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 grainsize, 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.
In a programming lab in REF , the students' average frustration in computer programming exercises across different labs is detected based on information from compiler data, such as the average time between compilations, and consecutive pairs of same error.
Coarse-grained detection of student frustration in an introductory programming course
ICER '09
Computer Science