DATA NEEDS IN ACADEMIC PLANNING: CHALLENGES AND THE WAY FORWARD

1. DATA NEEDS IN ACADEMIC PLANNING: CHALLENGES AND THE WAY FORWARD By DR. WILFRED A. IGUODALA Director, Academic Planning, University of Benin, Benin City, Nigeria. Email: waiguodala@yahoo.co.uk

2. 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.

3. 2.0 DEFINITION OF DATA The 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”.

4. 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.

5. 4.0 DATA COLLECTION There 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.

6. 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.

7. 5.0 METHODS OF DATA COLLECTION Academic 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 Agencies 5.5 Physical Observation 5.6 Vital Registration

8. 6.0 DATA COLLECTION FORMATS Different 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:

9. 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

10. (iii) New Intake by Quota, Admission and Clearance by Faculty/Dept.

11. 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

12. 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.

13. Student Course Registration Data 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 students = Total headcount enrolment of same level

14. 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

15. (ii) Total Staff by Function, Grade and Sex……………………Session

16. (iii) Staff Position by Function, Grade Level and Sex (Staff Schools)

17. (iv) Student Enrolment in Staff Schools:

18. 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.

19. (i) Budget Structure and Expenditure Analysis 2000/2001 – 2001/2002

20. Budget Structure by Faculty 2001/2002 – 2003/2004 • (iii) Financial Grants/Income to the University: 2000/2001

21. 6.4 Research Effort Data6.5 Space Inventory and Utilization Data6.6 Library Data: 6.7 Results Data: (i) Analysis of Degree Results by Faculty/ Department, Award of Class of Degree, Sex

22. Analysis of Sub-Degree and Post-Graduate Diploma Final Year Results by Subject Area 6.8 Health Centre Data 6.9 Student Accommodate Data

23. 7.0 TIME OF DATA COLLECTION Having 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.

24. 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.

25. 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.

26. 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.

27. Line Graph: Figure 1: A line graph showing the academic staff situation in an institution.

28. Bar Charts: Fig. 2: Bar charts for the different categories of staff in 2000/2001 – 2002/2003.

29. Pie Charts Figure 3: Pie chart showing the proportion of staff by status in the total number of Academic Staff in 2000/2001.

30. 9.0 DATA STORAGE Data 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 Device Data storage device is a device for recording (storing) information (data).

31. 10.0 DATA UTILIZATION Data 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.

32. 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.

33. 11.0 ATTRIBUTES OF GOOD DATA/ INFORMATION QUALITY The 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.

34. (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.

35. (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.

36. 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.

37. 13.0 DATA MANAGEMENT Data 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

38. 14.0 CHALLENGES OF DATA MANAGEMENT These 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.

39. 15.0 THE WAY FORWARD We 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.

40. 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:

41. 16.0 CONCLUSION I 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.