Towards Automatic Evaluation of Learning Object Metadata
Quality
Xavier Ochoa, ESPOL, Ecuador
Erik Duval, KULeuven, Belgium
QoIS 2006
Learning Objects are …
Any entity, digital or non-digital, that can be used, re-used or
referenced during technology-supported learning.
IEEE LOM Standard
Learning Object Metadata
Learning Object Metadata Standard
Initial growth has been slow
ARIADNE
Standardization, Interoperability of Repositories and Automatic Generation of
Metadatahad solved the scarcity
problem…
…but had created new “good” ones.
The production, management and consumption of Learning
Object Metadata is vastly surpassing the human capacity to
review or process these metadata.
Currently there is NOT scalable Quality Evaluation
of Learning Object Metadata
Quality of Metadata
"high quality metadata supports the functional requirements of the system it is designed to support"
(Guy at al, 2004)
Quality of Metadata
Title: “The Time Machine”Author: “Wells, H. G.”Publisher: “L&M Publishers, UK”Year: “1965”Location: ----
Quality of Metadata
Quality of Metadata
Why Measuring Quality?
• The quality of the metadata record that describes a learning object affects directly the chances of the object to be found, reviewed or reused.
• An object with the title “Lesson 1 – Course 201” and no description, could not be found in a “Introduction to Java” query, even if it is about that subject.
How to measure Metadata Quality?
• Manually check a statistical sample of records to evaluate their quality. – Use graphical tools to improve the task
• Use simple statistics from the repository
• Usability studies
Metrics
• A good system needs both characteristics:– Been mostly automated– Predict with certain amount of precision the fitness of
the metadata instance for its task
• Other fields had attacked similar problems through the use of metrics– Software Engineering– Bibliographical Studies (Scientometrics)– Search engines (Eg.: PageRank)
We cannot measure the quality manually
anymore…
…but is a good idea to follow the same
quality characteristics.
Quality Characteristics
• Framework proposed by Bruce and Hillman:– Completeness– Accuracy– Provenance– Conformance to expectations– Consistency & logical coherence– Timeliness– Accessability
Our Proposal: Use Metrics
• Small calculation performed over the values of the different fields of the metadata record in order to gain insight on a quality characteristics.
• For example we can count the number of fields that have been filled with information (metric) to assess the completeness of the metadata record (quality characteristic).
Quality Metrics
• Completeness– Simple Completeness:
• What percentage of the fields has been filled
– Weighted Completeness: • Not all fields are equally important. Use a
weighted sum.
Quality Metrics
• Conformance to Expectations– Nominal Information Content:
• How different is the value of field in the metadata record from the values in the repository (Entropy)
– Textual Information Content: • What is the relevance of the words
contained in free text fields (TFIDF)
Quality Metrics
• Accesability– Readability:
• How easy is to read the text of free text fields.
Quality Metrics
Evaluation of the Metrics
• Online Experiment:– http://ariadne.cti.espol.edu.ec/Metrics
• 22 Human Reviewers
• 20 Learning object metadata records – (10 manual, 10 automated)
• 7 characteristics used for evaluation• 5 quality Metrics
Evaluation ResultsTextual Information Content correlates highly
(0.842) with human-assigned quality score
Analysis of Results
• The quality of the title and description is perceived as the quality of the record.
• One of the metrics captured a complex human evaluation.
• This artificial measurement of quality is not an effective evaluation for the metrics
Applications:Repository Evaluation
Applications:Quality Visualization
Automated Evaluation of Quality
Average Grade
0
0,5
1
1,5
2
2,5
3
3,5
4
Comple
tnes
Accur
acy
Prove
nanc
e
Confo
rman
ce
Coher
ence
Timeli
ness
Acces
ibility
Quality Parameter
Qu
alit
y V
alu
e (
0 -
6)
AutomatedManual
Further Work
• Evaluate metrics as predictors of “real” quality.
• Quality as Fitness to fulfill a given purpose– Quality for Retrieval – Quality for Evaluation – Accessibility Quality
– Re-use Quality
Further Work
• But more important… Measure the Quality of the Learning Object itself
• LearnRank– Analysis of the Object itself– Analysis of Contextual Attention Metadata– Social Networking
• Learnometrics– Measuring the Impact of Learning Object in
the Learning/Teaching Community
Thank you, Gracias
Comments, Suggestions, Critics… are Welcome!
More Information:http://ariadne.cti.espol.edu.ec/M4M