The Python Language Reference - New Mexico Institute of Mining and
Reference Question Data Mining
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Transcript of Reference Question Data Mining
![Page 1: Reference Question Data Mining](https://reader036.fdocuments.net/reader036/viewer/2022062303/55397c864a79595b7a8b49d0/html5/thumbnails/1.jpg)
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Traditional Reference Classification
• Directional (Where is the catalog?)
• Ready reference (How tall is Mt. Everest?)
• Specific-search question (Where can I find information about the progressive movement in Louisiana?)
• Research (lengthy detailed assistance)
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Strengths
• Analyzes workflow
• Provides statistical comparison with peer organizations within the state
• Provides statistical comparison to evaluate national trends in reference services
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Weakness
• Statistics reveal nothing about the content of the question
• This “reference data” is equally important as statistical compilation
• The reference literature contains no effective means of “mining” this data.
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GOALS
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Goals
• The creation of subject guides to address popular topics
• Target specific courses for library instruction
• Provide further data to aid in collection development
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Methodology
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Flow Diagram
Record Data
Clean Data
Classify Data
Collate Data
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Recording Data
• After each reference interview the librarian records:
Subject of question Class for which the information was sought Professor’s name
• Example: hair styles in the 1950s – ENG 102 / Costello
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Recording Data
• It is the responsibility of each reference librarian to narrow the topic during the reference interview.
• Broad: “Decades”
• Narrow: “hairstyles” “1950s”
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Recording Data
• Key words from the recorded questions come from the reference interview.
• Emphasis on recording the question and subsequent key words.
• Questions without recorded classes are usually identified as “community.”
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Cleaning Data
• Sheets of reference data are collected monthly.
• Entries with incomplete, directional, or indiscernable data are excluded.
• Example: Directions to Starbucks
• Example: Business Week? Citations?
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Classifying Data
• Entries are correlated to a call number range for each subject using Alice (Ohio University’s Library Catalog) and the Library of Congress Classification scheme
• Matching of relevant hits to call numbers is done at the discretion of librarian performing the classification
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Classifying Data
• Data is correlated in ALICE (Ohio University’s Library Catalog)
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Collating Data
• Subject headings and class numbers are compiled into an Excel spreadsheet.
• Professors names are not included in the compilation of the excel spreadsheet.
• Data is graphed visually for general overview.
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Outcomes
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Study Guides
Reference Data
Study Guides
Reference Department
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Study Guides
• First Subject Guide was “Utopia.”
• We now have nine subject guides directly related to our data mining and instruction initiatives.
• Agriculture, Decades, Environmental Science, Nursing, and so forth.
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Outreach & Instruction
Reference Data
Instruction and Outreach
InstructionLibrarian
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Outreach & Instruction
• Names of professors, courses, and related data are sent to the instruction librarian for outreach
• Example – Louisiana History / Professor Allured
• Almost one third of the student cap in her class are needing assistance at the Reference Desk.
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Collection Development
Reference Data
CollectionDevelopment
CollectionManager
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Collection Development
• Monthly reports are sent to the Collection Manager
• Data is a component of the selecting process
• To date, the library has added several reference books and databases to the collection based on reference data mining
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Reference Data
Study GuidesCollection
DevelopmentInstruction
and Outreach
Reference Department
InstructionLibrarian
CollectionManager
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Biggest Obstacle to Implementation
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Joshua FinnellAssistant Professor of Library ScienceReference LibrarianMcNeese State University
Walt FontaneAssistant Professor of Library ScienceReference LibrarianMcNeese State University