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A High School Camp on Algorithms and Coding in Jamaica

Daniel T. Fokum, Zaria Chen Shui, Kerene Wright, Orr Paradise, Gunjan Mansingh, Daniel Coore

TL;DR

JamCoders documents a four-week residential algorithm and coding camp in Jamaica designed to broaden Computing access among demographically diverse high school students. The study combines a rich camp design with quantitative quiz data (two graded assessments) and a post-camp survey to examine gender effects, urban-rural differences, STEM-club influence, and shifts in attitudes toward Computing. Key findings show significant gender-based improvements favoring female students, rural students’ larger gains over urban peers, and a net increase in Computing-interest post-camp, supported by the Big Siblings mentorship model. The work highlights the value of small-class mentorship and diverse recruitment for accelerating exposure to advanced CS topics, while also noting limitations in measurement and data collection that suggest careful interpretation and opportunities for replication in similar settings.

Abstract

This is a report on JamCoders, a four-week long computer-science camp for high school students in Jamaica. The camp teaches college-level coding and algorithms, and targets academically excellent students in grades 9--11 (ages 14--17). Qualitative assessment shows that the camp was, in general terms, a success. We reflect on the background and academic structure of the camp and share key takeaways on designing and operating a successful camp. We analyze data collected before, during and after the camp and map the effects of demographic differences on student performance in camp. We conclude with a discussion on possible improvements on our approach.

A High School Camp on Algorithms and Coding in Jamaica

TL;DR

JamCoders documents a four-week residential algorithm and coding camp in Jamaica designed to broaden Computing access among demographically diverse high school students. The study combines a rich camp design with quantitative quiz data (two graded assessments) and a post-camp survey to examine gender effects, urban-rural differences, STEM-club influence, and shifts in attitudes toward Computing. Key findings show significant gender-based improvements favoring female students, rural students’ larger gains over urban peers, and a net increase in Computing-interest post-camp, supported by the Big Siblings mentorship model. The work highlights the value of small-class mentorship and diverse recruitment for accelerating exposure to advanced CS topics, while also noting limitations in measurement and data collection that suggest careful interpretation and opportunities for replication in similar settings.

Abstract

This is a report on JamCoders, a four-week long computer-science camp for high school students in Jamaica. The camp teaches college-level coding and algorithms, and targets academically excellent students in grades 9--11 (ages 14--17). Qualitative assessment shows that the camp was, in general terms, a success. We reflect on the background and academic structure of the camp and share key takeaways on designing and operating a successful camp. We analyze data collected before, during and after the camp and map the effects of demographic differences on student performance in camp. We conclude with a discussion on possible improvements on our approach.
Paper Structure (31 sections, 6 figures)

This paper contains 31 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Outline
  3. Related Work
  4. Study Design
  5. Camp Overview
  6. Camp Staff
  7. Student Recruitment and Admission
  8. Syllabus
  9. Lectures and Labs
  10. Quizzes and Study Halls
  11. Big Siblings
  12. Academic Staff Meetings
  13. Research Questions
  14. Data collection
  15. Results
  16. ...and 16 more sections

Figures (6)

  • Figure 1: List of variables and summary statistics. Mean and median is reported for float type variables, and mode reported for categorical variables. Performance improvement is defined as the score on Quiz 2 minus the score on Quiz 1.
  • Figure 2: Boxplot showing quiz 1 scores by gender
  • Figure 3: Boxplot showing improvement in scores from Quiz 1 to Quiz 2 by gender
  • Figure 4: Boxplot showing performance improvement from Quiz 1 to Quiz 2 by high school location
  • Figure 5: Boxplot showing Quiz 1 scores by school rank
  • ...and 1 more figures