data needs in academic planning: challenges and the way forward by dr. wilfred a. iguodala director,...
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DATA NEEDS IN ACADEMIC PLANNING: CHALLENGES AND THE WAY FORWARD
By
DR. WILFRED A. IGUODALADirector, Academic Planning,
University of Benin, Benin City, Nigeria. Email: [email protected]
1.0 INTRODUCTION:The discussions in this paper would be centred on the Data the Academic Planning Officer requires in the performance of his/her duties. Therefore, the type, sources, methods, time and purposes of data collection are discussed. Also, some data analysis/presentation techniques, data storage and retrieval methods are considered. In addition, some of the challenges of data collection, analysis, storage and management are given some consideration. Finally, the paper contains some recommendations that would enhance the competence and quality of Academic Planning Officers in Nigerian Universities as well as facilitate the data management enterprise in our institutions.
2.0 DEFINITION OF DATAThe term data refers to qualitative or quantitative attributes of a variable or set of variables. They are typically the results of measurements and can be the basis of graphs, images or observations of a set of variables. They are often viewed as the lowest level of abstraction from which information and, then knowledge are derived. Data on its own carries no meaning. For data to become information, it must be interpreted and take on a meaning. For example, the total number of students in a Department can generally be considered as “data”, the components or characteristics of that figure may be considered as “information”, and a report containing the movement/flow of students from one level to the other and on the efficiency of the system may be considered as “knowledge”.
3.0 TYPES OF DATA
The data types considered in this presentation are those relating to students, staff, finance, facilities, research, staff development efforts, library, health centre (welfare), students accommodation and the pre-University preparatory staff schools.
4.0 DATA COLLECTIONThere are two main sources of data collection viz:-
4.1 Primary Sources:
These are data collected through research efforts; they are obtained directly from the field by the use of questionnaire, survey or other research instruments designed to obtain specific information about the system. For instance, the desire to have relevant information on: the number of students in the University by programmes, level of course, sex, state of origin, etc; or the utilization of available lecture theatres/halls, laboratories, studios/workshops in the institution, or the desire to obtain information on the perception of students on the effectiveness of their lecturers, etc would only be obtained by the use of instruments designed for the specific purpose.
4.2 Secondary Sources:
These are published and existing materials from which relevant data or information on an institution could be extracted or obtained depending on the interest of the investigator. For instance, the published Statistical Digest of any institution, would be a secondary source of data/information to other persons desiring to have information on that institution. Also, the annual reports institutions present to the NUC during USARM meetings fall into this category.
5.0 METHODS OF DATA COLLECTIONAcademic Planning Officers adopt several means of collecting data from the different units of the institution or designated institutions/agencies. These include:5.1 The design of formats to reflect the nature of
data required.5.2 Structured Interview/Interactions with some
Members of University Community.5.3 The Use of Questionnaire.5.4 Direct Access to the Website of Institutions,
Organizations, and Agencies5.5 Physical Observation5.6 Vital Registration
6.0 DATA COLLECTION FORMATSDifferent formats are usually employed in the collection of the relevant data relating to the broad categorization made in the preceding section. These formats, their usefulness and time of data collection shall now be examined.
6.1 Student Data(a) The Admission Data – These are data on new intakes into the institution through UME, Direct Entry, Postgraduate and Sub-Degree levels. Such data could be aggregated by Faculty/Department, sex, level of course and state of origin as in the formats hereunder:
(i) Analysis of New Entrants by Faculty/Dept, Level of Course and Sex, 2011/2012 Session
(ii) Distribution of New Entrant by Faculty, State of Origin, Sex and Level of Course 2011/2012 Session
Faculty/ Dept/
Institute
Undergraduate Sub-Degre
e
Postgraduate Total
UMEDirec
t Total Master
s Ph.D Dip Total
M F M F M F M F M F M F F M M F M F
Total
States Level of Course Faculty/School
Agric Arts Educ etc
M F M F M F
e.g. Abia Undergraduate Postgraduate Sub-Degree
Sub-Total
(iii) New Intake by Quota, Admission and Clearance by Faculty/Dept.
