This thesis aims to explore the realm of Natural Language Processing (NLP) in the context of automated grading systems, focusing specifically on identifying and analyzing frequent learner mistakes. With the increasing integration of technology in education, automated grading systems have gained prominence, but they often lack nuanced understanding and feedback provision. This research endeavors to enhance the efficacy of such a system in use at the institute by employing NLP techniques to dissect learner errors and provide more tailored feedback.
Thesis Type |
|
Status |
Open |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Michal Slupczynski Laurenz Neumann |
Contact |
slupczynski@dbis.rwth-aachen.de laurenz.neumann@dbis.rwth-aachen.de |
Automated grading systems have revolutionized the educational landscape by offering scalable assessment solutions. However, these systems often struggle to provide meaningful feedback beyond simple correctness assessments. Understanding common learner mistakes is crucial for refining these systems to offer more personalized and effective feedback.
Prior research has delved into NLP applications in educational settings, such as essay grading and language learning. However, the focus on systematically analyzing frequent learner mistakes within automated grading tasks in programming tasks remains relatively unexplored.
If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to slupczynski@dbis.rwth-aachen.de and laurenz.neumann@dbis.rwth-aachen.de.
Please apply with a meaningful CV and a recent transcript of your academic performance.
- Must: Python
- Nice to have: Understanding of machine learning and NLP concepts
- Optional: Familiarity with educational assessment methodologies