Linking of Greek Academic Libraries Action 3: rationalistic development of academic libraries’



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STRATEGIC PROGRAM FOR THE EDUCATION AND THE INITIAL PROFESSIONAL TRAINING

PROJECT:


Linking of Greek Academic Libraries

Action 3:

RATIONALISTIC DEVELOPMENT OF ACADEMIC LIBRARIES’

SCIENTIFIC JOURNALS COLLECTIONS
CONSPECTUS APPLICATION

GUIDELINES No 5




USER NEEDS ANALYSIS

For locating users’ needs the application of the “Course Analysis” method is suggested. “Course Analysis takes information about courses (or research projects) and represents it in a usable form. To analyze courses, librarians first obtain the information by reading course catalog listings, studying syllabi and reading lists, or interviewing teaching faculty. They then develop a course description, which is usually a list of expressions in one of these formats: call numbers, subject headings, or Boolean keyword expressions”1


For the project’s needs information can be gathered in the following way:

  • From Students’ Guides, where a detailed description of the under- and postgraduate courses offered by an institution is usually given.

  • From web sites of the relevant Departments containing analytical descriptions of courses, research programs, applications of departmental laboratories and faculty areas of interest.

  • From reading lists (where available).

  • From descriptions of research interests of Ph.D. candidates.

  • Through contacts with faculty and researchers.

All the information gathered would be transformed into relevant LC Subject Headings. Then, for each subject heading one or more relevant LC Classification System call numbers will be attributed. It is highly important that the subject analysis of the information on courses and research or faculty interests is as detailed as possible so that the result would represent thoroughly the needs that the libraries’ collections should cover (See below EXAMPLE 1:SUBJECT ANALYSIS OF COURSES).


It is advisable that all data regarding the corresponding course level (under- or postgraduate) or research interest(s) is accompanying each subject heading. This will facilitate the assignment of Conspectus Collection Goal Indicators (GL), by showing in a direct way the depth and breadth that a library’s collection should have in order to cover users’ needs for the specific subject heading. (See below EXAMPLE 2: DETAILED COURSE ANALYSIS WITH APPROPRIATE COLLECTION GOAL [GL] INDICATORS ASSIGNED).

EXAMPLE 1: SUBJECT ANALYSIS OF COURSES

Course 1: SIMULATION TECHNIQUES


Course Description: The course presents the principles of system modeling and the simulation techniques used for the evaluation of systems. Topics include: system characteristics, types of system models, world view and time advance mechanisms, computer simulation algorithms and methodology, random number generation, analysis of simulation languages and tools, Monte Carlo simulation, analysis of simulation output, models validation and verification.
Derived Subject Headings

  • Analog computer simulation

  • Digital computer simulation

  • Hybrid computer simulation

  • Virtual reality

  • Random number generators

  • Computer programs – Validation

  • Computer programs – Verification

  • Computer simulation

  • Digital computer simulation

  • Monte Carlo method

  • Simulation methods

Course 2: ARTIFICIAL INTELLIGENCE


Course Description: Basic concepts, computers and artificial intelligence. Basic concepts, knowledge representation, logic-based representation, problem solving as searching, search algorithms, semantic networks, logic, production systems, objects/frames, declarative versus procedural. Artificial intelligence languages: Prolog, Lisp. Search and computational complexity in artificial intelligence systems.
Derived Subject Headings

  • Artificial intelligence

  • Lisp (Computer program language)

  • Prolog (Computer program language)

  • Fifth generation computers

  • Neural computers

  • Knowledge representation (Information theory)

  • Question-answering systems

  • Semantic networks

  • Frames (Information theory)

  • Declarative programming

  • Programming languages (Electronic computers)

  • Logic programming

  • Computational complexity

EXAMPLE 2: DETAILED COURSE ANALYSIS WITH APPROPRIATE COLLECTION GOAL [GL] INDICATORS ASSIGNED




Course LC Subject Heading

LCCS No

Conspectus Category

or

Subject

Course Level 1 (Undergraduate) / Department

Course Level 2 (Postgraduate - PhD) / Department,


Research Interest

Collection Goal (GL)




Artificial intelligence

Q334

COM2 Artificial Intelligence

Appl.Inf.

M.I.S. / PhD Appl.Inf.

4

Associative storage

TK7895.M4

COM66.5 Computer Engineering, Computer Software

Appl. Inf.




3b

Asynchronous transfer mode

TK5105.35

TEC156 Telecommunication (General)




M.I.S.

3c

Back Propagation (Artificial intelligence)

Q325.78

COM0.44 Machine Learning,

COM0.6 Cybernetics

Appl. Inf.




3b

C++ (Computer program language)

QA76.73.C

COM14 Programming Languages

Appl. Inf.




3b

Cache memory

TK7895.M4

COM66.5 Computer Engineering, Computer Hardware

Appl. Inf.




3b

Calculus

QA300 - QA316

MAT61 Mathematical Analysis (General)

Appl. Inf.




3a

Calculus, Integral

QA308 - QA311

MAT62 Calculus, Functional Analysis

Appl. Inf.




3a

Client / server computing

QA76.9.C55

COM36 Client/Server Computing

Appl. Inf.




3b

Client/server computing

QA76.9.C55

COM36 Client/Server Computing

Appl.Inf.




3a

Coding theory

QA268.5

COM59 Machine Theory – Coding Theory

Appl.Inf.




3a




1 Leighton, H. Vernon. Course Analysis: Techniques and Guidelines. Journal of Academic Librarianship, May 1995. pp. 175-179.


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