1.
INTERCONNECTING AND
ENRICHING HIGHER EDUCATION
PROGRAMS USING LINKED DATA
Fouad Zablith
American University of Beirut

2.
Problem
• Higher education programs are often designed around
courses that follow a specific sequence
• Courses are usually described at high levels in the form of
syllabi and program catalogues
• This text-based representation creates hard knowledge
boundaries around courses that tend to be delivered and
analyzed mostly in isolation

3.
Approach
• In this work, we aim to de-blackbox the representation of a
higher education curriculum
• We rely on linked data to generate and represent the
conceptual connections around the courses in a higher
education program
• We deploy a Semantic Mediawiki platform to collaboratively
build the knowledge graph around the courses
• We highlight the value of this linked data layer through two use
cases to (1) enrich online learning environments and (2)
support the program review process

4.
Data Model

5.
Data Model

6.
Building the Linked Data Graph
• Phase 1: Creating Courses Information
• Course syllabi are used to identify the high level course information
(e.g. course name, description, topics, etc.)
• Phase 2: Identifying Concepts Taught
• This was the most time consuming task, where course material
(mainly textbooks) were used to identify taught concepts in courses
• Phase 3: Anchoring Learning Material to Courses
• This is the ongoing enrichment step using a semantic bookmarklet
to connect external material to courses

7.
Course
Topic

8.
Course
Topic
Concept

9.
Course
Topic
Concept
Learning Material

10.
Phase 1: Creating Courses Information

11.
Phase 1: Creating Courses Information
http://linked.aub.edu.lb/collab

12.
Phase 1: Creating Courses Information
http://linked.aub.edu.lb/collab

13.
Phase 2: Identifying Concepts Taught

14.
Conceptual Linking

15.
Phase 3: Anchoring Learning Material to
Courses

16.
Learning Material Bookmarklet
http://linked.aub.edu.lb/docs/tutorial_material_bookmark/
Example:
https://www.youtube.com/watch?v=f60dheI4ARg

17.
Students Expanding the Data Graph
http://www.capgemini.com/resources/the-deciding-factor-big-data-decision-making
Learning Material

18.
Students Expanding the Data Graph
http://www.capgemini.com/resources/the-deciding-factor-big-data-decision-making
BigData
Operations Decisions
Social Media
Automated Process
Organizational Silos
Decision
Internet Services
Business Process
Reengineering
Intuitive Decision Making
Learning Material Concepts

19.
Current Data Available
• So far we have captured the following data around the
school’s higher education program:
Category Number
Courses 20
Topical coverage 171
Taught concepts 2,684
Learning Material 75

20.
Using the Linked Data Graph:
Enriching Moodle Environments

21.
Using the Linked Data Graph:
Enriching Moodle Environments

22.
Impact of Student’s Input
http://www.capgemini.com/resources/the-deciding-factor-big-data-decision-making
Information
Systems
Management
Operations
Management
BigData
Operations Decisions
Social Media
Automated Process
Organizational Silos
Decision
Internet Services
Business Process
Reengineering
Intuitive Decision Making
Learning Material Concepts Moodle Courses

23.
Impact of Student’s Input
http://www.capgemini.com/resources/the-deciding-factor-big-data-decision-making
Information
Systems
Management
Operations
Management
BigData
Operations Decisions
Social Media
Automated Process
Organizational Silos
Decision
Internet Services
Business Process
Reengineering
Intuitive Decision Making
Learning Material Moodle Courses
Indirectly interlinking
courses’ content
Concepts

24.
Using the Linked Data Graph:
Program Review Concepts Graph
http://linked.aub.edu.lb/collab/index.php/Learning_Concepts_Graph

25.
Using the Linked Data Graph:
Program Review Concepts Table
http://linked.aub.edu.lb/apps/tablebrowser/table.php

26.
Future Directions
• Develop further data-driven applications to support
learning experiences
• Capture social interactions around the linked data graph
to have more granular insights on students’ behavior
around the concepts delivered
• Capitalize on the data graph to boost analytics and further
support the curriculum review process

27.
Future Directions: Capturing Social
Interactions

28.
Future Directions: Boosting Analytics

29.
Future Directions: Boosting Analytics

30.
Future Directions: Boosting Analytics

31.
Conclusions
• We presented our efforts on collaboratively building a
linked data graph to capture concepts exchanged in
higher education programs
• We highlighted the value of this linked data layer at two
levels:
• First at the level of enriching learning environments and breaking
the knowledge boundaries around courses
• Second at the analytics level by building tools that provide new and
unprecedented curriculum visualizations

32.
Thank you!
fouad.zablith@aub.edu.lb
http://fouad.zablith.org
@fzablith