Our Health Sciences Journey

A Collaborative Story of Five Students 🎓

Header Image
Header Image

1 💕 Our Story: How We Formed Our Study Group

“We began as incoming students in Fall 2021, seated in the back row of Introduction to Health Sciences. A simple request to borrow a pen evolved into collaborative study sessions, shared academic goals, and professional relationships that have supported us throughout our program.”

1.1 🌟 The Beginning

It started during orientation week in September 2021. Sakura needed course materials, Amara had extra supplies, Diego located the student resources, Mei Lin organized study schedules for the group, and Siriporn initiated our first group discussion. By the end of the first week, we had established a study group and committed to supporting each other’s academic success.

Our Commitment: Collaborative learning and mutual academic support throughout our program.

1.2 🎯 Our Professional Goals and Motivations

passions <- data.frame(
  Student = c("Sakura Tanaka", "Amara Okafor", "Diego Martinez", "Mei Lin Chen", "Siriporn Patel"),
  Career_Goal = c(
    "Pediatric Nurse - Providing compassionate care to young patients 👶",
    "Global Health Professional - Addressing health disparities internationally 🌍",
    "Emergency Medicine - Delivering critical care services 🚑",
    "Medical Research - Contributing to scientific advancement 🔬",
    "Community Health Specialist - Improving healthcare access 🏘️"
  ),
  Motivation = c(
    "Inspired by the need for quality pediatric healthcare in underserved areas",
    "Observed healthcare inequities in my community and internationally",
    "Witnessed the impact of emergency medical services firsthand",
    "Driven by the potential of scientific research to improve health outcomes",
    "Committed to reducing healthcare disparities in diverse communities"
  ),
  Daily_Inspiration = c(
    "Clinical case studies 💖",
    "Global health statistics 🗺️",
    "Emergency response protocols 🎖️",
    "Research literature 🔬",
    "Community health initiatives 🌈"
  )
)

passions %>%
  kable(caption = "Table: Professional Aspirations and Motivations 💓",
        col.names = c("Student", "Career Goal", "Motivation", "Focus Area")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = TRUE) %>%
  row_spec(0, bold = TRUE, color = "white", background = "#FF6B9D") %>%
  column_spec(1, bold = TRUE, background = "#FFF5E6", color = "#FF6B9D") %>%
  column_spec(2:4, background = "#FFFACD")
Table 1.1: Table 1.2: Table: Professional Aspirations and Motivations 💓
Student Career Goal Motivation Focus Area
Sakura Tanaka Pediatric Nurse - Providing compassionate care to young patients 👶 | nspired by the need for quality pediatric healthcare in underserved areas | linical case studies 💖 |
Amara Okafor Global Health Professional - Addressing health disparities internationally 🌍 | bserved healthcare inequities in my community and internationally | lobal health statistics 🗺️
Diego Martinez Emergency Medicine - Delivering critical care services 🚑 | itnessed the impact of emergency medical services firsthand | mergency response protocols 🎖️
Mei Lin Chen Medical Research - Contributing to scientific advancement 🔬 | riven by the potential of scientific research to improve health outcomes | esearch literature 🔬 |
Siriporn Patel Community Health Specialist - Improving healthcare access 🏘️ |Committed to reducing healthcare disparities in diverse communities |Community health initiatives 🌈

2 👥 Meet Our Study Group!

“Five students, five career paths, one collaborative approach to academic success! 💕”

2.1 Student Profiles

# Create our study group data
friends <- data.frame(
  Student_ID = c("HS2021-001", "HS2021-002", "HS2021-003", "HS2021-004", "HS2021-005"),
  Name = c("Sakura Tanaka", "Amara Okafor", "Diego Martinez", "Mei Lin Chen", "Siriporn Patel"),
  Age = c(21, 22, 21, 20, 22),
  Country = c("🇯🇵 Japan", "🇳🇬 Nigeria", "🇲🇽 Mexico", "🇨🇳 China", "🇹🇭 Thailand"),
  Program = c("Nursing", "Public Health", "Nursing", "Medical Lab Tech", "Public Health"),
  Year_Started = c(2021, 2021, 2021, 2021, 2021),
  Current_Year = c("Junior", "Junior", "Junior", "Junior", "Junior"),
  Current_GPA = c(3.78, 3.85, 3.65, 3.92, 3.71),
  Notable_Skill = c(
    "Clinical communication ☕",
    "Health policy analysis 📝",
    "Emergency response 🚑",
    "Laboratory techniques 🔬",
    "Community outreach 🧘"
  )
)

datatable(friends, 
          options = list(
            pageLength = 5,
            scrollX = TRUE,
            dom = 'tip'
          ),
          caption = "Table 1: Study Group Members - Class of 2025",
          class = 'cell-border stripe',
          rownames = FALSE) %>%
  formatStyle(
    'Current_GPA',
    background = styleColorBar(friends$Current_GPA, '#FFD93D'),
    backgroundSize = '100% 90%',
    backgroundRepeat = 'no-repeat',
    backgroundPosition = 'center'
  )

