Learning Analytics Dashboard

Real-time machine learning predictions, accomplishments, and tailored path directives.

Quick Stats

Course Completion 64%
Quizzes Taken 4
Global Level Lvl 4

Automated Recommendations

Netflix-style adaptive learning recommendations based on performance history:

Badges & Achievements

๐ŸŒฟ DSA Explorer
๐Ÿ•ธ๏ธ Graph Master
๐Ÿ“ˆ ML Prodigy
๐ŸŽ™๏ธ Voice Scholar
๐Ÿ† Quiz Champion

ML Performance Forecast

Predicting final academic outcome based on study patterns:

--% Predicted Grade
LOW RISK

Predictor Context

  • Study Time vs Attendance weighted ratio: Optimal.
  • Prerequisites: Dijkstra Shortest Path mapped.

Update variables under the Predictor (ML) tab to see this forecast dynamically recalibrate.

Curriculum Organizer (DSA Graph & Tree)

Visualize BFS, DFS, Dijkstra Shortest Path, and Topological Sort sequence directly mapped to subject dependencies.

Start Node:

Algorithm State Log

Data Structures

Select an algorithm and press Start to initialize structure frames.

Syllabus Tree Structure (Subjects & Topics)

Click headers to toggle collapse state in the educational syllabus tree:

Performance Forecast Model (ML)

Tweak student stats to trigger score forecasts, or fit weights using multivariate Gradient Descent.

Forecast Parameters

Attendance Rate 85%
Weekly Study Hours 10h
Previous Semester GPA 3.2
Quiz Average Score 80%
Course Syllabus Difficulty 3

Multivariate Linear Regression Fit

Mathematical model equation using updated coefficients:

Score = wโ‚€ + wโ‚*(Attendance) + wโ‚‚*(StudyHours) + ...

Current Model Weights (Coefficients)

Training MSE Loss history

Model is utilizing baseline weights. Click Train Model to observe gradient descent.

GenAI Tutor & Revision Cards

Converse with your tutor, request interactive quizzes, and convert text to summarized revision cards.

Voice Accent:
๐Ÿค–

Hello! I am your AI Study Tutor.

Ask me to explain any topics from our curriculum (like K-Means Clustering, Overfitting, or Dijkstra's shortest path), or write notes below to summarize them.

Automated Text Summarizer

Paste lecture notes or chapter paragraphs to generate key points and revision flashcards:

Interactive Concept Quiz

Generate quizzes covering selected topics to test your comprehension and adjust prediction parameters.

Retrieval-Augmented Generation (RAG) Pipeline

Visualize textbook processing, vector coordinates database lookup, and prompt formulation.

2D Vector Space Projection

Visualizing text similarity using term-frequency cosine distances (2D scatter projection):

RAG Execution Steps

1
Document Chunking
Syllabus divided into sentence window segments.
2
TF-IDF Vectorization
Vector coordinates computed based on vocabulary frequency.
3
Cosine Vector Distance Lookup
Determined closest vector matches for the query.
4
Prompt Context Injection
Matched passages combined with study queries.

Formulated LLM Context

No query executed yet. Ask a question in the AI Tutor tab or trigger RAG database exploration.