learningJ Univers Comput Sci, 10
K Moore, JC McElroy (2012)
The influence of personality on Facebook usage, wall postings, and regretJ Comput Hum Behav, 28
C Jakson, LM Jones (1996)
Explaining the overlap between personality and learningstylesJ Personal Individ Differ, 20
A Furnham, CJ Jackson (1999)
Personality, learning styles and work performanceJ Personal Individ Differ, 27
EE Bachari, EH Abdelwahed, ME Adnani (2010)
Design of and adaptive e-learning model based on learner’s personalityUbiquitous Comput Commun J, 5
YS Li, PS Chen, SJ Tsai (2007)
A comparison of the learning styles among different nursing programs in Taiwan: implications for nursing educationJ Nurse Educ Today, 28
A Ortony, G Clore, A Collins (1990)
The cognitive structure of emotions
YH Wang, HC Liao (2011)
Data mining for adaptive learning in a TESL-based e-learning systemExpert Syst Appl, 38
RM Felder, LK Silverman (1988)
Learning and teaching styles in engineering educationJ Eng Educ, 78
A Klašnja-Milićević, B Vesin, M Ivanović, Z Budimac (2011)
E-Learning personalization based on hybrid recommendation strategy and learning style identificationComput Educ, 56
A Egges, S Kshirsagar, N Magnenat-Thalmann (2003)
A model for personality and emotion simulationJ Knowl Based Intell Inf Eng Syst, 2773
A Egges, S Kshirsagar, N Magnenat-Thalmann (2004)
Generic personality and emotion simulation for conversational agentsJ Comput Anim Virtual Worlds, 15
ND Fleming (2006)
V.A.R.K. visual, aural/auditory, read/write, kinesthetic
T Dewar, D Whittington (2000)
Online learners and their learning strategiesJ Educ Comput Res, 23
HG Lang, MS Stinson, F Kavanagh, Y Liu, M Basile (1999)
Learning styles of deaf college students and instructors’ teaching emphasesJ Deaf Stud Deaf Educ, 4
Today, one of the most important and challenging issues in artificial intelligence is modeling human behavior in virtual environments. Furthermore, studying e-learning environments is in great demand in computer science which requires understanding human behaviors. Thus, considering human behavior factors, such as personality, mood, and emotion, and modeling them in e-learning environments is a challenging issue in artificial intelligence. The purpose of this paper is to review the psychological models of personality used in computer science. In addition, the most important applications of personality models and their direct related topics in learning, i.e. learning style issues in e-learning environments, are presented. The study shows that researchers tend to use models that are simple to implement in virtual world and are as comprehensive as possible to cover all the features of human behavior. Finally, we concluded that models such as the Five Factor Model, the Myers–Briggs Type Indicator personality model, and Felder–Silverman learning styles model have the two most important features, which are simplicity and comprehensiveness. These two features have made these psychology models the most favorable in the virtual world.
Artificial Intelligence Review – Springer Journals
Published: Mar 8, 2016
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