Faculty/Dept Quota Admission Clearance
e.g. AgricAnimal Science
Total
(b) Student Enrolment Data:(i) Total Student Enrolment by Faculty/Dept, Level of
Course, Sex 2011
(ii) Distribution of Total Students by Faculty, Sex and State of Origin
Faculty/Dept
Undergraduate Sub-Degree
Postgraduate Total
100 200 300 400 …Master
s Ph.D Dip Total
M F M F M F M F M F M F M F M F F M M F M F
Sub Total
Faculty/Dept Level of Course
Abia Adamawa Akwa-Ibom etc
M F M F M F
e.g. Science Biochemistry
Undergraduate
Postgraduate
Sub-Degree
Sub-Total
Usefulness:
• Knowledge of these data would be useful in the following ways:
• Show clearly the number of students in the different disciplines by level, gender and state of origin.
• They would assist the University in its internal recurrent budgetary allocations to Departments.
• They could assist in the future projection of students in the institutions.
(c) Student Course Registration Data
Faculty/ Department
Level of Course
Headcount Enrolment by Level
Course & Code by
Level
Credit Unit per Course
No. of Students
Registered per Course including students
from other Department
s
Average Load of
each level by Session
e.g Science Chemistry
100 120 CHEM101CHEM102CHEM103etc
323
etc
250200320etc
40
200
The course registration format enables the planning officer to observe at a glance course offered by students by Department and the credit loads taken in each semester
=
Note: Average credits registered for in a year by own Department students.
Total credit his registered by all studentsTotal headcount enrolment of same level
6.2 Staff Data:Various formats could be developed to collect data on staff. We are too familiar with the NUC formats on this subject matter. Some of the formats include the following:
(i) Full-Time Academic Staff by Function, Nationality, Sex and Rank
Faculty/Dept./ Unit
Prof/Assoc
Prof / Lib/ Dep Lib
Snr Lect/Snr
Res Fellow/ Prin Lib
Lecture Res
Fellow Lib I
Asst Lect/ Jnr Res Fellow
Grad Asst/ Lib II
Total
Nign
NN Nign
NN Nign
NN Nign
NN Nign
NN Nign
NN
M F M F M F M F M F M F M F M F M F M F M F M F
Total
(ii)Total Staff by Function, Grade and Sex……………………Session
Salary Grade
Teaching/Other
Academics
Senior Tech Staff
Senior Admin Staff
Junior Tech Staff
Junior Non-Tech Staff
Secretarial Staff
Total
M F T M F T M F T M F T M F T M F T M & F
VC & Ex VCs15...1
Total
Grand Total
(iii) Staff Position by Function, Grade Level and Sex (Staff Schools)
Salary Grade Level
Teaching Staff Non Teaching Total
Senior Junior Sub Total
Senior Junior Sub Total
M F M F M F M F M F M F M F
15...1
Sub Total
Total
(iv) Student Enrolment in Staff Schools:
Arms Junior Secondary Senior Secondary Total Jnr & Snr Sec.
I II III Sub Total
I II III Sub Total
M F M F M F M F M F M F M F M F M F
Total All Arms
6.3 Financial Data:The financial data are useful in the system as they • Show the pattern and trends in financial allocations and
expenditures by units of allocations/expenditures in the University.• Act as guide in financial allocations to units in relation to NUC
guidelines.• Could facilitate the determination of the unit cost per student
especially when annual expenditure is related to the total student enrolled for their session.
• Allow financial allocations and comparisons to be made between academic sessions.
• Facilitate the determination of the proportion of each Faculty in the financial allocations and expenditures of every academic year.
• Enable sources of funds to be easily ascertained.• Provide information on the total funds available for what activity in
any particular financial year.
(i) Budget Structure and Expenditure Analysis 2000/2001 – 2001/2002
Resource Allocation Units
(a)
2000/2001 2001/2002
Allocation(b)
(%) Expenditure(c)
(%) Allocation(d)
(%) Expenditure(e)
(%)
(a) Academic (b) Administration(c) Teaching Support(d) Services (General)(e) Student Services(f) General Expenditures
Total
Year Capital
% Recurrent % Teaching &
Research
% IGR % Other Special Grants*
% Total
Faculty Academic Sessions
e.g 2001/2002 2002/2003 2003/2004
e.g Science
Total
(ii) Budget Structure by Faculty 2001/2002 – 2003/2004
(iii) Financial Grants/Income to the University: 2000/2001
6.4 Research Effort Data6.5 Space Inventory and Utilization Data
6.6 Library Data:
6.7 Results Data:
(i) Analysis of Degree Results by Faculty/ Department, Award of Class of Degree, Sex
Faculty/Dept. First Degree Higher Degree Total
1st Class
2nd Class Uppe
r
2nd Class Lowe
r
3rd Class
Pass Unclassified Pass
Sub Total
Masters Ph.D Sub Total
M F M F M F M F M F M F M F M F M F M F M F
Total
(ii) Analysis of Sub-Degree and Post-Graduate Diploma Final Year Results by Subject Area
6.8 Health Centre Data6.9 Student Accommodate Data
Faculty/Institute/ Depts.
