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Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions Presentation at the LAK 2013 conference - 10-04-2013
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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
Nicolas JayUniversité de Lorraine, LORIA,
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
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/
