Lesodia
  • Lesodia
  • I. Introduction
  • II. Method
  • III. Results
  • IV. Discussion
  • V. Conclusion
  • VI. Recommendation
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IV. Discussion

PreviousIII. ResultsNextV. Conclusion

Last updated 20 days ago

The deployment of Lesodia over a one-month pilot period from March 15, 2025, to April 15, 2025, showed several key benefits based on the collected data.

  1. Operational Efficiency and Tutor Load Management: Lesodia successfully maintained its 1:10 tutor-to-learner ratio, with Kim Sumagang having 2 students and Melissa Bautista with none. This indicates effective load management, ensuring personalized learning. Going forward, optimizing tutor assignments using data-driven strategies could improve resource allocation, especially during peak times.

  2. Cost Efficiency and Accessibility: The platform exceeded its goal of reducing tutoring costs by 20%, achieving an average savings of 33.3%, as shown in the payment data. This reduction makes tutoring more affordable while maintaining fair compensation for tutors. Future recommendations include exploring tiered pricing or additional pricing options to sustain affordability as demand grows.

  3. Academic Improvement and Documentation Strategy: The shared documentation approach showed an average student improvement of 41.7%, though the target of 50% was not fully met. This suggests potential for further refinement of the documentation-sharing system. Offering more personalized support could help more students reach their academic targets.

  4. Scalability and Future Enhancements: Lesodia’s platform is designed for growth. Future upgrades, such as AI-based student-tutor matching and real-time progress tracking, could further improve operational efficiency and learning outcomes. Ensuring that personalized attention and academic improvement remain central to future developments will be key to long-term success.

Figure 8: Payment Records

Figure 8 offers a concise overview of tutor compensation, rate ranges, and the savings achieved for each transaction. The consistent savings percentage of 33.3% across all entries reflects effective cost management and transparency within the platform’s pricing structure. To further improve the payment system, it may be beneficial to explore the integration of AI-powered analytics that could suggest optimal payment amounts based on factors such as market trends, tutor performance, and demand. Automated alerts for unusual payment patterns or discrepancies might also enhance financial oversight. Additionally, providing tutors and clients with personalized savings reports and projections could help highlight the advantages of the platform’s pricing model, potentially increasing engagement and satisfaction. These enhancements could support data-driven decision-making and foster greater trust among users.

Figure 9: Progress Overview

Figure 9 illustrates the academic gains of two students, Troy Montibon and Krist Sanchez, following their participation in tutoring sessions. Both students demonstrated notable improvement, with Troy Montibon achieving a 33.3% increase and Krist Sanchez reaching a 50% increase in their assessment scores. The average improvement across both students stands at 41.7%, indicating substantial progress. To further enhance the system, integrating AI-driven analytics is recommended. By leveraging artificial intelligence, the platform can analyze student performance data to identify learning gaps, predict areas where additional support may be needed, and recommend personalized resources or interventions for each learner. This approach would enable more targeted and effective tutoring, ultimately supporting improved academic outcomes for a wider range of students.

Figure 10: Student Assignment

Figure 10 presents the current distribution of students among tutors on the platform. To further enhance the system, it is recommended to implement AI-powered student-tutor matching, which can pair learners with tutors based on compatibility, subject expertise, and learning preferences. Additionally, introducing features such as automated notifications for available tutor slots and personalized recommendations for students could help optimize assignments and improve overall user satisfaction. These enhancements would support a more dynamic and efficient allocation of resources within the platform.