Page 239 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Ø Personalization Accuracy:
Evaluation of the AI's ability to adapt to individual learning styles, paces, and performance metrics.
Analysis of learner satisfaction with tailored content and recommendations.
Ø Skill Development:
Performance in simulated cybersecurity challenges and real-world scenarios.
Improvement in critical thinking, problem-solving, and teamwork skills.
Ø System Scalability and Accessibility:
Ability to handle a growing number of users without performance degradation.
Feedback from learners in remote or underrepresented areas on platform accessibility and usability.
2. Experimental Setup
Ø Participant Groups:
A control group using traditional learning methods.
An experimental group using the AI-powered eLearning platform.

Ø Data Collection:
Pre- and post-tests for both groups to evaluate knowledge and skills.
Surveys, interviews, and focus groups for qualitative feedback.

Platform analytics to track engagement, progress, and system usage.
Ø Duration:
A multi-week pilot study involving cybersecurity students and professionals.
3. Evaluation Phases
Ø Phase 1: Usability Testing
Initial testing to identify and resolve any usability issues with the platform.
Metrics: User interface intuitiveness, ease of navigation, and overall user experience.

Ø Phase 2: Pilot Implementation
Conduct a small-scale implementation with a defined group of participants.
Metrics: Effectiveness of personalized learning paths, quality of virtual labs, and relevance of content.

Ø Phase 3: Large-Scale Testing
Expand the implementation to a larger audience, including learners from diverse backgrounds.
Metrics: Scalability, accessibility, and consistency in delivering high-quality learning experiences.

Fig.3 Performance Evaluation

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