Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success?
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914431768723456 |
|---|---|
| author | Borchers, Conrad Švábenský, Valdemar Kafle, Sandesh K. Tang, Kevin K. Vykopal, Jan |
| author_facet | Borchers, Conrad Švábenský, Valdemar Kafle, Sandesh K. Tang, Kevin K. Vykopal, Jan |
| contents | Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale. We examine alignment in cybersecurity simulations using multimodal traces from 23 teams (76 students) across five exercise sessions. Study 1 codes objectives and team emails with Bloom's taxonomy and models the completion of key exercise tasks with generalized linear mixed models. Alignment, defined as the discrepancy between required and enacted Bloom levels, predicts success, whereas the Bloom category alone does not predict success once discrepancy is considered. Study 2 compares predictive feature families using grouped cross-validation and l1-regularized logistic regression. Text embeddings and log features outperform Bloom-only models (AUC~0.74 and 0.71 vs. 0.55), and their combination performs best (Test AUC~0.80), with Bloom frequencies adding little. Overall, the work offers a measure of alignment for simulations and shows that multimodal traces best forecast performance, while alignment provides interpretable diagnostic insight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28553 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success? Borchers, Conrad Švábenský, Valdemar Kafle, Sandesh K. Tang, Kevin K. Vykopal, Jan Human-Computer Interaction Computers and Society Machine Learning Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale. We examine alignment in cybersecurity simulations using multimodal traces from 23 teams (76 students) across five exercise sessions. Study 1 codes objectives and team emails with Bloom's taxonomy and models the completion of key exercise tasks with generalized linear mixed models. Alignment, defined as the discrepancy between required and enacted Bloom levels, predicts success, whereas the Bloom category alone does not predict success once discrepancy is considered. Study 2 compares predictive feature families using grouped cross-validation and l1-regularized logistic regression. Text embeddings and log features outperform Bloom-only models (AUC~0.74 and 0.71 vs. 0.55), and their combination performs best (Test AUC~0.80), with Bloom frequencies adding little. Overall, the work offers a measure of alignment for simulations and shows that multimodal traces best forecast performance, while alignment provides interpretable diagnostic insight. |
| title | Multimodal Analytics of Cybersecurity Crisis Preparation Exercises: What Predicts Success? |
| topic | Human-Computer Interaction Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2603.28553 |