SayPro Course Performance DataMonthly February Education Technology Literacy Courses Report and UpdatePrepared by: Chancellor SCHARDate: March 14, 2025.

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Introduction

This section of the Monthly Report focuses on the performance metrics for the February 2025 cohort of SayPro’s Education Technology Literacy courses. It provides an in-depth summary of learner scores, engagement levels, and overall performance across the various courses offered. These insights will help assess whether learners are meeting the expected learning outcomes, identify any trends in engagement, and highlight areas for improvement.

1. Learner Scores and Assessment Performance

A. Overall Learner Scores:

The average score for all courses in the February 2025 cohort is 82%, reflecting strong learner performance across the board.
- This is a slight increase from the 80% average score in the January cohort, indicating improved mastery of course material due to refinements in course delivery and structure.
Top Scoring Learners: Out of the 1,200 learners, approximately 150 scored above 95% in their respective courses, demonstrating a high level of proficiency in the material.
Lowest Scoring Learners: The bottom 10% of learners scored below 55%, highlighting potential challenges in areas such as content comprehension, time management, or technical issues. Targeted interventions will be necessary to support these learners in future cohorts.

B. Course-Specific Performance:

The following table provides a breakdown of average learner scores for each course offered in February:

Course	Average Score	Highest Score	Lowest Score	Score Range
AI and Machine Learning	86%	98%	52%	46% – 98%
Cybersecurity and Data Protection	84%	95%	55%	50% – 95%
Cloud Computing and Big Data	80%	94%	45%	45% – 94%
Blockchain and Emerging Technologies	83%	97%	50%	50% – 97%
IoT and Smart Technologies	78%	91%	40%	40% – 91%

Insights:

AI and Machine Learning had the highest average score at 86%, with 98% being the highest individual score, indicating strong overall understanding of complex concepts, especially in the Data Science and ML algorithms areas.
Cybersecurity and Data Protection also performed well with an 84% average score, indicating that learners are generally able to apply security principles and practices to real-world scenarios.
Cloud Computing and Big Data had the lowest average score at 80%, suggesting that while learners grasp core concepts, they may be struggling with more advanced topics like distributed systems and data processing techniques.
Blockchain courses demonstrated an 83% average score, showing good engagement, but still revealing that a significant portion of students need more support in understanding the complexities of decentralized applications and smart contracts.
IoT and Smart Technologies had the lowest average score at 78%, with the widest score range. The low performance suggests that either the material might not be sufficiently engaging or that some learners find it challenging to connect theoretical knowledge with practical application.

2. Learner Engagement Metrics

A. Engagement Overview:

Engagement metrics are essential to understanding how actively learners are participating in the course activities, including interacting with course materials, completing assignments, and participating in discussions. The following engagement metrics reflect the overall involvement of students across the February 2025 cohort.

Overall Engagement Rate: 88% of learners consistently participated in course activities, including attending live sessions, completing quizzes, and engaging in forum discussions.
- This is an increase from the 85% engagement rate observed in the January cohort, showing that learners are becoming more involved in course offerings over time.
Active Learners: 65% of learners interacted with peers or instructors at least once per week, which is an important indicator of engagement. These learners participated in discussion boards, group projects, or asked questions during live sessions.

B. Course-Specific Engagement:

The following table provides a breakdown of engagement rates for each course:

Course	Overall Engagement Rate	Active Learners	Learners Participating in Discussions	Live Session Attendance
AI and Machine Learning	90%	72%	80%	85%
Cybersecurity and Data Protection	87%	69%	76%	80%
Cloud Computing and Big Data	84%	65%	71%	75%
Blockchain and Emerging Technologies	85%	68%	70%	78%
IoT and Smart Technologies	80%	60%	65%	70%

Insights:

AI and Machine Learning had the highest engagement rate at 90%, with 85% of learners attending live sessions. This reflects high interest and commitment to the subject matter.
Cybersecurity and Data Protection had strong active learner participation at 69%, though engagement levels dipped slightly compared to AI. Efforts to increase engagement in discussion boards and live sessions could further enhance learning outcomes.
Cloud Computing and Big Data saw a lower engagement rate of 84%, with the lowest active learners rate at 65%. This suggests that learners may require more direct interaction or motivation, such as increased opportunities for hands-on practice.
Blockchain and Emerging Technologies had strong engagement numbers, especially in live session attendance, suggesting that learners are highly motivated when given access to real-time support and expert guidance.
IoT and Smart Technologies had the lowest engagement rate at 80%, indicating that students may feel disconnected from the material, especially given its technical complexity. Increased interactive content and real-world applications could improve engagement.

3. Learner Performance Trends

A. High-Performing Learners:

Top 10% of Learners: Approximately 120 students (10% of total cohort) achieved scores of 90% or higher. These learners consistently performed well on quizzes, assignments, and exams and were highly engaged in course activities. This group tends to have strong foundational knowledge and is motivated to complete additional coursework beyond the required assignments.
- Key Factors for High Performance:
  - Strong prior knowledge of subject matter
  - Active participation in discussions and live sessions
  - Early completion of assignments and proactive engagement with instructors

B. Struggling Learners:

Bottom 10% of Learners: Approximately 120 students scored below 55% in their courses. These learners faced challenges, including difficulty in understanding course content, falling behind on assignments, or struggling to apply theoretical concepts in practical contexts.
- Key Factors for Struggling Learners:
  - Lack of engagement in discussions or live sessions
  - Delay in assignment submissions or incomplete coursework
  - Struggled with technical concepts and complex topics such as cloud architectures or blockchain mechanics

4. Conclusion and Recommendations

The performance data for the February 2025 cohort indicates that while learners are generally performing well, there are areas for improvement, particularly in engagement and support for struggling learners.

Key takeaways:

High Completion and Performance: The 85% completion rate and 82% average score reflect strong overall success, especially in AI and Machine Learning and Cybersecurity courses.
Engagement Variability: While AI and Machine Learning saw the highest engagement, courses like IoT and Cloud Computing had lower levels of participation and engagement. Further efforts to create more engaging, hands-on learning experiences are needed.
Support for Struggling Learners: Targeted support, such as additional tutoring, study groups, or mentorship, should be provided to learners who are scoring below 55% to improve their outcomes.

To ensure continued success, SayPro should consider:

Improving engagement through interactive, real-world content and practical exercises, especially in technical courses.
Personalized learning paths and additional support systems for learners who are struggling with course content.
Leveraging learner analytics to provide timely interventions and ensure that no student falls behind.

Report submitted by:
Chancellor SCHAR
March 14, 2025

SayPro Course Performance DataMonthly February Education Technology Literacy Courses Report and UpdatePrepared by: Chancellor SCHARDate: March 14, 2025.

Introduction

1. Learner Scores and Assessment Performance

A. Overall Learner Scores:

B. Course-Specific Performance:

2. Learner Engagement Metrics

A. Engagement Overview:

B. Course-Specific Engagement:

3. Learner Performance Trends

A. High-Performing Learners:

B. Struggling Learners:

4. Conclusion and Recommendations

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