Enhancing Feedback Generation for Autograded SQL Statements to Improve Student Learning ...
Several tools to support autograding of student provided SQL statements have already been introduced. The full potential of such tools can only be leveraged, if they extend beyond grading efficiency by also providing tutoring capabilities to the students. With that, tools become really useful by off...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
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ACM
2024
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.25968/opus-3230 https://serwiss.bib.hs-hannover.de/3230 |
| Summary: | Several tools to support autograding of student provided SQL statements have already been introduced. The full potential of such tools can only be leveraged, if they extend beyond grading efficiency by also providing tutoring capabilities to the students. With that, tools become really useful by offering self-paced and individually timed learning experiences. In this paper we present an extension for an SQL autograder which improves the hints generated for students in cases where their solution is not entirely correct. Our approach is to compare the student’s solution with the model solution structurally to identify differences between the syntax trees describing the statements. This complements comparing the student’s query with a model solution based on query results. In addition to improving the quality of hints generated for the students, this concept can also be used easily for data manipulation language (DML) or data definition language (DDL) statements, thus extending the applicability of the ... |
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