This document discusses creating personalized learning maps for students based on their data and teacher feedback. It proposes generating cognitive maps using student assessment data, common core standards, and teacher input. These maps would store recommended online resources for different learning paths. Teachers could evaluate and rank resources to help the system provide more personalized recommendations to students.

1. Student-data-driven Learning
Maps & Useful Resources
Cognitive Maps
Gagan Annamreddy

2. What did we create and why is this important?
• No personalized Learning maps for each class.
• Teachers need to create maps for their course and validate the recommendations
(One time activity for a course)
• Lots of online resources for every learning path. Which one is good for student ??
How to make the recommendation personalized??
• Cognitive resource recommendation based on the search engine and teacher
feedback. Eg. Search engines can bring all the top videos from KhanAcademy
,youtube, wiki pages, other online resources a learning path(Say 5th grade
Geometry). Teachers evaluates these videos and rank them.
Editable title in the footer 11/29/2012 2

3. How did we validate the problem?
Research:
• Google trend on the word Geometry. Lot of time spent on finding the right helpful
resource for a learning path.
Editable title in the footer 11/29/2012 3

4. Learning Maps for Khan Academy Content
Editable title in the footer 11/29/2012 4

5. Map Generation
SLC Assessment
Data
Individual
Common Core Cognitive Maps
Standards Cognitive
Store (SLC
Map
Generation Custom Data)
Teacher Input
Editable title in the footer 11/29/2012 5

6. Leveraging SLC
• SLC can help to store the learning maps and useful resources to the student &
teacher data models.
• Leverage the existing apis from KhanAcademy , learningregistry.org to pull the meta
data of the useful resources on the internet.
Editable title in the footer 11/29/2012 6

7. Demonstration
Editable title in the footer 11/29/2012 7