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).
1
Leighton, H. Vernon. Course Analysis: Techniques and Guidelines. Journal of Academic
Librarianship, May 1995. pp. 175-179.
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
COLLECTION GOAL [GL] INDICATORS ASSIGNED
Course LC Subject
Heading
Artificial intelligence
LCCS No
Q334
Associative storage
TK7895.M4
Asynchronous transfer
mode
TK5105.35
Back Propagation
(Artificial intelligence)
Q325.78
C++ (Computer
program language)
QA76.73.C
Cache memory
TK7895.M4
Calculus
Calculus, Integral
QA300 QA316
QA308 QA311
Client / server
computing
QA76.9.C55
Client/server computing
QA76.9.C55
Coding theory
QA268.5
Conspectus Category
or
Subject
COM2 Artificial
Intelligence
COM66.5 Computer
Engineering, Computer
Software
TEC156
Telecommunication
(General)
COM0.44 Machine
Learning,
COM0.6 Cybernetics
COM14 Programming
Languages
COM66.5 Computer
Engineering, Computer
Hardware
MAT61 Mathematical
Analysis (General)
MAT62 Calculus,
Functional Analysis
COM36 Client/Server
Computing
COM36 Client/Server
Computing
COM59 Machine
Theory – Coding Theory
WITH APPROPRIATE
Course Level 1
(Undergraduate) /
Department
Course Level 2
(Postgraduate PhD) /
Department,
Research
Interest
Appl.Inf.
M.I.S. / PhD
Appl.Inf.
Appl. Inf.
Collection
Goal
(GL)
4
3b
M.I.S.
3c
Appl. Inf.
3b
Appl. Inf.
3b
Appl. Inf.
3b
Appl. Inf.
3a
Appl. Inf.
3a
Appl. Inf.
3b
Appl.Inf.
3a
Appl.Inf.
3a