Title: Dublin Core Audience Elements
1Dublin Core Audience Elements
- A Study of a Data Analysis Technique for
Controlled Vocabulary Interoperability
2Overview
- Problem
- Assumptions/Approach
- Pilot Study
- The Card Sort
- Some Results
- Recommendations
3Problem
4Problem
- Dublin Core Education Working Group currently
investigating the possibility of a "high level"
language for use in vocabulary recommendation for
the Education Working Groups application profile
that uses the Dublin Core Metadata Initiative
(DCMI) audience element.
5Problem
- It is necessary that the Working Group constructs
such a vocabulary by working with existing
vocabularies in well-established projects serving
the education community. - The working group has seven sets of audience type
terms (see below), from seven different
international controlled vocabularies. These
terms will be used in vocabulary work in Dublin
Core Education Working Group
6Problem (vocabularies)
- Education Network Australia
- European Treasury Browser
- Gateway to Educational Materials
- Instructional Management Systems
- Australian Government Locator Service
- UK National Curriculum
- U.S. Department of Education
7Problem (37 Audience Types)
- Adult Educators
- Alumni
- Animateur
- Author
- College/University Instructors
- Counselors
- Curriculum Supervisors
- Dropouts
- Educational Administration
- Educationalists
- Families
- Graduates
- Guidance Officer
- Inspector
- Learner
- Librarians
- Manager
- Managerial Staff
- Media Specialists
- Non-teaching Staff
- Political Decision Makers
- School Aides
- School Doctor
- School Leadership
- School Nurses
- School Personnel Workers
- School Psychologists
- School Publisher
- Speech Therapist
- Stopouts
- Students
- Teacher Interns
- Teachers
- Technology Coordinators
- Trainer
- Tutors
- Vocational Educators
8Assumptions/Approach
9Assumptions/Approaches
- Assumption in order to get a more user-centered
view of a high-level vocabulary look at users - Assumption sorting will provide insight into
basic facets of audience from terms given to users
10Assumptions/Approaches
- Both the pilot study and the study used card
sorting, to see how non-expert users 1) sorted
terms in to relationships, and 2) what their
thought process was in sorting these terms.
11Assumptions/Approaches
- to generate some data that might inform how the
Education Working group could begin this
vocabulary work - wysiwyg we must sort using terms from seven
already extant controlled vocabularies
12Assumptions/Approaches
- The Card Sort Approach
- Card sorting, or just sorting, is used by various
disciplines to examine how individuals organize a
given set of cards.
13Assumptions/Approach
- Sorting is a tool that can be used to help inform
the design of online displays (Carlyle, 2001). - The high level vocabulary will display the
terms in relationship to one another to allow for
interoperability sorting may help inform this
14Pilot Study
15The Pilot Study
- 9 Participants
- All non-experts
- Sorted 37 cards
- Talk-aloud (think-aloud) protocol used
- Observation used
- Once cards sorted asked to label groups
16Results Pilot Cluster Analysis
- Cluster Analysis
- Descriptive Technique
- Looks for similarities or dissimilarities across
the data for the 9 participants - Generates an analysis of the structure
- Used Wards Method provided by SPSS
dissimilarity matrix formed, squared Euclidean
distance used
17Results Pilot Cluster Analysis
- Group C
- Inspector
- School Leadership
- Managerial Staff
- Manager
- School Personnel Worker
- Curriculum Supervisor
- Non-teaching Staff
- Technology Coordinator
- Group D
- Guidance Officer
- Speech Therapist
- School Nurses
- Counselors
- School Psychologists
- School Doctor
- Group A
- Animatuer
- Media Specialists
- School Aides
- Librarians
- Group B
- Educational Administration
- Political Decision Makers
- School Publisher
- Author
- Group E
- Graduates
- Learner
- Alumni
- Stopouts
- Dropouts
- Students
- Families
- Group F
- Teachers
- Trainer
- College/University Instructors
- Educationalists
- Tutors
- Teacher Interns
- Vocational Educators
- Adult Educators
18Results Pilot Cluster Analysis
19Results Talk aloud and observation
- It was clear that the participants wanted to
construct a meaning for a term in relationship to
the other terms available. Often the participant
was confused, expressed this confusion, and made
decisions without certainty.
20Results Talk aloud and observation
- Across all participants common phenomenon was
observed. Each of the participants laid the
entire set of cards out before them, and from
this undivided universe of 37 cards began the
process of sorting.
21Results Talk aloud and observation
- Another common process in this sorting task was a
vacillation between sorting from the bottom up
and sorting from the top down. Some categories
grow from lumping like things. Others were made
from dividing the unlike groups of terms, then
further dividing. However, neither approach
(top-down nor bottom-up) was used exclusively by
any of the participants.
