Quiz implementation TODO list

[larnsce]

Small Classes Implementation Todo List

Overview

Detailed step-by-step todo list for implementing learnr tutorials for small classes using Shinyapps.io with Google Sheets logging and learnrhash for submission tracking.

---

Phase 1: Account Setup and Prerequisites

1.1 Shinyapps.io Account Setup

• Create RStudio/Posit account:
  • Go to https://www.rstudio.com/products/shinyapps/
  • Sign up for free account (or upgrade to paid if needed)
  • Verify email address
  • Note account name for deployment
• Review pricing and limits:
  • Free tier: 25 active hours/month, 5 apps
  • Starter ($9/month): 100 hours/month, 10 apps
  • Basic ($39/month): 500 hours/month, 25 apps
  • Choose appropriate plan for your class size

1.2 Google Account Setup

• Create/use Google account for data collection:
  • Use institutional Google account if available
  • Or create dedicated account (e.g., ds4owd.quiz@gmail.com)
  • Enable 2-factor authentication for security
• Set up Google Sheets API access:
  • Go to Google Cloud Console (console.cloud.google.com)
  • Create new project or use existing
  • Enable Google Sheets API
  • Create service account and download JSON key
  • Share Google Sheets with service account email

1.3 R Environment Setup

• Install required R packages:

  install.packages(c(
    "rsconnect",      # For deploying to shinyapps.io
    "learnr",         # Interactive tutorials
    "gradethis",      # Exercise checking
    "googlesheets4",  # Google Sheets integration
    "learnrhash",     # Submission hashing
    "tidyverse",      # Data manipulation
    "gapminder",      # Sample data
    "gt",             # Tables
    "shiny",          # Web framework
    "DT"              # Interactive tables
  ))

• Configure rsconnect:

  # Get tokens from shinyapps.io account
  rsconnect::setAccountInfo(
    name = "your-account-name",
    token = "your-token", 
    secret = "your-secret"
  )

---

Phase 2: Google Sheets Data Collection Setup

2.1 Create Data Collection Spreadsheet

• Create main submissions spreadsheet:
  • Create new Google Sheet named "DS4OWD Module 1 Submissions"
  • Set up columns: timestamp, student_name, student_email, exercise_id,
    correct, code_submitted, attempt_number, session_id
  • Format timestamp column as datetime
  • Share with service account email (view + edit permissions)
• Create student roster sheet (optional):
  • Add tab for enrolled students
  • Columns: student_name, student_email, student_id, enrolled_date
  • Import class roster if available

2.2 Configure Google Sheets Authentication

• Set up authentication in tutorial:

  # In setup chunk of learnr tutorial
  library(googlesheets4)
  
  # Use service account authentication
  gs4_auth(path = "path-to-service-account-key.json")
  
  # Or use cached token approach for shinyapps.io
  gs4_auth(cache = ".secrets", email = "your-email@gmail.com")

• Test connection:

  # Test writing to sheet
  sheet_id <- "your-google-sheet-id"
  test_data <- data.frame(
    timestamp = Sys.time(),
    test_field = "connection_test"
  )
  sheet_append(sheet_id, test_data)

2.3 Create Logging Functions

• Implement submission logging function:

  log_submission <- function(student_name, student_email, exercise_id, 
                           correct, code_submitted, attempt_number = 1) {
    
    # Sheet ID from your Google Sheet URL
    sheet_id <- "your-google-sheet-id-here"
    
    # Create log entry
    log_entry <- data.frame(
      timestamp = Sys.time(),
      student_name = student_name,
      student_email = student_email,
      exercise_id = exercise_id,
      correct = correct,
      code_submitted = code_submitted,
      attempt_number = attempt_number,
      session_id = paste0("session_", format(Sys.time(), "%Y%m%d_%H%M%S")),
      stringsAsFactors = FALSE
    )
    
    # Append to Google Sheet
    tryCatch({
      sheet_append(sheet_id, log_entry)
    }, error = function(e) {
      # Log errors locally as backup
      cat("Error logging to Google Sheets:", e$message, "\n",
          file = "submission_errors.log", append = TRUE)
    })
  }

---

Phase 3: Tutorial Modification for Data Collection

3.1 Add Student Information Collection

• Create student info UI at start of tutorial:

  # Add to beginning of tutorial
  div(id = "student-info-form",
    h3("Student Information"),
    p("Please enter your information to begin the quiz:"),
    textInput("student_name", "Full Name:", 
              placeholder = "First Last"),
    textInput("student_email", "Email Address:", 
              placeholder = "student@university.edu"),
    textInput("student_id", "Student ID (optional):", 
              placeholder = "12345678"),
    actionButton("start_quiz", "Start Quiz", class = "btn-primary"),
    br(), br()
  )

