Assessment of Security Knowledge, Skills and Abilities using Hands-On Exercises in 2016 (Abstract Only)
Bibliographic record
Abstract
We see teaching cybersecurity through hands-on, interactive exercises as a way to engage students. However, we also want to assess how much students are learning from these exercises, and the exercises themselves could be used to assess what students know. Creating new hands-on exercises requires significant preparation on the part of the instructor. As a community we have begun to share exercises and discuss what works and what problems students and instructors have encountered. The purpose of this BOF is two-fold: 1) to continue to bring together instructors who have developed hands-on exercises with those who would like to use them, and 2) extend the discussion to include assessment of student learning. We recognize that few CS programs can afford new required courses, so we will discuss ways to integrate security-related exercises into existing ones. This could include networking, OS, computer architecture, programming languages, software engineering, algorithms and programming (CS0, CS1, CS2). The questions we will ask are, "What exercises have you tried? What are your experiences? What are you looking for? What are the learning goals for your students? How do you assess them?"
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".