Main Article Content
This paper explores a system that attempts to maximize high school students’ sense of choice when selecting elective subjects. We propose that individual schools can tailor the combinations of subjects they offer in order to maximize the number of prospective students who can study their preferred subjects, potentially increasing enrol- ment numbers and academic outcomes while also reducing administrative overheads. We analyze the underlying computational problem encountered in this task and describe a suitable AI-based optimization algorithm that we have made available for free download. We also discuss some outcomes of using this method on a small number of case study schools.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Burns, J. (2017). School budget squeeze “is reducing pupils’ subject choice”. https://www.bbc.co.uk/news/ education-39527183. BBC.
Cambazard, H., Hebrard, E., O’Sullivan, B., & Papadopoulos, A. (2012). Local search and constraint programming for the post enrolment-based timetabling problem. Annals of Operational Research, 194, 111–135. doi: https://dx.doi.org/10.1007/ s10479-010-0737-7
Donaldson, G. (2015, February). Successful futures: Independent review of curriculum and assessment arrange- ments in Wales. https://www.ewc.wales/site/index.php/en/documents/ite-accreditation/ 802-donaldson-report-successful-futures.html.
Educating Cardiff. (2019). https://www.channel4.com/programmes/educating-cardiff/.
Egeblad, J., & Pisinger, D. (2009). Heuristic approaches for the two- and three-dimensional knapsack packing problem. Computers and Operational Research, 36(4), 1026–1049. doi: https://doi.org/10.1016/j.cor.2007.12.004
Garey, M., & Johnson, D. (1979). Computers and intractability — A guide to NP-completeness. San Francisco: W. H. Freeman and Company.
Hanover Research. (2014, November). Impact of student choice and personalized learning. http://www.gssaweb.org/wp-content/uploads/2015/04/Impact-of-Student-Choice-and-Personalized-Learning-1 .pdf.
Harackiewicz, J., & Hulleman, C. (2010). The importance of interest: The role of achievement goals and task values in promoting the development of interest. Social and Personality Psychology Compass, 4(1), 42–52. doi: https://doi.org/ 10.1111/j.1751-9004.2009.00207.x
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60, 549–571. doi: https://doi.org/10.3102%2F00346543060004549
Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 4598, 671–680. doi: https://doi.org/10.1126/science.220.4598.671
Kostuch, P. (2005). The university course timetabling problem with a 3-phase approach. In E. Burke & M. Trick (Eds.), Practice and theory of automated timetabling (PATAT) V (Vol. 3616, pp. 109–125). Berlin: Springer-Verlag.
Lewis, R. (2016). A guide to graph colouring: Algorithms and applications. Springer International Publishing. doi: https://doi.org/10.1007/978-3-319-25730-3
Lewis, R., & Thompson, J. (2015). Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem. European Journal of Operational Research, 240, 637–648. doi: https://doi.org/10.1016/ j.ejor.2014.07.041
Little, D. (1995). Learning as dialogue: The dependence of learner autonomy on teacher autonomy. System, 23(2), 175–181. doi: https://doi.org/10.1016/0346-251X(95)00006-6
Perea, C., Alcaca, J., Yepes, V., Gonzalez-Vidosa, F., & Hospitaler, A. (2008). Design of reinforced concrete bridge frames by heuristic optimization. Advances in Engineering Software, 39(8), 676–688. doi: https://doi.org/10.1016/ j.advengsoft.2007.07.007
Reality Check team, BBC News. (2018). A-levels: What subjects are students dropping and why? https://www.bbc.co .uk/news/uk-45171371. BBC.
Schiefele, U., Krapp, A., & Winteler, A. (1992). Interest as a predictor of academic achievement: A meta-analysis of research. In K.
Renninger, S. Hidi, & A. Krapp (Eds.), The role of interest in learning and development (pp. 183–221). New York: Psychology Press.
SIMS Options. (2019). https://www.capita-sims.co.uk/products-and-services/sims-options. Yamin-Ali, J. (2014). Data-driven decision-making in schools: Lessons from Trinidad. Palgrave Macmillan.