Investigating the effectiveness of a HyFlex cyber security training in a developing country: A case study
Bibliographic record
Abstract
HyFlex termed as hybrid-flexibility is a teaching approach where teachers and students have the alternative to participate in planned courses either remotely or face-to-face. This study examines the effectiveness of the HyFlex pedagogical method to teach highly interactive digital and face-to-face cyber security training in Nigeria amidst the pandemic. Data was collected using a survey questionnaire from 113 participants to evaluate student's perception towards the effectiveness of the Hyflex method using physical and Zoom teleconferencing which allow students to participate remotely in the cyber security training. The developed questionnaire comprising both open-ended and Likert-style questions was administered to purposely sampled participants. Findings from this study presents implementation details on how the HyFlex teaching model was implemented from a developing country context. Besides, findings present challenges and opportunities experienced with adopting the HyFlex pedagogical model, and also offers recommendations to other instructors for employing this teaching model. Findings also reveal that although there were challenges experienced by the students who attended via online such as connectivity issues, competency in using some features in Zoom-conferencing, etc. The students did appreciate the flexibility HyFlex teaching afforded, indicating that HyFlex is a promising teaching approach for fostering engagement of students especially in large-group cyber security courses.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".