Student Assessment by Optimal Questionnaire Design

In this paper a new technique is presented for automatic design of optimal questionnaires. The technique, that is based on the Item Response Theory, performs multiple-choice item selection by a Genetic Algorithm. The experimental results demonstrate the validity of the proposed approach to adjust the characteristics of the questionnaire to the abilities of the student class.

[1]  Hang Li,et al.  Mining Open Answers in Questionnaire Data , 2001, IEEE Intell. Syst..

[2]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[5]  S. IMPEDOVO,et al.  Advanced Methodologies for Student ' s Tests on e-Learning Courses : e-Examinations , 2006 .

[6]  Sabine Fenstermacher,et al.  Genetic Algorithms Data Structures Evolution Programs , 2016 .

[7]  Kelly A. Brennan,et al.  An item response theory analysis of self-report measures of adult attachment. , 2000, Journal of personality and social psychology.

[8]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[9]  Toon Calders,et al.  Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study , 2008, EDM.

[10]  Melvin R. Novick,et al.  Some latent train models and their use in inferring an examinee's ability , 1966 .

[11]  Sebastiano Impedovo,et al.  Traditional Learning Toward On-Line Learning , 2003 .

[12]  Donato Impedovo,et al.  A participant-based approach for e-learning evaluation , 2006 .

[13]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[14]  Sebastián Ventura,et al.  Using mobile and web‐based computerized tests to evaluate university students , 2009, Comput. Appl. Eng. Educ..

[15]  K. Robert Lai,et al.  Enrichment of Peer Assessment with Agent Negotiation , 2011, IEEE Transactions on Learning Technologies.

[16]  M. R. Novick,et al.  Statistical Theories of Mental Test Scores. , 1971 .

[17]  Hang Li,et al.  Mining from open answers in questionnaire data , 2001, KDD '01.

[18]  Yen-Liang Chen,et al.  Mining fuzzy association rules from questionnaire data , 2009, Knowl. Based Syst..

[19]  Gwo-Jen Hwang,et al.  A Computerized Approach to Diagnosing Student Learning Problems in Health Education , 2006 .

[20]  R. J. Mokken,et al.  Handbook of modern item response theory , 1997 .

[21]  Mark G. Simkin,et al.  How Well Do Multiple Choice Tests Evaluate Student Understanding in Computer Programming Classes? , 2003, J. Inf. Syst. Educ..

[22]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm , 1981 .

[23]  Dietmar F. Rösner,et al.  E-Assessment as a Service , 2011, IEEE Transactions on Learning Technologies.

[24]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters , 1982 .