Post Graduate Diploma Sub-Degree Total PG Dip & SD
Grand Total
PG Dip & SD
Class of Pass Sub Total
Class of Pass Sub Total
Distn Credit
Merit Pass Distn Credit
Merit Pass
M F M F M F M F M F M F M F M F M F M F M F M F
Total
7.0 TIME OF DATA COLLECTIONHaving discussed the data needs of the Academic Planning Officer for his/her tasks and assignments, the next issue to address is when and how should these data be collected in the institutions. We shall jointly discuss this section with a view to arriving at a consensus that would represent the position of CODAPNU.– Student Data– Staff Data– Result Data– Financial Data– Facility Data– Course Offering Data– Data on Research and Staff Development Efforts etc.
8.0 DATA ANALYSIS Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggestion, conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing adverse techniques under a variety of names in different business, science and social science domains. Examples include data mining technique, data integration technique and data cleaning technique.
Meanwhile, there are several statistical packages for data analysis as listed hereunder. But the choice of a statistical package should not depend on the complexity and elegance of a package but rather should depend on simplicity and meeting the needs of the user.
• Aabel – Graphic display and plotting of statistical data sets.• ADAPA – bath and real-time scoring of statistical models.• ASReml – for restricted maximum likelihood analysis.• BMDP – general statistical package.• CalEst – general statistics and probability package with didactic.• Data Applied – for building statistical models.• DPS – comprehensive statistics package.• EViews – for econometric analysis.• FAME – a system for managing time series statistics and time series database.• GAUSS – programming language for statistics.• SHAZAM – comprehensive econometrics and statistics package.• SigmaStat – for group analysis.• SOCR – online tools for teaching statistics and probability theory.• SPSS – comprehensive statistics package.• Stata – comprehensive statistics package.• Statgraphics – general statistics package.• STATISTICA – comprehensive statistics package.• StatXact – package for exact non parametric and parametric statistics.• Systat – general statistics package.• S-PLUS – general statistics package.
However, while those interested can explore the application of these statistical packages to their routine tasks as Academic Planning Officers, this presentation would attempt to use some of the commonest and simple data analysis techniques to illustrate some of the data often collated from our routine tasks. Consequently, the percentage calculations, line graph, bar charts, and pie-charts would be considered as techniques for pictorially presenting our data for illustration and easy understanding. This is even more so when it is realised that most of the data presentations in our routine tasks are descriptive as they are presented in tabular or graphic forms.
Category of Staff 2000/2001 2001/2002 2002/2003 NUC Ratio (%)No % No % No %
Professors/Assoc. Profs.
175 18.4 188 18.6 240 20.9 20.0
Senior Lecturer 360 37.9 415 41.1 496 43.1 35.0
Other grades 415 43.7 407 40.3 414 36.0 45.0
Total 950 100.0 1010 100 1150 100.0 100.0
Line Graph:Figure 1: A line graph showing the academic staff situation in an institution.
9501010
1150
0
200
400
600
800
1000
1200
1400
2000/2001 2001/2002 2002/2003
Academic Year
No
of
Sta
ff
Bar Charts:Fig. 2: Bar charts for the different categories of staff in 2000/2001 – 2002/2003.
0
100
200
300
400
500
600
2000/2001 2001/2002 2002/2003
Professors/Assoc. Profs.
Senior Lecturer
Other grades
Pie ChartsFigure 3: Pie chart showing the proportion of staff by status in the total number of Academic Staff in 2000/2001.
Other grades, 157.3
Senior Lecturer, 136.4
Professors/Assoc. Profs., 66.2
9.0 DATA STORAGEData storage can be defined in ICT parlance as the holding of data in an electromagnetic form for access by a computer processor. There are two main kinds of storage:– Primary storage – data that is held in random access
memory (RAM) and other memory devices that are built into the computer.
– Secondary storage – data that is stored on external storage devices such as hard disks, tapes, CD’s.
Data Storage DeviceData storage device is a device for recording (storing) information (data).
