Abstract
The assessment of SQL queries is a time-consuming task for the teacher, as each query needs customized feedback. Automation of such a task can prove beneficial for students as well as teachers. Some of the semi-automated evaluation tools for SQL queries are reported in the literature though none of them provides Quantitative as well as Qualitative feedback. All the evaluation tools available for SQL queries provide a binary type of feedback, which results in the query being right or wrong. However, evaluation could be more meaningful if customized self-explanatory feedback is provided to the student stating the level of correctness of the query along with the description of the mistake committed (if any). Authors have developed “An Automated Assessment tool for SQL Queries: Eval_SQL” which provides the marks even for partially correct query (Quantitative) and the feedback on what went wrong in the query (Qualitative). This can improve the student’s learning experience in the virtual world. Eval_SQL also helps to reduce teacher workload, allowing them to focus more on learning-centric tasks.
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Shah, B., Pareek, J. (2022). Automated Evaluation of SQL Queries: Eval_SQL. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_9
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DOI: https://doi.org/10.1007/978-981-16-6616-2_9
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