2.2 Study Group Statistics

group_stats <- data.frame(
  Metric = c(
    "Total Study Sessions",
    "Study Hours Logged ☕",
    "Intensive Study Sessions",
    "Group Projects Completed",
    "Average GPA",
    "Countries Represented",
    "Languages Spoken",
    "Group Meals Shared 🍕"
  ),
  Count = c(
    156,
    892,
    23,
    18,
    "3.78",
    5,
    8,
    347
  )
)

group_stats %>%
  kable(caption = "Table 2: Study Group Activities Summary",
        col.names = c("Activity Metric", "Count")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = FALSE,
                position = "center") %>%
  row_spec(0, bold = TRUE, color = "white", background = "#FF6B9D") %>%
  column_spec(1, bold = TRUE, width = "250px", background = "#FFF5E6") %>%
  column_spec(2, width = "120px", background = "#FFFACD")
Table 2.1: Table 2.2: Table 2: Study Group Activities Summary
Activity Metric Count
Total Study Sessions 156
Study Hours Logged ☕ | 92 |
Intensive Study Sessions 23
Group Projects Completed 18
Average GPA 3.78
Countries Represented 5
Languages Spoken 8
Group Meals Shared 🍕 | 47 |

3 💖 Notable Study Group Experiences

3.1 🌙 Intensive Study Session - Microbiology Final

Spring 2023

During preparation for our Microbiology final examination, our study group encountered difficulty with the concept of bacterial conjugation. We decided to create a physical demonstration of the process, which proved to be an effective learning technique. A campus facilities staff member observed our study method and provided refreshments. This collaborative approach resulted in strong performance on the examination for all group members.

Key Learning: Creative study methods can enhance comprehension and retention.

3.2 🎉 Peer Recognition Initiative

Each Semester

Our study group maintains a tradition of acknowledging individual academic achievements. When a member reaches a personal academic milestone, the group convenes to discuss each person’s contributions to our collective success. This practice reinforces our collaborative approach and promotes continued motivation.

Key Learning: Peer recognition strengthens group cohesion and individual motivation.

3.3 🌈 Extended Study Retreat

Fall 2023

Prior to our Clinical Practice Fundamentals examination, we organized a weekend study retreat. This allowed for focused preparation while also providing opportunities for group bonding through shared meals representing our diverse cultural backgrounds. The experience balanced intensive study with stress management.

Key Learning: Comprehensive preparation includes both academic focus and wellness activities.

4 📚 Shared Course History

4.1 Courses Completed Together

# Courses all five students took together
shared_courses <- data.frame(
  Semester = c(
    "Fall 2021", "Fall 2021", "Spring 2022", "Spring 2022",
    "Fall 2022", "Fall 2022", "Spring 2023", "Spring 2023",
    "Fall 2023", "Fall 2023", "Spring 2024", "Spring 2024"
  ),
  Course_Code = c(
    "HS101", "BIO121", "HS102", "CHEM131",
    "HS201", "STAT200", "HS202", "MICRO240",
    "HS301", "PHARM310", "HS302", "EPID320"
  ),
  Course_Name = c(
    "Introduction to Health Sciences",
    "Human Anatomy & Physiology I",
    "Medical Terminology",
    "Chemistry for Health Sciences",
    "Pathophysiology",
    "Biostatistics",
    "Pharmacology Basics",
    "Microbiology",
    "Clinical Practice Fundamentals",
    "Pharmacology Advanced",
    "Health Research Methods",
    "Epidemiology"
  ),
  Credits = c(3, 4, 3, 4, 4, 3, 3, 4, 4, 3, 3, 3),
  Study_Group_Hours = c(45, 62, 38, 55, 58, 48, 42, 65, 52, 47, 40, 38)
)

datatable(shared_courses,
          options = list(
            pageLength = 12,
            scrollX = TRUE,
            dom = 'tip'
          ),
          caption = "Table 3: Shared Course History 💪",
          class = 'cell-border stripe',
          rownames = FALSE)

