Education Pathways: Difference between revisions

Latest revision as of 05:19, 17 February 2019

Looking at data science products in the educational space.

Get input data
- Dataset of all courses
  - Create components of courses
  - Course tagging, MDM, metadata
- Get Student Data
  - Aptitude
  - Location of residence
  - Socio-Economic profile
  - Age etc..
  - Aspirations
- Get environmental data
  - Access to finance
  - Economic conditions
  - Benefits of Educational Outcome / Sentiment Media
Overlay any dependencies
- Automated identification of dependencies via NLP, topic modelling, downstream word co-relation
- Tokenisation, Topic Modelling
- Correlation of topics -> Correlation of Courses
- Generate Educational Graph Pathways
- Analyse Graph nodes for waypoints, course clusters, Badging
  - Allows us to identify duplication in course content
Overlay value of courses
- Based on demand, Based on market need, Social feedback
- Scoring of courses using sentiment analysis
Recommendation
- Based on user selection/user goals?
- Based on university marketing?
- Based on end goals?
- Based on user aptitude?
- Recommendation using inputs + trained recommendation model

@@ Line 14: / Line 14: @@
 *** Access to finance
 *** Economic conditions
-*** Benefits of Educational Outcome
+*** Benefits of Educational Outcome / Sentiment Media
 * Overlay any dependencies
 ** Automated identification of dependencies via NLP, topic modelling, downstream word co-relation
@@ Line 20: / Line 20: @@
 ** Correlation of topics -> Correlation of Courses
 ** Generate Educational Graph Pathways
-** Analyse Graph nodes for waypoints, course clusters (Badging)
+** Analyse Graph nodes for waypoints, course clusters, [[Badging]]
 *** Allows us to identify duplication in course content
 * Overlay value of courses