pTA: A Programmable Teaching Assistant for Lab Courses
Bibliographic record
Abstract
Lab courses play a crucial role in enabling students to gain a deeper understanding of theoretical concepts, but these courses require a significant effort from the course's organizational staff, such as instructors and teaching assistants. To address this challenge, we developed pTA, an acronym for programmable teaching assistant, which automates the functional evaluation of students' submissions for the Cloud Databases course taught at the Technical University of Munich (TUM). pTA reduces the staff workload and provides instant feedback to students, thereby enhancing their understanding of the project specifications. Additionally, pTA includes a live leaderboard that provides a gamification element that makes the course more interactive and engaging for students. It is deployed on a Kubernetes cluster that ensures scalability with evaluation requests. In this paper, we describe the course's learning milestones and provide an overview of pTA's architecture and features. The system's efficacy was evaluated at TUM and the University of Toronto, where it was deployed in two similar courses. Our findings show that pTA reduced the staff workload by at least 75%, lowered the operating cost, and increased course capacity in terms of the number of students. Furthermore, our study suggests that students exhibit more interest in courses that integrate interactive learning systems and gamification elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".