Abstract
In educational context (online or face-to-face), with increasing cohort size and the need for individualization, the question of partial or full automation of assessments is growing. This paper deals with preliminary works that tackle the question of automatic generation of assessment Tests that could guarantee fairness and reasonable difference, by the content and the structure. A structural metric characterizing the distance between two given Tests is presented. This metric provides a dedicated fitness function that leads to define a Genetic Algorithm (GA) technique. The original use of GA allows optimizing this structural differentiation and thus guarantees the generation of collections of Tests with the largest distance possible while involving the smallest items source database. Preliminary experiments and results on the basis of multiple choice questions items are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The notion of distinction between two Tests is then considered from a structural angle and as a first step, fairness of the assessments is out of the scope of this paper.
References
Kumar, V. (1992). Algorithms for constraint-satisfaction problems: A survey. AI Magazine, 13(1), 32.
Mackworth, A. K. (1992). Constraint satisfaction problems. Encyclopedia of AI, 285–293.
Garrison, C., & Ehringhaus, M. (2007). Formative and summative assessments in the classroom.
Harlen, W. (2006). On the relationship between assessment for formative and summative purposes. Assessment and Learning, 103–118.
Taras, M. (2005). Assessment-summative and formative-some theoretical reflections. British Journal of Educational Studies, 53(4), 466–478.
Harlen, W., & James, M. (1997). Assessment and learning: Differences and relationships between formative and summative assessment. Assessment in Education, 4(3), 365–379.
Knuth, D. (1998). The art of computer programming, volume 2: Seminumerical algorithms (3rd ed.). Boston: Addison-Wesley Publishing Company.
Hamming, R. W. (1950). Error detecting and error correcting codes. Bell Labs Technical Journal, 29(2), 147–160.
Norouzi, M., Fleet, D. J., & Salakhutdinov. R. R. (2012). Hamming distance metric learning. In Advances in Neural Information Processing Systems (pp. 1061–1069).
Levenshtein, V. I. (1966, February). Binary codes capable of correcting deletions, insertions, and reversals, 10.
Yujian, L., & Bo, L. (2007). A normalized levenshtein distance metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1091–1095.
Melanie, M. (1988). An introduction to genetic algorithms. London: MIT press.
Mitsuo, G., & Runwei, C. (2000). Genetic algorithms and engineering optimization. New York: Wiley.
Luan, J. (2002). Data mining and its applications in higher education. New Directions for Institutional Research, 2002(113), 17–36.
Burke, E., Newall, J., & Weare, R. (1996). A memetic algorithm for university exam timetabling. Practice and Theory of Automated Timetabling, 241–250.
Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 24(4), 656–667.
Ciguene, R., Joiron, C., & Dequen, G. (2016). Automatically generating assessment tests within higher education context thanks to genetic approach. In International Conference on Metaheuristics and Nature Inspired Computing, Morocco (Vol. 3, pp. 168–170). Marocco: Marrakech.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Ciguené, R., Joiron, C., Dequen, G. (2019). Automatically Generating Assessment Tests Within Higher Education Context Thanks to Genetic Approach. In: Talbi, EG., Nakib, A. (eds) Bioinspired Heuristics for Optimization. Studies in Computational Intelligence, vol 774. Springer, Cham. https://doi.org/10.1007/978-3-319-95104-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-95104-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95103-4
Online ISBN: 978-3-319-95104-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)