# Assessment in Team Problem-Solving Exercises in Computing Education

arXiv: https://arxiv.org/abs/2607.19209 · HTML: https://mlsanity.com/paper/2607.19209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMs](https://mlsanity.com/paper/2401.06431/) (2024)
- [Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring](https://mlsanity.com/paper/2411.16337/) (2024)
- [Heterogenous Network Analytics of Small Group Teamwork: Using Multimodal Data to Uncover Individual Behavioral Engagement Strategies](https://mlsanity.com/paper/2312.08786/) (2023)
- [Research and Practice of Delivering Tabletop Exercises](https://mlsanity.com/paper/2404.10206/) (2024)
- [From Paper to Platform: Evolution of a Novel Learning Environment for Tabletop Exercises](https://mlsanity.com/paper/2404.10988/) (2024)
- [Self-regulated Learning Processes in Secondary Education: A Network Analysis of Trace-based Measures](https://mlsanity.com/paper/2412.08921/) (2024)
- [Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training](https://mlsanity.com/paper/2501.06658/) (2025)
- [Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering](https://mlsanity.com/paper/2307.10260/) (2023)
- [Using LLMs for Tabletop Exercises within the Security Domain](https://mlsanity.com/paper/2403.01626/) (2024)

## Abstract (arXiv)

This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents. While assessment is essential for determining how well teams achieve learning objectives, the complex, open-ended nature of TTXs often leads to delayed or incomplete feedback. TTX learning platforms can record teams' actions and communication; yet, leveraging these data to assess performance is underexplored. To address this gap, we compared two post-TTX team assessment methods -- clustering and large language models (LLMs) -- using an original dataset from 81 participants across two countries. We evaluated these methods against instructor-assigned scores based on standardized rubrics. Clustering grouped teams that approached TTX tasks similarly, enabling instructors to deliver faster, targeted feedback to teams within a cluster. This method was valid and reliable, with low computational requirements. LLMs used the standardized rubrics to assess teams' communication. While GPT-4o frequently disagreed with instructor scores, GPT-5.2 demonstrated considerably lower error. The researched methods have been integrated into INJECT, an open-source TTX learning platform, to support scalability and teaching practice. To encourage community adoption, we publicly share all datasets, software tools, and a full-fledged TTX scenario.