4.2 Individual Course Performance

# Create grade data for each student in shared courses
course_grades <- data.frame(
  Student = rep(friends$Name, each = 12),
  Course = rep(shared_courses$Course_Code, 5),
  Semester = rep(shared_courses$Semester, 5),
  Grade = c(
    # Sakura's grades
    "A-", "B+", "A", "A-", "A", "A-", "B+", "A", "A-", "A", "A", "A-",
    # Amara's grades
    "A", "A", "A-", "A", "A", "A", "A-", "A", "A", "A-", "A", "A",
    # Diego's grades
    "B+", "B", "A-", "B+", "A-", "B+", "B+", "A-", "A", "A-", "B+", "A-",
    # Mei Lin's grades
    "A", "A", "A", "A", "A", "A", "A-", "A", "A", "A", "A", "A",
    # Siriporn's grades
    "A-", "B+", "A", "A-", "A", "A-", "A", "A-", "A-", "A", "A-", "A"
  ),
  Numerical_Score = c(
    # Sakura
    88, 85, 92, 90, 93, 89, 87, 91, 90, 94, 92, 91,
    # Amara
    94, 93, 91, 95, 96, 94, 92, 95, 94, 93, 95, 96,
    # Diego
    85, 82, 88, 86, 89, 87, 86, 90, 91, 90, 88, 89,
    # Mei Lin
    97, 96, 95, 96, 98, 97, 94, 96, 97, 98, 96, 97,
    # Siriporn
    90, 87, 92, 91, 93, 90, 92, 91, 91, 93, 92, 94
  )
)

datatable(course_grades,
          options = list(
            pageLength = 10,
            scrollX = TRUE,
            dom = 'ftp'
          ),
          caption = "Table 4: Individual Course Performance",
          class = 'cell-border stripe',
          rownames = FALSE,
          filter = 'top')

5 📈 Academic Progress Analysis

5.1 GPA Trajectory Over Time

# Semester-by-semester GPA for each student
semester_gpa <- data.frame(
  Semester = rep(c("Fall 2021", "Spring 2022", "Fall 2022", "Spring 2023", "Fall 2023", "Spring 2024"), 5),
  Student = rep(friends$Name, each = 6),
  GPA = c(
    # Sakura
    3.65, 3.70, 3.73, 3.75, 3.77, 3.78,
    # Amara
    3.78, 3.80, 3.82, 3.83, 3.84, 3.85,
    # Diego
    3.55, 3.58, 3.60, 3.62, 3.64, 3.65,
    # Mei Lin
    3.85, 3.88, 3.89, 3.90, 3.91, 3.92,
    # Siriporn
    3.62, 3.65, 3.67, 3.69, 3.70, 3.71
  ),
  Credits_Completed = rep(c(15, 30, 45, 60, 75, 90), 5)
)

# Order semesters chronologically
semester_order <- c("Fall 2021", "Spring 2022", "Fall 2022", "Spring 2023", "Fall 2023", "Spring 2024")
semester_gpa$Semester <- factor(semester_gpa$Semester, levels = semester_order)

plot_ly(semester_gpa, x = ~Semester, y = ~GPA, color = ~Student, 
        type = 'scatter', mode = 'lines+markers',
        colors = c("#FF6B9D", "#FFD93D", "#FF8C42", "#FFA07A", "#FFB6C1"),
        marker = list(size = 10),
        line = list(width = 3)) %>%
  layout(title = "📊 GPA Progression: Fall 2021 - Spring 2024",
         xaxis = list(title = "Semester", categoryorder = "array", 
                     categoryarray = semester_order),
         yaxis = list(title = "Cumulative GPA", range = c(3.5, 4.0)),
         hovermode = "closest",
         legend = list(orientation = 'h', y = -0.2))

5.2 Course Performance Comparison

# Average scores by course
course_avg <- course_grades %>%
  group_by(Course, Semester) %>%
  summarise(
    Avg_Score = round(mean(Numerical_Score), 1),
    Highest = max(Numerical_Score),
    Lowest = min(Numerical_Score)
  )

plot_ly(course_avg, x = ~Course, y = ~Avg_Score, type = 'bar',
        marker = list(color = "#FFD93D"),
        text = ~paste("Avg:", Avg_Score, "<br>Range:", Lowest, "-", Highest),
        hoverinfo = 'text') %>%
  layout(title = "📚 Average Study Group Performance by Course",
         xaxis = list(title = "Course Code"),
         yaxis = list(title = "Average Score", range = c(75, 100)))

