Engagement, Gaps, Personalization

Knobase’s analytics engine goes beyond basic usage tracking—it helps educators understand how students engage, where they struggle, and how to personalize support based on real-time data. This feature is key to unlocking the full potential of AI-enhanced learning.

🧠 Why It Matters

✅ Identifies students who are highly engaged or disengaged
✅ Pinpoints academic gaps and confusion areas
✅ Enables personalized learning pathways and interventions
✅ Supports inclusive and differentiated instruction

⚙️ How It Works

1. Engagement Tracking

Knobase monitors how often and how meaningfully students interact with AI agents:

• Prompt frequency and timing (e.g., late-night usage)

• Subject focus (e.g., frequent queries in science vs. humanities)

• Depth of inquiry (e.g., factual vs. analytical questions)

✅ Example:

Student C submits 40 prompts per week, mostly in science and coding.
→ Suggest enrichment programs or mentorship in STEM.

2. Gap Detection

Knobase identifies recurring confusion or weak areas based on prompt patterns:

• Repeated questions on the same topic

• Low prompt quality or vague phrasing

• Requests for clarification or re-explanation

✅ Example:

Student D repeatedly asks about fractions and shows low prompt scores in math.
→ Recommend targeted tutoring or scaffolded resources.

3. Personalization Engine

Based on engagement and gaps, Knobase suggests:

• Tailored learning resources (e.g., videos, worksheets, agents)

• Adaptive feedback strategies (e.g., simplified explanations, visual aids)

• Enrichment opportunities (e.g., clubs, competitions, projects)

✅ Example:

Student E shows interest in storytelling and asks for plot ideas.
→ Suggest joining the creative writing club and using the “Creative Partner” agent.

📊 Use Cases 🧑‍🎓 Students: Receive support and challenges tailored to their needs
🧑‍🏫 Teachers: Differentiate instruction based on real-time data
🧑‍💼 Admins: Monitor school-wide engagement and optimize resource allocation