Analyzing Student SQL Solutions via Hierarchical Clustering and Sequence Alignment Scores

Yang, Sophia; Herman, Geoffrey L.; Alawini, Abdussalam

doi:10.1145/3531072.3535319

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2023

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Cited by 6 publications

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“…It involves checking whether a student's query has the same meaning as the instructor's reference query. With semantic analysis we can also identify different answering patterns and approaches [20]. This can help to identify difficult SQL areas so that we can improve the quality of teaching to help the students learn better.…”

Section: The Choice For Specific Propertiesmentioning

confidence: 99%

Towards a Generic Model for Classifying Software into Correctness Levels and its Application to SQL

Wanjiru,

Bommel,

Hiemstra

2023

2023 IEEE/ACM 5th International Workshop on Software Engineering Education for the Next Generation (SEENG)

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Automated grading systems can save a lot of time when carrying out grading of software exercises. In this paper we present our ongoing work on a generic model for generating software correctness levels. These correctness levels enable partial grading of students' software exercises. The generic model can be used as a foundation for building SQL grading systems that check for correctness of SQL queries and can be generalized to different programming languages.

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Section: The Choice For Specific Propertiesmentioning

confidence: 99%