Personalized Learning Assistant: AI Project – PART 5

Deeper Insights into Testing Strategies, Advanced ML Features, and User Engagement Tracking

For the Personalized Learning Assistant, it is essential to validate functionality thoroughly, explore advanced ML capabilities for dynamic recommendations, and track user engagement to fine-tune the system. Here's an in-depth breakdown:

1. Testing Strategies

Backend Testing

a. Unit Testing

• Framework: xUnit or NUnit.
• Goal: Test individual services and repository methods in isolation.

Example: Testing ProgressService

 [Fact]

public async Task GetProgressByUser_ShouldReturnProgressForValidUser()

{

    // Arrange

    var mockDbContext = new Mock<LearningDbContext>();

    var service = new ProgressService(mockDbContext.Object);

    var userId = 1;

    var progressData = new List<Progress>

    {

        new Progress { ProgressID = 1, UserID = 1, CourseID = 101, CompletionPercentage = 75.5f, LastAccessed = DateTime.Now }

    };

    mockDbContext.Setup(db => db.Progress.Where(It.IsAny<Expression<Func<Progress, bool>>>()))

                 .Returns(progressData.AsQueryable());

    // Act

    var result = await service.GetProgressByUser(userId);

    // Assert

    Assert.NotNull(result);

    Assert.Equal(1, result.Count);

    Assert.Equal(75.5f, result.First().CompletionPercentage);

}

b. Integration Testing

• Framework: MSTest or SpecFlow (for BDD).
• Goal: Ensure proper interaction between layers (API ↔ Database).

Example: Test API Endpoint

 [Fact]

public async Task GetRecommendationsEndpoint_ShouldReturnRecommendedCourses()

{

    var client = _factory.CreateClient();

    var response = await client.GetAsync("/api/Recommendations/1");

    response.EnsureSuccessStatusCode();

    var content = await response.Content.ReadAsStringAsync();

    Assert.Contains("Intro to AI", content);

}

c. Load Testing

• Tool: Apache JMeter or k6.
• Goal: Measure API performance under varying load conditions.

Frontend Testing

a. Unit Testing

• Tool: BUnit for Blazor components.
• Example: Validate that the course list renders correctly.

 [Fact]

public void CourseList_ShouldRenderCorrectly()

{

    // Arrange

    using var ctx = new TestContext();

    var courses = new List<Course> { new Course { CourseID = 101, CourseName = "Intro to AI" } };

    ctx.Services.AddSingleton(courses);

    // Act

    var component = ctx.RenderComponent<CourseList>();

    // Assert

    Assert.Contains("Intro to AI", component.Markup);

}

b. End-to-End Testing

• Tool: Selenium or Playwright.
• Scenario: Simulate a user journey from login to course recommendations.

Database Testing

• Use SQL unit testing frameworks like tSQLt to test stored procedures and data integrity.

2. Advanced ML Features for Recommendations

Dynamic Recommendations Using Context-Aware Filtering

Enhance recommendations by including contextual data such as:

1. Time of Access: Suggest short courses during weekdays and longer ones on weekends.
2. Device Type: Recommend content optimized for mobile vs. desktop.
3. Real-Time Engagement: Prioritize courses with higher user ratings or live sessions.

Implementation: Contextual Embeddings

Use a pre-trained transformer model like BERT for encoding course descriptions and user preferences dynamically.

Steps:

1. Extract Metadata: For users: Gather learning preferences, activity logs, and ratings. For courses: Extract keywords and tags.
2. Encode Features: Encode using BERT embeddings.
3. Train Enhanced Recommendation Model: Use embeddings as input to a neural collaborative filtering model.

Incorporating Reinforcement Learning

Utilize reinforcement learning to dynamically adapt recommendations based on feedback (e.g., likes/dislikes, course completions).

• Tool: ML.NET with custom RL environments or Python with PyTorch.
• Algorithm: Q-Learning or Deep Q-Learning.

Workflow:

1. Action: Recommend a course.
2. Reward: +1 if the user engages (e.g., clicks or completes). -1 if skipped.
3. Update Policy: Use user feedback to refine the recommendation strategy.

3. User Engagement Tracking

Key Metrics

• Active Time: How long users spend on the platform.
• Completion Rates: Number of users completing courses.
• Drop-Off Points: Identify where users abandon courses.
• Session Frequency: Average number of sessions per week.

Database Schema

Add an EngagementLog table to track user interactions.

 Field Type Constraints Description

LogID int Primary Key, Identity Unique identifier for the log entry.

UserID int Foreign Key (Users) References the Users table.

CourseID int Foreign Key (Courses) References the Courses table.

Action nvarchar(50) Not Null E.g., "Viewed", "Completed".

Timestamp datetime Not Null Timestamp of the interaction.

Engagement Analytics Service

public class EngagementService

{

    private readonly LearningDbContext _context;

    public EngagementService(LearningDbContext context)

    {

        _context = context;

    }

    public async Task<int> GetTotalActiveUsers()

    {

        var oneWeekAgo = DateTime.Now.AddDays(-7);

        return await _context.EngagementLogs

            .Where(log => log.Timestamp >= oneWeekAgo)

            .Select(log => log.UserID)

            .Distinct()

            .CountAsync();

    }

    public async Task<List<string>> GetTopCoursesByEngagement()

    {

        return await _context.EngagementLogs

            .GroupBy(log => log.CourseID)

            .OrderByDescending(g => g.Count())

            .Select(g => g.Key.ToString())

            .Take(5)

            .ToListAsync();

    }

}

Frontend Integration

1. Display active user trends in a line graph.
2. Show top courses by engagement in a leaderboard.

Deployment and Scaling

Infrastructure

1. Backend: Deploy APIs and ML models using Azure App Services or AWS Lambda.
2. Database: Use Azure SQL Database with geo-replication for high availability.
3. Frontend: Host Blazor WebAssembly on Azure Static Web Apps.

Scaling Recommendations

• Autoscale based on CPU usage for APIs.
• Use Redis Cache to cache frequent API responses.
• Implement data partitioning in SQL Server for large datasets.

Future Enhancements

1. Gamification: Add badges and points for milestones (e.g., course completions).
2. AI Chatbot: Use Azure Bot Framework for a conversational assistant.
3. Social Features: Add discussion forums and peer reviews for courses.