Frequent Learner Errors: NLP Insights into Automated Grading

February 29th, 2024

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
  • Bachelor
Leon Harks
Presentation room
Seminar room I5 6202
Stefan Decker
Michal Slupczynski
Laurenz Neumann

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.

  • Must: Python
  • Nice to have: Understanding of machine learning and NLP concepts
  • Optional: Familiarity with educational assessment methodologies