Interconnecting and Enriching Higher Education Programs using Linked Data

INTERCONNECTING AND

ENRICHING HIGHER EDUCATION

PROGRAMS USING LINKED DATA

Fouad ZablithAmerican University of Beirut

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

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

Data Model

Data Model

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

Course

Topic

Course

Topic

Concept

Course

Topic

Concept

Learning Material

Phase 1: Creating Courses Information

Phase 1: Creating Courses Information

http://linked.aub.edu.lb/collab

Phase 1: Creating Courses Information

http://linked.aub.edu.lb/collab

Phase 2: Identifying Concepts Taught

Conceptual Linking

Phase 3: Anchoring Learning Material to

Courses

Learning Material Bookmarklet

http://linked.aub.edu.lb/docs/tutorial_material_bookmark/

Example:

https://www.youtube.com/watch?v=f60dheI4ARg

Students Expanding the Data Graph

http://www.capgemini.com/resources/the-deciding-factor-big-data-decision-making

Learning Material

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

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

Using the Linked Data Graph:

Enriching Moodle Environments

Using the Linked Data Graph:

Enriching Moodle Environments

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

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

Using the Linked Data Graph:

Program Review Concepts Graph

http://linked.aub.edu.lb/collab/index.php/Learning_Concepts_Graph

Using the Linked Data Graph:

Program Review Concepts Table

http://linked.aub.edu.lb/apps/tablebrowser/table.php

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

Future Directions: Capturing Social

Interactions

Future Directions: Boosting Analytics

Future Directions: Boosting Analytics

Future Directions: Boosting Analytics

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

Thank you!

[email protected]://fouad.zablith.org

@fzablith