Abstract
Over the last few years, computer science class sizes have increased, resulting in tutors providing more support to struggling students, and instructors having less time per-student in larger classes.
Universities typically assign multiple tutors to lab sessions, especially introductory programming courses, to maximise the help available to students during their sessions.
However, using multiple tutors does not help struggling students outside of official sessions.
The lack of support outside official settings is especially the case for online courses and remote learning.
To help resolve student frustration from not being able to get support when they need it, we propose a tool that can detect when a student is struggling with their programming task and give them a hint that gets them closer to their goal.
Universities typically assign multiple tutors to lab sessions, especially introductory programming courses, to maximise the help available to students during their sessions.
However, using multiple tutors does not help struggling students outside of official sessions.
The lack of support outside official settings is especially the case for online courses and remote learning.
To help resolve student frustration from not being able to get support when they need it, we propose a tool that can detect when a student is struggling with their programming task and give them a hint that gets them closer to their goal.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th International Conference on Educational Data Mining |
| Publication status | Published - 22 Jul 2022 |
Keywords
- computer science education
- computer programs
- sequence mining
- learning behaviours
- feedback
