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

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1,5

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2,5

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3,5

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Comple

tnes

Accur

acy

Prove

nanc

e

Confo

rman

ce

Coher

ence

Timeli

ness

Acces

ibility

Quality Parameter

Qu

alit

y V

alu

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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