Interpreting Data Mining Results with Linked Data for Learning Analytics

Interpreting Data Mining Results with Linked Data for Learning

Analytics:Motivation, Case Study and

DirectionsMathieu d’Aquin

Knowledge Media Institute, The Open University mdaquin.net - @mdaquin

mathieu.daquin@open.ac.uk

Nicolas JayUniversité de Lorraine, LORIA,

nicolas.jay@loria.fr

My super naïve view of learning analytics

Data (from some education

related system)

Some kind of data processing Visualisation

Insight!

Tada!

But actually…

Data (from some education

related system)

Some kind of data processing Visualisation

Insight!

Tadada!

Interpretation

Needs more data/information

Data (from some education

related system)

Some kind of data processing Visualisation

Insight!

Tadada dou!

Interpretation Background knowledge

The challenge for learning analytics

Most of the time, background knowledge needs to be in the head of the people looking at the analytics.

How to find/obtain background information for interpretation to support him/her considering that:

– The data we are analysing and insight we are trying to obtain can cover a wide range of things, topics, domains, subjects…

– We might not know in advance we background information is needed for interpretation

Our approach: Integrate linked data sources at the time of interpretation

What’s linked data

See the “Using Linked Data in Learning Analytics” tutorial yesterdayhttp://linkedu.eu/event/lak2013-linkeddata-tutorial/

Linked Data

Open University Website

Open UniversityVLE

KMi Website

Mathieu’s Homepage

Mathieu’s List of

PublicationsMathieu’s

Twitter

The Web

M366 Coursepage

Person: Mathieu

Publication: Pub1

Organisation:The Open University

Course: M366

Country: Belgium

Book: Mechatronics

author

workFor

availableIn

offers

setBook

The Web of Linked Data

Gene Ontology

FMA OntologyLODE

BIBO

Geo Ontology

DBPedia Ontology

Dublin Core

FOAF

DOAP

SIOC

Music Ontology

Media Ontology

rNews

Example: data.open.ac.uk

Use case: student enrolment data

From the Open University’s Course Profile Facebook Application:

Who enrolled to what course at what time

Student ID Course Code Status Date112 dse212 Studying 2007

112 d315 Intend to study 2008

109 a207 Completed 2005

Examples:

Sequence mining

We can represent each student’s trajectory by a sequence of courses, e.g.

(DD100) (D203, S180) (S283)

Applying sequence mining makes it possible to find frequent patterns in these sequences, i.e., courses often taken together in a certain order.

The results(and again, why they need background knowledge for interpretation)

Out of 8,806 sequences (students), we obtained 126 different sequential patterns with a support threshold of 100*i.e. filtering out patterns included in less than 100 sequences.

How to know what that means?We need background information about the courses (DD100, DSE212, ED209 ,etc.)

Sequential pattern Support

(DD100) (DSE212) 232

(DSE212) (ED209) (DD303) 150

(B120) (B201) 122

Examples:

The approach to interpretation:

Building a navigation structure in the patterns using dimensions obtained in linked data

Making the results linked data compliantUse a simple ontology of sequences to represent the patterns

And use linked data URIs to represent the items, e.g. DSE212 http://data.open.ac.uk/course/dse212

Selecting a dimension in linked dataPropose relations that apply to the items of the patterns

Then relations that apply to the objects of these relations

Etc.

i.e. follow the links to build a chain of relationships.

Building a hierarchy of patterns

The end-values of the chain of relations built out of following links of linked data form attributes of the patterns

Build a lattice (hierarchy) of concepts representing groupings of these attributes, using formal concept analysis

Exploring the hierarchy

Benefits (see following examples)

Provides an overview of the patterns obtained along a custom dimension

Helps identifying gaps and issues in the original data/process

Helps identifying areas in need of further exploration

Generic: can be straightforwardly applied to other source data, other linked data and other mining methods

Generalisation of the subjects

Examples

• Subjects of books

Subjects of related course material

Examples

Assessment method

DiscussionLimitations of the approach:

– Requires the results to be linked data and the items to connect to linked data

– Sources of linked data needs to be available to support interpretation)

http://data.linkededucation.org/linkedup/catalog

Discussion: It’s a loop

Data (from some education

related system)

mining

Interpretation Background knowledge

Views and dimensionsData selection

Conclusion

Linked data can be used to enrich and bring some meaningful structure to the patterns from an analytics/mining process

Introducing linked data not only in input of the process, but also in support of more analytical tasks

Promising, considering the growth of education-related linked data

Should become part of an iterative process, where patterns and data get refined through interpretation and the introduction of background information from linked data

Thank you!

More info at:http://mdaquin.net @mdaquin

http://linkedup-project.eu http://linkedup-challenge.org

http://linkedu.eu/event/lak2013-linkeddata-tutorial/