Leveraging large language models to construct feedback from medical multiple-choice Questions.

Exams like the formative Progress Test Medizin can enhance their effectiveness by offering feedback beyond numerical scores. Content-based feedback, which encompasses relevant information from exam questions, can be valuable for students by offering them insight into their performance on the current exam, as well as serving as study aids and tools for revision. Our goal was to utilize Large Language Models (LLMs) in preparing content-based feedback for the Progress Test Medizin and evaluate their effectiveness in this task.

Discovering unknown response patterns in progress test data to improve the estimation of student performance.

Background: The Progress Test Medizin (PTM) is a 200-question formative test that is administered to approximately 11,000 students at medical universities (Germany, Austria, Switzerland) each term. Students receive feedback on their knowledge (development) mostly in comparison to their own cohort. In this study, we use the data of the PTM to find groups with similar response patterns.

SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks.

Stakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing the need for this knowledge is an ideal text-to-SQL benchmark problem, a field where public datasets are scarce and needed.