3.2 Integrate Submission Tracking

• Add event tracking to exercises:

  # Modify existing exercises to include logging
  # Add this after each exercise check
  observe({
    if(!is.null(input$student_email) && input$student_email != "") {
      # Log exercise attempts
      if(!is.null(input$`exercise-name-code-editor`)) {
        log_submission(
          student_name = input$student_name,
          student_email = input$student_email,
          exercise_id = "exercise-name",
          correct = FALSE,  # Will be updated when checked
          code_submitted = input$`exercise-name-code-editor`,
          attempt_number = 1
        )
      }
    }
  })

3.3 Add learnrhash Integration

• Add submission hash generation:

  # At end of tutorial
  div(
    h3("Generate Submission Hash"),
    p("Complete all exercises above, then click below to generate your submission hash:"),
    encoder_ui("hash_encoder"),
    br(),
    div(id = "submission-instructions",
      h4("Submission Instructions:"),
      tags$ol(
        tags$li("Copy the hash code above"),
        tags$li("Submit it via [your LMS/email/form]"),
        tags$li("Include your name and student ID in submission")
      )
    )
  )
  
  # Server logic for hash encoder
  encoder_logic("hash_encoder")

---

Phase 4: Testing and Deployment

4.1 Local Testing

• Test tutorial locally:

  # Run tutorial locally to test all functionality
  rmarkdown::run("md-01-quiz.Rmd")

• Test all features:
  • Student information collection works
  • All MCQ questions function properly
  • All coding exercises provide feedback
  • Google Sheets logging works
  • learnrhash generation works
  • Error handling doesn't break tutorial

4.2 Prepare for Deployment

• Handle authentication files:

  # For Google Sheets authentication on shinyapps.io
  # Use cached authentication approach:
  
  # Run this locally first:
  gs4_auth(cache = ".secrets", email = "your-email@gmail.com")
  
  # This creates .secrets folder with cached tokens
  # Include .secrets folder in deployment

• Create deployment script:

  # deploy.R
  library(rsconnect)
  
  # Deploy the tutorial
  rsconnect::deployApp(
    appDir = ".",
    appFiles = c("md-01-quiz.Rmd", ".secrets/"),
    appName = "ds4owd-module1-quiz",
    account = "your-account-name",
    forceUpdate = TRUE
  )

4.3 Deploy to Shinyapps.io

• Deploy tutorial:

  # Run deployment script
  source("deploy.R")

• Test deployed version:
  • Visit deployed URL
  • Complete full tutorial as test student
  • Verify submissions appear in Google Sheets
  • Test on different browsers/devices
  • Check mobile responsiveness

---

Phase 5: Student Access and Instructions

5.1 Create Student Instructions

• Write clear instructions for students:

  # Module 1 Interactive Quiz Instructions
  
  ## Before You Begin
  1. Ensure you have a stable internet connection
  2. Allow 30-45 minutes to complete the quiz
  3. Have your student ID ready
  
  ## How to Take the Quiz
  1. Click the quiz link: [your-app-url]
  2. Enter your full name and email address
  3. Work through all three sections
  4. Generate your submission hash at the end
  5. Submit the hash via [Canvas/email/form]
  
  ## Technical Requirements
  - Modern web browser (Chrome, Firefox, Safari, Edge)
  - JavaScript enabled
  - Cookies enabled
  
  ## Getting Help
  - Technical issues: email instructor
  - Content questions: review Module 1 materials

5.2 Set Up Submission Collection

• Choose submission method:
  • Option A: LMS assignment (Canvas, Moodle, etc.)
  • Option B: Google Form
  • Option C: Email submission
  • Option D: GitHub issue template
• Create submission form (if using Google Forms):

  Form fields:
  - Student Name (required)
  - Student Email (required) 
  - Student ID (required)
  - Submission Hash (required, long text)
  - Comments/Issues (optional)
  

---

Phase 6: Launch and Monitoring

6.1 Soft Launch

• Test with small group first:
  • Invite 3-5 volunteer students
  • Monitor for any issues
  • Gather feedback on user experience
  • Make necessary adjustments
• Check data collection:
  • Verify submissions appearing in Google Sheets
  • Check submission hashes are valid
  • Test grading workflow

6.2 Full Launch

• Announce to class:
  • Send email with instructions and deadline
  • Post announcement in LMS
  • Provide direct link to quiz
  • Set clear deadline and expectations
• Monitor during active period:
  • Check shinyapps.io usage dashboard
  • Monitor Google Sheets for submissions
  • Respond to student questions quickly
  • Watch for technical issues