5.3 Performance by Subject Area

# Calculate average by subject area for each student
subject_performance <- course_grades %>%
  mutate(Subject = case_when(
    Course %in% c("HS101", "HS102", "HS201", "HS202", "HS301", "HS302") ~ "Core Health Sciences",
    Course %in% c("BIO121", "MICRO240") ~ "Biological Sciences",
    Course %in% c("CHEM131", "PHARM310") ~ "Chemistry & Pharmacology",
    Course %in% c("STAT200", "EPID320") ~ "Statistics & Research"
  )) %>%
  group_by(Student, Subject) %>%
  summarise(Avg_Score = round(mean(Numerical_Score), 1))

plot_ly(subject_performance, x = ~Subject, y = ~Avg_Score, color = ~Student, type = 'bar',
        colors = c("#FF6B9D", "#FFD93D", "#FF8C42", "#FFA07A", "#FFB6C1")) %>%
  layout(title = "🎯 Performance by Subject Area",
         xaxis = list(title = ""),
         yaxis = list(title = "Average Score"),
         barmode = 'group',
         legend = list(orientation = 'h', y = -0.3))

6 🏆 Academic Achievements & Financial Aid

6.1 Scholarships and Awards Received

scholarships <- data.frame(
  Student = c(
    "Sakura Tanaka", "Sakura Tanaka",
    "Amara Okafor", "Amara Okafor", "Amara Okafor",
    "Diego Martinez", "Diego Martinez",
    "Mei Lin Chen", "Mei Lin Chen", "Mei Lin Chen",
    "Siriporn Patel", "Siriporn Patel"
  ),
  Scholarship_Name = c(
    "International Student Merit Award",
    "Nursing Excellence Scholarship",
    "Dean's List Scholarship",
    "Public Health Leadership Award",
    "Community Service Grant",
    "First Generation Scholar Award",
    "Hispanic Heritage Scholarship",
    "Presidential Scholarship",
    "STEM Excellence Award",
    "Research Assistant Stipend",
    "Women in STEM Grant",
    "Cultural Diversity Scholarship"
  ),
  Amount = c(
    5000, 3000,
    4000, 2500, 1500,
    4500, 2000,
    8000, 3500, 2000,
    3000, 2500
  ),
  Year_Awarded = c(
    2022, 2023,
    2022, 2023, 2023,
    2022, 2023,
    2021, 2022, 2023,
    2022, 2023
  )
)

datatable(scholarships,
          options = list(
            pageLength = 12,
            scrollX = TRUE,
            dom = 'tip'
          ),
          caption = "Table 5: Scholarships and Awards Received 🎓💰",
          class = 'cell-border stripe',
          rownames = FALSE) %>%
  formatCurrency('Amount', '$', digits = 0)

6.2 Total Financial Aid by Student

scholarship_totals <- scholarships %>%
  group_by(Student) %>%
  summarise(
    Total_Scholarships = n(),
    Total_Amount = sum(Amount)
  ) %>%
  arrange(desc(Total_Amount))

plot_ly(scholarship_totals, x = ~Student, y = ~Total_Amount, type = 'bar',
        marker = list(color = c("#FF6B9D", "#FFD93D", "#FF8C42", "#FFA07A", "#FFB6C1")),
        text = ~paste("$", format(Total_Amount, big.mark=","), "<br>", Total_Scholarships, "scholarships"),
        textposition = 'outside') %>%
  layout(title = "💰 Total Scholarship Funding by Student",
         xaxis = list(title = ""),
         yaxis = list(title = "Total Amount ($)"))

6.3 Financial Aid Timeline

awards_summary <- scholarships %>%
  group_by(Student, Year_Awarded) %>%
  summarise(Count = n(), Total = sum(Amount))

plot_ly(awards_summary, x = ~Year_Awarded, y = ~Total, color = ~Student, type = 'scatter',
        mode = 'lines+markers',
        colors = c("#FF6B9D", "#FFD93D", "#FF8C42", "#FFA07A", "#FFB6C1"),
        marker = list(size = ~Count * 5),
        text = ~paste(Student, "<br>", Count, "scholarships<br>$", format(Total, big.mark=",")),
        hoverinfo = 'text') %>%
  layout(title = "📅 Scholarship Awards Timeline",
         xaxis = list(title = "Year"),
         yaxis = list(title = "Total Amount ($)"))

7 📊 Study Habits Analysis

7.1 Study Hours by Course

plot_ly(shared_courses, x = ~Course_Name, y = ~Study_Group_Hours, type = 'bar',
        marker = list(color = "#FF8C42")) %>%
  layout(title = "⏰ Collaborative Study Hours by Course",
         xaxis = list(title = "", tickangle = -45),
         yaxis = list(title = "Total Study Hours"),
         margin = list(b = 150))

