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- W2100120627 abstract "We attempt to automatically detect student frustration, at a coarse-grained level, using measures distilled from student behavior within a learning environment for introductory programming. We find that each student's average level of frustration across five lab exercises can be detected based on the number of pairs of consecutive compilations with the same edit location, the number of pairs of consecutive compilations with the same error, the average time between compilations and the total number of errors. Attempts to detect frustration at a finer grain-size, identifying individual students' fluctuations in frustration between labs, were less successful. These results indicate that it is possible to detect frustration at a coarse-grained level, solely from coarse-grained data about students' behavior within a learning environment." @default.
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- W2100120627 date "2009-01-01" @default.
- W2100120627 modified "2023-10-17" @default.
- W2100120627 title "Coarse-grained detection of student frustration in an introductory programming course" @default.
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- W2100120627 doi "https://doi.org/10.1145/1584322.1584332" @default.
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