An Adaptive Questionnaire for Automatic Identification of Learning Styles

Learning styles refer to how a person acquires and processes information. Identifying the learning styles of students is important because it allows more personnalized teaching. The most popular method for learning style recognition is through the use of a questionnaire. Although such an approach can correctly identify the learning style of a student, it suffers from three important limitations: 1 filling a questionnaire is time-consuming since questionnaires usually contain numerous questions, 2 learners may lack time and motivation to fill long questionnaires and 3 a specialist needs to analyse the answers. In this paper, we address these limitations by presenting an adaptative electronic questionnaire that dynamically selects subsequent questions based on previous answers, thus reducing the number of questions. Experimental results with 1,931 questionnaires for the Myers Briggs Type Indicators show that our approach Q-SELECT considerably reduces the number of questions asked by a median of 30 % while predicting learning styles with a low error rate.

[1]  Ahmad Baylari,et al.  Design a personalized e-learning system based on item response theory and artificial neural network approach , 2009, Expert Syst. Appl..

[2]  Susan H. Jones,et al.  The Relationship Between the Myers-Briggs Type Indicator and the Eysenck Personality Questionnaire Among Adult Churchgoers , 2000 .

[3]  Essaid El Bachari,et al.  DESIGN OF AN ADAPTIVE E-LEARNING MODEL BASED ON LEARNER ’ S PERSONALITY , 2011 .

[4]  George D. Magoulas,et al.  Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment , 2002, Comput. Educ..

[5]  R. Felder,et al.  Understanding Student Differences , 2005 .

[6]  Pilar Rodríguez Marín,et al.  AH-questionnaire: An adaptive hierarchical questionnaire for learning styles , 2010, Comput. Educ..

[7]  Petri Nokelainen,et al.  Bayesian Modeling Approach to Implement an Adaptive Questionnaire , 2001 .

[8]  G. Boyle.  Myers‐Briggs Type Indicator (MBTI): Some Psychometric Limitations , 1995 .

[9]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[10]  Richard M. Felder,et al.  MATTERS OF STYLE , 2004 .

[11]  Maria G. Xanthou.  An intelligent personalized e-assessment tool developed and implemented for a Greek Lyric Poetry undergraduate course , 2013 .

[12]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[13]  Hahn-Ming Lee,et al.  Personalized e-learning system using Item Response Theory , 2005, Comput. Educ..

[14]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[16]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[17]  David McSherry.  Increasing Dialogue Efficiency in Case-Based Reasoning Without Loss of Solution Quality , 2003, IJCAI.

[18]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[19]  Analía Amandi,et al.  Evaluating Bayesian networks' precision for detecting students' learning styles , 2007, Comput. Educ..

[20]  Jacob D. Abernethy,et al.  An Optimization Framework for the Adaptive Design of Robust Choice Questionnaires , 2005 .