22Card Sort II
23Card Sort II
- 21 participants
- Convenient sample (self-selected from the
population) of both experts and non-experts - Sorted 37 cards
- Talk-aloud (think-aloud) protocol used
- Observation used
- Once cards sorted asked to label groups
- Then drew a concept map illustrating their
understanding of the cards sorted
24Card Sort II
- It is clear that there is more variation in the
card sort behavior than observed before - And though there is more variation in activity in
the sorting task, there is not necessarily a wide
variety in the resulting structure
25Card Sort II
- That is, by eyeballing the data there seems to be
little variation between the pilot card sort
groups and the groups from the second card sort.
26Some Results
27Some Results
- Comparison of the concept maps
- Among the 21 participants
- Titles of Groups
- Against Pilot Data
28Some Results
- By counting the frequency of names we get seven
major groups - Administration
- Libraries
- Learners/Students
- Families
- Educators/Teachers
- Non-Teaching Staff
- Dont Know/??
29Some Results - Titles of Groups
- Administration - 21, 9, 5, 2 (17, 15, 10, 3)
- Libraries - 18, 9, 7, 5 (15, 11, 2)
- Learners/Students - 16, 15, 13, 12, 11, 5, 21,
17, 10, 9, 8, 6, 2 (everyone) - Families - 21, 18, 16, 15, 9, 5, 2, 1
30Some Results - Titles of Groups
- Educators/Teachers - 19, 16, 15, 14, 10, 3, 13,
12, 9, 8, 7, 6, 5, 2 (11, 17) - Non-Teaching Staff - 16, 13, 12, 10 (everyone)
- Dont Know/??? - 14, 13, 11, 7 (not everyone)
31Results Pilot Cluster Analysis
- Group C
- Inspector
- School Leadership
- Managerial Staff
- Manager
- School Personnel Worker
- Curriculum Supervisor
- Non-teaching Staff
- Technology Coordinator
- Group D
- Guidance Officer
- Speech Therapist
- School Nurses
- Counselors
- School Psychologists
- School Doctor
- Group A
- Animatuer
- Media Specialists
- School Aides
- Librarians
- Group B
- Educational Administration
- Political Decision Makers
- School Publisher
- Author
- Group E
- Graduates
- Learner
- Alumni
- Stopouts
- Dropouts
- Students
- Families
- Group F
- Teachers
- Trainer
- College/University Instructors
- Educationalists
- Tutors
- Teacher Interns
- Vocational Educators
- Adult Educators
32Some Results Concept Maps
Participant 2
B
Politicians, school board, administration
Students and Families
teachers
Information people
E
F
A
Professionals who are not teachers
C D
33Some Results Concept Maps
B
Oversight
Administration
Students
Families
E
Curriculum development
Staff
A C
Educators/Teachers
Student Services
D
F
Participant 21
34Some Results Concept Maps
Teaching Roles
F
Government Roles
Administrative Roles
B C?
Students Affiliates
E
Teaching Support Roles
Educational Resources
A?
D
Participant 17
35Results Concept Maps
Teaching Roles
manage
Government Roles
Administrative Roles
instruct
influence
Students Affiliates
use for instruction
manage
use for learning
Teaching Support Roles
Educational Resources
Instruct with
Participant 17
36Results Pilot Cluster Analysis
- Group C
- Inspector
- School Leadership
- Managerial Staff
- Manager
- School Personnel Worker
- Curriculum Supervisor
- Non-teaching Staff
- Technology Coordinator
- Group D
- Guidance Officer
- Speech Therapist
- School Nurses
- Counselors
- School Psychologists
- School Doctor
- Group A
- Animatuer
- Media Specialists
- School Aides
- Librarians
- Group B
- Educational Administration
- Political Decision Makers
- School Publisher
- Author
- Group E
- Graduates
- Learner
- Alumni
- Stopouts
- Dropouts
- Students
- Families
- Group F
- Teachers
- Trainer
- College/University Instructors
- Educationalists
- Tutors
- Teacher Interns
- Vocational Educators
- Adult Educators
37Recommendations
38Recommendations
- We have two results from this study
- Data Collection Technique
- First Facets
- These results allow me to make some
recommendations
39Recommendations
- Data Analysis Technique
- Implement a web-based card sort tool to keep
gathering data - Cooperate with Stór Curam in Scotland (Sarah
Currier and Crawford Revie) have tool that
theyre tweaking - or someone else - But add search tasks to refine our understanding
of this vocabulary and its utility
40Recommendations
- First Facets
- Start building facets around groups like these,
using these groups as hypotheses - Use facets for switching at high-level while
retaining detail in individual collections - In other words, use facets as display feature
that helps user navigate, start basic
interoperability, but not as the aid for retrieval
41Acknowledgements
- Stuart Sutton, Nancy Morgan, Keith Stubbs, DCED,
and the participants.
42References
- Carlyle, A. (2001). Developing organized
information displays for voluminous works a
study of user clustering behavior. In
Information Processing and Management, 37
677-699
43References cont.