Reusing Assessments Tests by Generating Arborescent Test Groups Using a Genetic Algorithm
Using Information and Communication Technologies (ICT) notions in education and three basic processes of education (teaching, learning and assessment) can bring benefits to the pupils and the professional development of teachers. In this matter, we refer to these notions as concepts taken from the informatics area and apply them to the domain of education. These notions refer to genetic algorithms and arborescent structures, used in the specific process of assessment or evaluation. This paper uses these kinds of notions to generate subtrees from a main tree of tests related between them by their degree of difficulty. These subtrees must contain the highest number of connections between the nodes and the lowest number of missing edges (which are subtrees of the main tree) and, in the particular case of the non-existence of a subtree with no missing edges, the subtrees which have the lowest (minimal) number of missing edges between the nodes, where a node is a test and an edge is a direct connection between two tests which differs by one degree of difficulty. The subtrees are represented as sequences. The tests are the same (a number coding a test represents that test in every sequence) and they are reused for each sequence of tests.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130349Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 588
 D. Boud, N. Falchikov, Rethinking Assessment in Higher Education: Learning for the Longer Term, Routledge Publishing, 2007.
 E. MacLellan, “Assessment for Learning: The differing perceptions of tutors and students”, Assessment & Evaluation in Higher Education Volume 26, Issue 4, pp. 307-318, 2001.
 K. Struyven, F. Dochy, S. Janssens, “Students’ perceptions about new modes of assessment in higher education: a review, optimising new modes of assessment: in search of qualities and standards”, Innovation and Change in Professional Education, Volume 1, pp. 171-223, 2005.
 Ş. Valeriu, C. Ştefănescu, O. Roşu-Stoican, “The influence of using ICT on the quality of learning” in International Conference on Virtual Learning – ICVL, Timișoara, România, 2015, pp. 169-172.
 C. Roy, P. Wallace, “Paper–based versus computer–based assessment: key factors associated with the test mode effect”, British Journal of Educational Technology, Volume 33, Issue 5, pp. 593–602, 2002.
 E. Popescu, “Adaptation provisioning with respect to learning styles in a web-based educational system: an experimental study”, Journal of Computer Assisted Learning, Vol. 26(4), Wiley, pp. 243-257, 2010.
 C. Holotescu, “A conceptual model for Open Learning Environments” in International Conference on Virtual Learning – ICVL, Timișoara, România, 2015, pp. 54- 61.
 C. Baron, A. Şerb, N. M. Iacob, C. L. Defta, “IT infrastructure model used for implementing an e-learning platform based on distributed databases”, Quality-Access to Success Journal, Vol. 15, Issue 140, pp. 195-201, 2014.
 C. L. Defta, A. Şerb, N. M. Iacob, C. Baron, “Threats analysis for E-learning platforms”, Knowledge Horizons. Economics, Vol. 6 / Nr. 1, pp. 132–135, 2014.
 G. Martin, “Cognitive style and attitudes towards using online learning and assessment methods”, Electronic Journal of e-Learning, Volume 1, Issue 1, pp. 21-28, 2003.
 J. Gaytan, C. McEwen-Beryl, “Effective online instructional and assessment strategies”, American Journal of Distance Education, Volume 21, Issue 3, pp. 117-132, 2007.
 D. Nijloveanu, N. Bold, A.C. Bold, “A hierarchical model of test generation within a battery of tests” in International Conference on Virtual Learning-ICVL, Timișoara, România, 2015, pp. 147-153.
 D. E. Knuth., The art of computer programming, volume 3: (2nd ed.) sorting and searching, Addison Wesley Longman Publishing Co., Inc. Redwood City, CA, USA.
 Th. G. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization”, Machine Learning, Volume 40, Issue 2, pp. 139-157, August 2000.
 S. Rahmani, S. M. Mousavi, M. J. Kamali, “Modeling of road-traffic noise with the use of genetic algorithm”, Applied Soft Computing, Volume 11, Issue 1, pp. 1008–1013, 2011.
 L. G. Caldas, L. K. Norford, “A design optimization tool based on a genetic algorithm, Automation in Construction”, ACADIA '99, Volume 11, Issue 2, pp. 173–184, 2002.
 H.-S. Kim, S.-B. Cho, “Application of interactive genetic algorithm to fashion design”, Engineering Applications of Artificial Intelligence, Volume 13, Issue 6, Pages 635–644, December 2000.
 D. A. Popescu, D. Radulescu, “Approximately similarity measurement of web sites” in ICONIP, Neural Information Processing, Proceedings LNCS, Springer, 9-12 November 2015.
 D. A. Popescu, I. A. Popescu, “Model of determination of coverings with web pages for a website” in International Conference on Virtual Learning-ICVL, Timișoara, România, 2015, pp. 279-283.
 S. Darby, Th. V. Mortimer-Jones, R. L. Johnston and Ch. Roberts, “Theoretical study of Cu–Au nanoalloy clusters using a genetic algorithm”, J. Chem. Phys. 116, 1536, The Journal of Chemical Physics, Volume 116, Issue 4, 2002.
 D. A. Popescu, D. Radulescu, “Monitoring of irrigation systems using genetic algorithms” in ICMSAO, IEEE Xplorer, 2015, pp. 1-4.
 D. A. Popescu, D. Nicolae, “Generating a class schedule with reduced number of constraints” in the 9th International Scientific Conference eLearning and software for Education, Bucharest, April 25-26, 2013, ISI Proceedings, pp. 297-300.
 D. A. Popescu, “Probabilistic program that generates Langford sequences”, Scientific Bulletin – University of Pitești, Mathematics and Computer Sciences Series, (Buletin Stiințific - Universitatea din Pitești, Seria Matematică și Informatică), Nr. 12, pp. 129-133, 2006.