Dynamic and Partial Grading of SQL Queries
by Benard Wanjiru* 
, Patrick van Bommel and Djoerd Hiemstra
Radboud University, Nijmegen, Postbus 9010 6500 GL Nijmegen, The Netherlands
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 8, Page # 1-14, 2024; DOI: 10.55708/js0308001
Keywords: Correctness Levels, Software Correctness, Automated Grading, Assessment, Partial
Marks, SQL Query Grading
Received: 12 July, 2024, Revised: 20 July, 2024, Accepted: 21 July, 2024, Published Online: 01 August, 2024
APA Style
Wanjiru, B., Van Bommel, P., & Hiemstra, D. (2024). Dynamic and partial grading of SQL queries. Journal of Engineering Research and Sciences, 3(8), 1-14. https://doi.org/10.55708/js0308001
Chicago/Turabian Style
Wanjiru, Benard, Patrick Van Bommel, and Djoerd Hiemstra. “Dynamic and Partial Grading of SQL Queries.” Journal of Engineering Research and Sciences 3, no. 8 (2024): 1-14. https://doi.org/10.55708/js0308001.
IEEE Style
B. Wanjiru, P. Van Bommel, and D. Hiemstra, “Dynamic and Partial Grading of SQL Queries,” Journal of Engineering Research and Sciences, vol. 3, no. 8, pp. 1-14, 2024, doi: 10.55708/js0308001.
130 Downloads
Abstract
Full Text
References
Cited By
Metrics
Abstract
Automated grading systems can help save a lot of time when evaluating students’ assignments. In this paper we present our ongoing work for a model for generating correctness levels. We utilize this model to demonstrate how we can grade students SQL queries employing partial grading in order to allocate points to parts of the queries well written and to enable provision of feedback for the missing parts. Furthermore, we show how we can grade the queries taking into account the skill level of students at different stages of SQL learning process. We divide the stages into introductory, intermediary, and advanced stages and in each apply different type of grading that takes account the students’ knowledge at that stage. We implemented this model in our class and graded 5 quizzes containing more than 25 different questions for 309 students. We discuss 3 examples for each stage and offer comprehensive examples of the model in action.
Full Text
References
- T. Seifert, “Understanding student motivation”, Educational Research, vol. 46, no. 2, pp. 137–149, 2004, doi:10.1080/0013188042000222421.
- B. Chandra, A. Banerjee, U. Hazra, M. Joseph, S. Sudarshan, “Au- tomated grading of SQL queries”, “2019 IEEE 35th International Conference on Data Engineering (ICDE)”, pp. 1630–1633, 2019, doi:10.1109/ICDE.2019.00159.
- G. Dambić, M. Fabijanić, A. L. Ćošković, “Automatic, configurable and partial assessment of student SQL queries with joins and group- ings”, “2021 44th International Convention on Information, Com- munication and Electronic Technology (MIPRO)”, pp. 837–842, 2021, doi:10.23919/MIPRO52101.2021.9596680.
- M. Fabijanić, G. Dambić, J. Sasunić, “Automatic, configurable, and partial assessment of student SQL queries with subqueries”, “2022 45th Jubilee International Convention on Information, Communi- cation and Electronic Technology (MIPRO)”, pp. 542–547, 2022, doi:10.23919/MIPRO55190.2022.9803559.
- J. Kjerstad, “Automatic evaluation and grading of SQL queries using relational algebra trees”, Master’s thesis, Norwegian University of Science and Technology, 2020.
- T. J. McGill, S. E. Volet, “A conceptual framework for analyz- ing students’ knowledge of programming”, Journal of Research on Computing in Education, vol. 29, no. 3, pp. 276–297, 1997, doi:10.1080/08886504.1997.10782199.
- B. Shneiderman, R. Mayer, “Syntactic/semantic interactions in pro- grammer behavior: A model and experimental results”, Interna- tional Journal of Parallel Programming, vol. 8, pp. 219–238, 1979, doi:10.1007/BF00977789.
- B. Shneiderman, “Teaching programming: A spiral approach to syn- tax and semantics”, Computers & Education, vol. 1, no. 4, pp. 193–197, 1977.
- A. Stefik, S. Siebert, “An empirical investigation into programming language syntax”, ACM Transactions on Computing Education (TOCE), vol. 13, no. 4, pp. 1–40, 2013.
- K. Renaud, J. van Biljon, “Teaching sql — which pedagogical horse for this course?”, H. Williams, L. MacKinnon, eds., “Key Technologies for Data Management”, pp. 244–256, Springer Berlin Heidelberg, Berlin, Heidelberg, 2004.
- P. Garner, J. A. Mariani, “Learning sql in steps”, Journal on Systemics, Cybernetics and Informatics, vol. 13, pp. 19–24, 2015.
- H. Al Shauily, K. Renaud, “A framework for sql learning: linking learning taxonomy, cognitive model and cross cutting factors”, Inter- national Journal of Computer and Systems Engineering, vol. 10, no. 9, pp. 3105–3111, 2016.
- A. Bhangdiya, B. Chandra, B. Kar, B. Radhakrishnan, K. V. M. Reddy, S. Shah, S. Sudarshan, “The XDa-TA system for automated grading of SQL query assignments”, 2015 IEEE 31st International Conference on Data Engineering, pp. 1468–1471, 2015.
- S. Dekeyser, M. de Raadt, T. Y. Lee, “Computer assisted assessment of SQL query skills”, “Proceedings of the Eighteenth Conference on Australasian Database – Volume 63”, ADC ’07, p. 53–62, Australian Computer Society, Inc., AUS, 2007.
- M. Gilsing, J. Pelay, F. Hermans, “Design, implementa- tion and evaluation of the hedy programming language”, Journal of Computer Languages, vol. 73, p. 101158, 2022, doi:https://doi.org/10.1016/j.cola.2022.101158.