---

Phase 7: Data Analysis and Grading

7.1 Collect and Analyze Submissions

• Download submission data:

  # Read data from Google Sheets
  library(googlesheets4)
  
  submissions <- read_sheet("your-sheet-id")
  
  # Basic analysis
  library(dplyr)
  
  # Completion rates by exercise
  completion_by_exercise <- submissions %>%
    group_by(exercise_id) %>%
    summarise(
      total_attempts = n(),
      unique_students = n_distinct(student_email),
      success_rate = mean(correct, na.rm = TRUE)
    )
  
  # Student progress
  student_progress <- submissions %>%
    group_by(student_email) %>%
    summarise(
      exercises_attempted = n_distinct(exercise_id),
      total_correct = sum(correct, na.rm = TRUE),
      completion_rate = exercises_attempted / 15  # 15 total questions
    )

7.2 Generate Grades

• Create grading rubric:
  • MCQ questions: 1 point each (10 total)
  • Coding exercises: 1 point each (5 total)
  • Total: 15 points possible
  • Consider partial credit for coding exercises
• Calculate final grades:

  # Generate gradebook export
  gradebook <- student_progress %>%
    mutate(
      points_earned = total_correct,
      points_possible = 15,
      percentage = (points_earned / points_possible) * 100,
      letter_grade = case_when(
        percentage >= 90 ~ "A",
        percentage >= 80 ~ "B", 
        percentage >= 70 ~ "C",
        percentage >= 60 ~ "D",
        TRUE ~ "F"
      )
    )
  
  # Export for LMS
  write_csv(gradebook, "module1_grades.csv")

---

Phase 8: Maintenance and Improvements

8.1 Regular Maintenance

• Weekly tasks:
  • Check shinyapps.io usage (avoid hitting limits)
  • Monitor Google Sheets for issues
  • Backup submission data locally
  • Review student feedback/questions
• After each semester:
  • Archive old submission data
  • Update tutorial content based on feedback
  • Review and improve questions
  • Update package versions

8.2 Continuous Improvement

• Collect student feedback:
  • Add feedback form to end of tutorial
  • Analyze common error patterns
  • Improve hints and explanations
  • Add more practice exercises if needed
• Instructor feedback:
  • Track time spent on grading
  • Note common student issues
  • Document lessons learned

---

Success Metrics

Quantitative Metrics

• Completion rates:
  • Target: >85% of enrolled students complete quiz
  • >90% of students who start complete all sections
• Technical performance:
  • <5 seconds initial load time
  • <1% technical failure rate
  • 100% data capture rate
• Academic performance:
  • Average score >75%
  • <10% students need to retake

Qualitative Metrics

• Student satisfaction:
  • Easy to use interface
  • Clear instructions
  • Helpful feedback on exercises
• Instructor efficiency:
  • Automated grading saves time
  • Easy to monitor student progress
  • Clear analytics for improvement

---

Timeline and Budget

Timeline (Small Classes)

• Phase 1: 1 week (Account setup)
• Phase 2: 1 week (Google Sheets setup)
• Phase 3: 1-2 weeks (Tutorial modification)
• Phase 4: 1 week (Testing and deployment)
• Phase 5: 1 week (Student instructions)
• Phase 6: 1 week (Launch and monitoring)
• Phase 7: 1 week (Grading)
• Phase 8: Ongoing

Total Setup Time: 6-8 weeks
Ongoing Time: 2-3 hours per semester

Budget (Small Classes)

• Shinyapps.io: $0-39/month depending on usage
• Google Workspace: $0 (free tier sufficient)
• Domain: $0 (using shinyapps.io subdomain)
• Development time: 20-30 hours initial setup
• Maintenance: 2-3 hours per semester

Total Annual Cost: $0-468 (mostly labor)

---

Troubleshooting Common Issues

Technical Issues

• Tutorial won't load:
  • Check shinyapps.io status page
  • Verify package versions are compatible
  • Check deployment logs
• Google Sheets not updating:
  • Verify authentication is working
  • Check API quotas not exceeded
  • Ensure sheet permissions are correct
• Students can't access:
  • Check URL is correct
  • Verify app is deployed and active
  • Test on different browsers

Content Issues

• Exercises not working:
  • Check gradethis syntax
  • Verify solution code is correct
  • Test locally before deployment
• Hints not helpful:
  • Add progressive hints
  • Include common error explanations
  • Link to relevant documentation

This streamlined approach is perfect for small classes, providing professional-grade interactive quizzes with minimal technical overhead and cost.