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Dublin Core Audience Elements

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We have two results from this study: Data Collection Technique 'First Facets' These results allow me to make some recommendations. Recommendations ... – PowerPoint PPT presentation

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Title: Dublin Core Audience Elements


1
Dublin Core Audience Elements
  • A Study of a Data Analysis Technique for
    Controlled Vocabulary Interoperability

2
Overview
  • Problem
  • Assumptions/Approach
  • Pilot Study
  • The Card Sort
  • Some Results
  • Recommendations

3
Problem
4
Problem
  • 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.

5
Problem
  • 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

6
Problem (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

7
Problem (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

8
Assumptions/Approach
9
Assumptions/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

10
Assumptions/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.

11
Assumptions/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

12
Assumptions/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.

13
Assumptions/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

14
Pilot Study
15
The 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

16
Results 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

17
Results 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

18
Results Pilot Cluster Analysis
19
Results 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.

20
Results 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.

21
Results 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.

22
Card Sort II
23
Card 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

24
Card 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

25
Card 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.

26
Some Results
27
Some Results
  • Comparison of the concept maps
  • Among the 21 participants
  • Titles of Groups
  • Against Pilot Data

28
Some Results
  • By counting the frequency of names we get seven
    major groups
  • Administration
  • Libraries
  • Learners/Students
  • Families
  • Educators/Teachers
  • Non-Teaching Staff
  • Dont Know/??

29
Some 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

30
Some 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)

31
Results 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

32
Some 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
33
Some Results Concept Maps
B
Oversight
Administration
Students
Families
E
Curriculum development
Staff
A C
Educators/Teachers
Student Services
D
F
Participant 21
34
Some Results Concept Maps
Teaching Roles
F
Government Roles
Administrative Roles
B C?
Students Affiliates
E
Teaching Support Roles
Educational Resources
A?
D
Participant 17
35
Results 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
36
Results 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

37
Recommendations
38
Recommendations
  • We have two results from this study
  • Data Collection Technique
  • First Facets
  • These results allow me to make some
    recommendations

39
Recommendations
  • 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

40
Recommendations
  • 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

41
Acknowledgements
  • Stuart Sutton, Nancy Morgan, Keith Stubbs, DCED,
    and the participants.

42
References
  • Carlyle, A. (2001). Developing organized
    information displays for voluminous works a
    study of user clustering behavior. In
    Information Processing and Management, 37
    677-699

43
References cont.
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