Cyber Discovery evaluation : published 13 August 2021
Notice bibliographique
Résumé
The government's National Cyber Security Strategy 2016 set aside 1.9 billion to drive forward the UK cyber security agenda and ensure that the UK is secure and resilient to cyber threats Among other initiatives, it recognised the importance of identifying and training young people to enter the cyber security profession and pledged to develop a "selfstanding skills strategy that builds on existing work to integrate cyber security into the education system" Participation 23,636 students registered for Cyber Discovery in Year One, growing to 27,903 in Year Two and 35,941 students in Year Three.This dropped slightly to 28,232 in Year Four, perhaps due to the impact of school closures during the COVID-19 pandemicThe proportion of girls registered for Assess reached a third in Years Three (32%) and Four (33%), compared to a quarter (25%) in Year Two and about a fifth (21%) in Year One.The increase in female participants is largely due to the programme being expanded to 13-year-olds, where a higher rate of female participants was driven by targeted marketing campaigns Cyber Discovery engaged a greater proportion of ethnic minority participants than sit computer science school exams Participation tended to be highest in the less deprived areas and in the South of England EngagementThose taking part tended to do so as they felt it would be enjoyable and useful.Improving skills was important, with both qualitative and quantitative evidence showing that career consideration was not a major motivating factor In the first three years, 590 students took part in the Elite phase and completed SANS professional level 6-day training courses in Years Two and Three.89 Elite participants achieved GIAC certification in Year Two (93% pass rate) and 124 in Year Three (89% pass rate) Participants reported that their experiences of the programme were strengthened by being part of a group, having access to support and guidance, and the extension of the programme during the pandemic.Demands on students' time, particularly for those sitting exams, were a barrier.During school closures, a minor theme from qualitative research was that access to a suitable computer and internet connection was an additional barrier Club Leaders and students found the platform engaging, user-friendly and intuitive.They found the level of challenge appropriate and often welcomed being pushed.A minor theme was that it was sometimes too challenging if students did not have the required soft skills and interest in cyber security Cyber careersStudents generally had a very positive view of cyber careers, with most agreeing that they were important for society (94%), suitable for someone like them (80%), and open to anyone regardless of background (66%) Students were more likely to get information on cyber security careers from Cyber Discovery than from other sources, and after taking part most felt they understood the requirements for pursuing a cyber career Survey data suggested that the programme increased skills in computer science and cyber security.There was no evidence that it increased interest in cyber security as a study subject or as a career -this may be due to study methodology or increased knowledge leading some participants to realise that they did not wish to pursue cyber further.Equally, it may reflect the initial objective of the programme which was to engage an elite cohort of most talented young people, rather than appeal to a wider audience Additional outcomesFor industry experts, outcomes centred around the potential for recruitment, including the opportunity to meet talented students and raise awareness of cyber security roles Club Leaders and industry experts generally felt it was possible that the programme could contribute towards longer-term impact and closing the cyber security skills gap Club Leaders were split as to whether Cyber Discovery needed to be linked to the curriculum.A common theme was that content enhanced the curriculum and met the needs of students seeking to broaden their knowledge and skills 10 -least deprived 14% 14%Source: Management Information.Analysis of school postcodes shows that in terms of levels of deprivation, Cyber Discovery engaged similar schools in Years Three and Four.In both years, schools were
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».