- J. C. Prior, R. Lister, “The backwash effect on SQL skills grading”, “Proceedings of the 9th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education”, ITiCSE ’04, p. 32–36, Association for Computing Machinery, New York, NY, USA, 2004, doi:10.1145/1007996.1008008.
- M. Kramer, M. Barkmin, D. Tobinski, T. Brinda, “Understanding the differences between novice and expert programmers in memoriz- ing source code”, A. Tatnall, M. Webb, eds., “Tomorrow’s Learning: Involving Everyone. Learning with and about Technologies and Com- puting”, pp. 630–639, Springer International Publishing, Cham, 2017.
- M. Weiser, J. Shertz, “Programming problem representation in novice and expert programmers”, International Journal of Man-Machine Studies, vol. 19, no. 4, pp. 391–398, 1983, doi:https://doi.org/10.1016/S0020- 7373(83)80061-3.
- C. M. Zeitz, “Expert-novice differences in memory, abstraction, and reasoning in the domain of literature”, Cognition and Instruction, vol. 12, no. 4, pp. 277–312, 1994.
- M. T. Chi, P. J. Feltovich, R. Glaser, “Categorization and representation of physics problems by experts and novices”, Cognitive Science, vol. 5, no. 2, pp. 121–152, 1981.
- L. E. Winslow, “Programming pedagogy—a psychological overview”, SIGCSE Bulletin, vol. 28, no. 3, p. 17–22, 1996, doi:10.1145/234867.234872.
- A. Robins, J. Rountree, N. Rountree, “Learning and teaching program- ming: A review and discussion”, Computer science education, vol. 13, no. 2, pp. 137–172, 2003.
- B. Xie, D. Loksa, G. L. Nelson, M. J. Davidson, D. Dong, H. Kwik, A. H. Tan, L. Hwa, M. Li, A. J. Ko, “A theory of instruction for introductory programming skills”, Computer Science Education, vol. 29, no. 2-3, pp. 205–253, 2019, doi:10.1080/08993408.2019.1565235.
- C. Wilcox, “Testing strategies for the automated grading of stu- dent programs”, “Proceedings of the 47th ACM Technical Sympo- sium on Computing Science Education”, SIGCSE ’16, p. 437–442, Association for Computing Machinery, New York, NY, USA, 2016, doi:10.1145/2839509.2844616.
- C. Benac Earle, L.-r. Fredlund, J. Hughes, “Automatic grading of pro- gramming exercises using property-based testing”, “Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education”, ITiCSE ’16, p. 47–52, Association for Computing Machinery, New York, NY, USA, 2016, doi:10.1145/2899415.2899443.
- P. Runeson, “A survey of unit testing practices”, IEEE Software, vol. 23, 2006, doi:10.1109/MS.2006.91.
- B. Wanjiru, P. v. Bommel, D. Hiemstra, “Towards a generic model for classifying software into correctness levels and its application to SQL”, “2023 IEEE/ACM 5th International Workshop on Software Engineering Education for the Next Generation (SEENG)”, pp. 37–40, 2023, doi:10.1109/SEENG59157.2023.00012.
- B. Wanjiru, P. v. Bommel, D. Hiemstra, “Sensitivity of automated SQL grading in computer science courses”, “Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24)”, 2024.
- C. Kleiner, C. Tebbe, F. Heine, “Automated grading and tutoring of sql statements to improve student learning”, “Proceedings of the 13th Koli Calling International Conference on Computing Education Research”, Koli Calling ’13, p. 161–168, Association for Computing Machinery, New York, NY, USA, 2013, doi:10.1145/2526968.2526986.
- B. Chandra, B. Chawda, B. Kar, K. V. M. Reddy, S. Shah, S. Sudarshan, “Data generation for testing and grading sql queries”, The VLDB Journal, vol. 24, no. 6, p. 731–755, 2015, doi:10.1007/s00778-015-0395-0.
- S. Chaudhuri, “An overview of query optimization in relational systems”, “Proceedings of the Seventeenth ACM SIGACT-SIGMOD- SIGART Symposium on Principles of Database Systems”, PODS ’98, p. 34–43, Association for Computing Machinery, New York, NY, USA, 1998, doi:10.1145/275487.275492.
- K. Ala-Mutka, T. Uimonen, H.-M. Järvinen, “Supporting students in C++ programming courses with automatic program style assessment”, JITE, vol. 3, pp. 245–262, 2004, doi:10.28945/300.
- F. G. Wilkie, B. Hylands, “Measuring complexity in C++ application software”, Software: Practice and Experience, vol. 28, 1998.
- N. R. Tallent, J. M. Mellor-Crummey, “Effective performance mea- surement and analysis of multithreaded applications”, PPoPP ’09, p. 229–240, Association for Computing Machinery, New York, NY, USA, 2009, doi:10.1145/1504176.1504210.
- M. Raasveldt, H. Mühleisen, “Duckdb: an embeddable analyt- ical database”, “Proceedings of the 2019 International Confer- ence on Management of Data”, SIGMOD ’19, p. 1981–1984, As- sociation for Computing Machinery, New York, NY, USA, 2019, doi:10.1145/3299869.3320212.
- T. pganalyze Developer Team, “libpg_query”, 2023, version 15-4.2.1.
- L. Yujian, L. Bo, “A normalized levenshtein distance metric”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1091–1095, 2007, doi:10.1109/TPAMI.2007.1078.
- K. Zhang, D. Shasha, “Simple fast algorithms for the editing distance between trees and related problems”, SIAM J. Comput., vol. 18, pp. 1245–1262, 1989, doi:10.1137/0218082.
Cited By
Metrics
