Packet Tracer as an Educational Serious Gaming Platform
Bibliographic record
Abstract
Serious gaming is quickly becoming an important trend in education, as it provides an interactive as well as enjoyable environment for learning various technology, business and scientific topics. The main objective of this research is to explore the possibility of using Cisco packet tracer's multiuser capability to develop several interactive, multiuser games for teaching networking topics and assessing students skills in the class. We present a list of primary principles for design of such games, and design two multiuser educational games as in-class activities to showcase the benefits of our approach. We demonstrate how students' skills in important network topics such as routing, remote access and security help them succeed in these games. We also provide test results to evaluate the performance of packet tracer in handling multiuser traffic in this scenario. Institute of Technology (UOIT) in Ontario, Canada. UOIT has an undergraduate program in networking and IT security, and also serves as a regional Cisco Network Academy. With growing interest in the networking field and the hands-on nature of the topic, the objective of this project was to make the classroom lecture hours more interactive. The research team began by analyzing and utilizing the tools available through the Cisco Networking Academy. Cisco Packet Tracer's is most commonly used as an emulation platform for Cisco networking devices and IOS network operating system. The new version includes multiuser capabilities that enable designers to create large group based interactions during classroom hours. The Packet Tracer includes many necessary elements needed for CCNA-level education. The research team explored the area of using packet tracer as an educational serious gaming platform by developing two gaming activities, Domination & Relay Race. Domination & Relay Race utilize the multiuser capabilities of packet tracer to the greatest extent. The two games provide students with a great educational value and help them hone their networking expertise. Packet Tracer provides a great interface to gain experience with practical configuration scenarios. Our aim was to combine the powerful capabilities of packet tracer with serious gaming in order to provide a simple and entertaining environment for teaching basic and advanced networking topics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".