7.2 Study Time and Performance Correlation

# Calculate average score per course
course_performance <- course_grades %>%
  group_by(Course) %>%
  summarise(Avg_Score = mean(Numerical_Score))

# Merge with study hours
study_analysis <- shared_courses %>%
  select(Course_Code, Course_Name, Study_Group_Hours) %>%
  left_join(course_performance, by = c("Course_Code" = "Course"))

plot_ly(study_analysis, x = ~Study_Group_Hours, y = ~Avg_Score, type = 'scatter', mode = 'markers',
        marker = list(size = 15, color = "#FFD93D", opacity = 0.7),
        text = ~paste("<b>", Course_Name, "</b><br>",
                     "Study Hours:", Study_Group_Hours, "<br>",
                     "Avg Score:", round(Avg_Score, 1)),
        hoverinfo = 'text') %>%
  layout(title = "📖 Relationship Between Study Time and Performance",
         xaxis = list(title = "Group Study Hours"),
         yaxis = list(title = "Average Group Score"))

8 🌟 Academic Highlights

8.1 Individual Performance Summary

highlights <- data.frame(
  Student = friends$Name,
  Best_Course = c(
    "Microbiology (96)",
    "Epidemiology (96)",
    "Clinical Practice (91)",
    "Pathophysiology (98)",
    "Epidemiology (94)"
  ),
  Improvement = c(
    "+13 points (BIO→MICRO)",
    "+2 points consistent growth",
    "+9 points (BIO→CLINICAL)",
    "+2 points sustained excellence",
    "+7 points (CHEM→EPID)"
  ),
  Notable_Achievement = c(
    "Microbiology examination success 🌙",
    "Group research presentation 📊",
    "Clinical rotation initiation 🏥",
    "Laboratory practical excellence ⭐",
    "Extended study session effectiveness ☕"
  )
)

highlights %>%
  kable(caption = "Table 6: Individual Academic Achievements 🎉",
        col.names = c("Student", "Highest Performance", "Greatest Improvement", "Notable Achievement")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"),
                full_width = TRUE) %>%
  row_spec(0, bold = TRUE, color = "white", background = "#FF6B9D") %>%
  column_spec(1, bold = TRUE, background = "#FFF5E6", color = "#FF6B9D")
Table 8.1: Table 8.2: Table 6: Individual Academic Achievements 🎉
Student Highest Performance Greatest Improvement Notable Achievement
Sakura Tanaka Microbiology (96) +13 points (BIO→MICRO) Microbiology examination success 🌙 |
Amara Okafor Epidemiology (96) +2 points consistent growth Group research presentation 📊 |
Diego Martinez Clinical Practice (91) +9 points (BIO→CLINICAL) Clinical rotation initiation 🏥 |
Mei Lin Chen Pathophysiology (98) +2 points sustained excellence Laboratory practical excellence ⭐ |
Siriporn Patel Epidemiology (94) +7 points (CHEM→EPID) Extended study session effectiveness ☕ |

8.2 Grade Distribution Analysis

grade_counts <- course_grades %>%
  count(Student, Grade) %>%
  group_by(Student) %>%
  mutate(Percentage = round(n / sum(n) * 100, 1))

plot_ly(grade_counts, x = ~Student, y = ~n, color = ~Grade, type = 'bar',
        colors = c("#FF6B9D", "#FFD93D", "#FF8C42", "#FFA07A"),
        text = ~paste(Grade, ":", n, "courses (", Percentage, "%)"),
        hoverinfo = 'text') %>%
  layout(title = "📊 Grade Distribution by Student",
         xaxis = list(title = ""),
         yaxis = list(title = "Number of Courses"),
         barmode = 'stack')

9 💝 Future Academic Plans

9.1 🎓 Senior Year Objectives

As we approach our final academic year, the study group has identified several key objectives for successful program completion:

Academic Goals:

  • Complete comprehensive examinations successfully
  • Fulfill clinical rotation and practicum requirements
  • Submit applications for graduate programs and professional positions
  • Maintain consistent study group meetings (with continued peer support)
  • Support each other through program completion challenges

Post-Graduation Plans:

The study group has established a commitment to maintain professional connections following graduation. We plan to convene annually to discuss career development, share professional experiences, and continue our collaborative approach to professional growth.

Developed collaboratively by five Health Sciences students

Class of 2025 | Health Sciences Program | Professional Study Group