10.0 DATA UTILIZATIONData utilization relies on people, and a variety of hardware, software, data and communications network technologies as resources to collect, transform and disseminate information in an organisation. It depends on computer-based information systems that use computer hardware and software, the internet and other communications networks, computer-based data resource management techniques, and many other information technologies to transform data resources into an endless variety of information products for consumers/users and business professionals.
Data utilization must have feedback and control mechanism to make the data utility effective. Control involves monitoring and evaluating feedback to determine whether a system is moving toward the achievement of its goal. The control function then makes necessary adjustments to a system’s input and processing components to ensure that it produces proper output. For example, the Director of Academic Planning exercises control when he/she re-assigns a planning officer from an area of data collection in the institution to another after evaluating the feedback about his/her data collection performance.
11.0 ATTRIBUTES OF GOOD DATA/ INFORMATION QUALITYThe data collected, collated and analyzed by the Academic Planning Unit should possess some attributes that would lend them to general acceptability and usage. These attributes could be grouped into three categories:
(a) Time Dimension: This is further broken down into four components:
• Timelines – Information should be provided when it is needed.
• Currency – Information should be up-to-date when it is provided.
• Frequency – Information should be provided as often as needed.
• Time Period – Information can be provided about past, present, and future time periods.
(b) Content Dimension: This involves the following:• Accuracy – Information should be free from
errors.• Relevance – Information should be related to
the information needs of a specific recipient for a specific situation.
• Completeness – All the information that is needed should be provided.
• Conciseness – Only the information needed should be provided.
• Scope – Information can have a broad or narrow scope, or an internal or external focus.
• Performance – Information can reveal performance by measuring activities accomplished, progress made, or resources accumulated.
(c)Form Dimension: This encompasses the following qualities:
• Clarity – Information should be provided in a form that is easy to understand.
• Detail – Information can be provided in detail or summary form.
• Order – Information can be arranged in a predetermined sequence.
• Presentation – Information can be presented in narrative, numeric, graphic, or other forms.
• Media – Information can be provided in the form of printed paper documents, video displays, or other media.
12.0 CLASSIFICATION OF REPORTS PRODUCED BY ACADEMIC PLANNING UNITS
The Academic Planning Officers produce series of reports annually using the varied data at their disposal. Such reports could be classified as belonging to any of the following groups: Exception reports, schedule listing, predictive reports and demand reports.
13.0 DATA MANAGEMENTData management is the process of managing data as a resource that is valuable to an organization or business. The Data Management Association (DAMA) sees it as the process of developing data architectures, practices and procedures dealing with data and then executing these aspects on a regular basis. Data management involves the following:
• Data modelling.• Data warehousing• Data movement• Database administration• Recoverability of Data or Data Backup• Database Security
14.0 CHALLENGES OF DATA MANAGEMENTThese can be summarised as follows:• Inadequate technical/professional staff sufficiently trained
for the tasks.• Inadequate training for available personnel.• Erratic power supply• System failure i.e inadequate attention being paid to ICT
development, use and management.• Inadequate processing equipment and computer facilities • Inadequate funding• Low data storage capacity• Lack of automated method of data collection• Absence of data security• The challenge of computer systems hackers to the internet
facilities of institutions.
15.0 THE WAY FORWARDWe have tried to examine the concept of data in relation to some specific data requirements the Academic Planning Officers in the Nigerian University system would need to be familiar with in the course of their routine tasks. The data elements considered include those relating to students, staff, curriculum, finance, facilities, library, results, health centre, students accommodation, etc. Typical format samples were used to illustrate the data collection instrument and the usefulness of some of the data were highlighted. Also, attempts were made to examine some techniques of data analysis/presentation, and the qualities of good data were mentioned. Data storage mechanisms were also discussed.
Arising from the discussions, some proposals are being made to enhance the processes of data collection and usage as well as dissemination of information between Nigerian Universities and other government agencies.
• Strengthening of Academic Planning Units in Nigerian Universities:
• The NUC should resuscitate the publication of Statistical Digest on Nigerian Universities:
• Regular training workshops for Academic Planning Officers:
• Recognising Academic Planning Units in Nigerian Universities as Professional Outfits
• Training Opportunities for Directors of Academic Planning Units:
16.0 CONCLUSIONI want to thank the organizers of this training workshop for giving me the opportunity to share my thoughts on the topic. It is hoped that the views expressed would have thrown some insights into the nature, type and usefulness of some of the data we are required to collect, analyze and store in the University system as Planning Officers. I want to say that I wholeheartedly accept any shortcoming in the presentation.
Thank you all for listening.