using efficiency indicators to build leadership capacity in african universities

Using efficiency indicators to build leadership capacity in African universities

Research Team:Dr Charles Sheppard: Director Management Information, NMMUDr Nico CloeteProf Ian Bunting, DoE South AfricaMs Lisa Belding, CHET consultant

Research funded by the Ford Foundation, Project managed by the Centre for Higher Education

TransformationPresented by: Dr Charles Sheppard, Nelson Mandela

Metropolitan University

Data Collection and Management

Participating Countries:

Data were collected from:

University of BotswanaUniversity of Dar es Salaam (Tanzania)Eduardo Mondlana University (Mozambique)University of GhanaMakerere University (Uganda)University of MauritiusUniversity of Nairobi (Kenya)University of NamibiaAll South African Universities

Aim of the project

Support of this cross-national project centred on the need for ‘continent relevant’ performance indicators for higher education in Africa.

These performance indicators would facilitate regional integration and co-operation and would enable African countries to compare themselves to their ‘peers’; which would be other African countries.

While there was acknowledgement that countries were at different stages in the development of performance measurement strategies, it was felt that this project would allow African countries to learn from each others’ experiences, to recognise common features and generic categories for measuring performance, and to set benchmarks for good practice


Process followed:

• The basic data elements and supporting information needed to calculate a set of efficiency indicators were collected through a template that was provided to the participating universities in Africa.

• The quality and completeness of the data and information provided by many of the universities were of poor quality and in most instances clearly inconsistent and incorrect.

• It was decided that the viable option to improve the datasets was to visit the various universities to discuss the quality and consistency of the data with the project coordinators at the various universities and to attempt to collect as much raw data as possible on site.

Data Collection and ManagementData Collected

1. Head count student enrolments by qualification type (UG diplomas/ degrees, PG diplomas/degrees, diplomas up to masters level, masters degrees and doctoral degrees) and major field of study (science and technology, business and management, education, social sciences and humanities)

2. Full time equivalent student data – could only obtain FTE data for one of the other eight African Countries (have it for all the South African Universities).

• This lack of FTE student data is a major problem for performance indicator analyses.

• This notion of an FTE student is an important one for this project because it, rather than a head count enrolment total, gives a sense of what teaching loads are being carried by different universities.

• Relying solely on head count enrolments would be problematic when judgements on institutional performance are being made due to the differences in FTE: Headcount ratios.


Data Collected

3. Student outputs: full-time equivalent successful students. These ratios between totals of FTE successful and FTE enrolled students permit annual calculations to be made of a university’s weighted average success rate for all the courses taken by students. As a result of the problems experienced with the calculation of FTE enrolled data, this indicator could not be provided for the eight African universities.

4. Categories of staff. Universities were asked to provide data on their staffing resources in terms of two broad categories: permanent staff and full-time equivalent staff. These had to be broken down into: academic staff, administrative staff and service staff. All universities were able to provide satisfactory information on permanent staff within the three categories summarised above. Six of the eight African universities were not able to provide information on FTE staff.


5. Income of universities. Information on income was collected according to the following streams: government appropriations, student fees and private income (gifts and grants, investment income, contracts with non-government agencies, income from the sale of goods and services. Information was well provided and some minor follow-up work will have to be done.

6. Current expenditure information was collected on student and staff housing, which includes costs of goods and services. Current expenditure information was also collected on teaching, research and support services, which includes costs of academic, administrative and service staff, goods and services, finance costs and depreciation of assets. Some follow-up work will have to be done.

Data Collection and ManagementReasons for the difficulties experienced in compiling the requested data tables

• Not functional or appropriate electronic databases in place

• Where there were electronic data bases in place, the data on the databases were often incomplete, classifications were inaccurate, graduate sets were incomplete, not all marks to indicate subject success were captured, where credit systems were used, credits of subjects were not always captured on systems.

• In many instances there are no central office responsible for data collection.

• Various versions of data sets – not clear which are official final data sets.

• Data incomplete – part time staff and students often not captured.

• Incorporated colleges - historical data lacking


Reasons for the difficulties experienced in compiling the requested data tables

•It is evident that the focus in many institutions is merely on input data: financial, enrolments and the staff establishment. The data collected in terms of pass rates and graduation is merely used for the graduation of students. Data on the monitoring of efficiency in terms of student outputs as well as research outputs not compiled in a systematic manner.


Recommendation for data improvements

•There is a dire need for the establishment of electronic data bases or the improvement of existing data bases in these universities. Even basic functional databases would be a major contribution for the effectiveness and efficiency of these institutions.

•There can be no grasp on the efficiency and performance of these institutions without easily accessible and reliable information. This has the potential of making a major contribution to the effective management and planning endeavours of these institutions.

•Capacity building in terms of the improvement of data quality is urgently needed.


Possible calculation of Full-Time Equivalents

Table 1: FTE conversion factors

Qualification Study Level Contact Tuition Distance Tuition

Undergraduate diplomas/degrees

1 0.90 0.50

Postgraduate diplomas 1 0.73 0.73

Postgrad. degrees/Honors

2 0.60 0.60

Masters degree 3 0.31 0.31

Doctoral degree 4 0.48 0.48

Data provided by:University of Namibia, University of Mauritius, CHET, South African Department of Education and Conceptual Framework based on CHET Indicator Project

INSTITUTIONAL PROFILES: COMPARISON OF UNIVERSITIES OF MAURITIUS, NAMIBIA,

FORT HARE AND RHODES Research Team:Dr Charles Sheppard: Director Management Information, Nelson Mandela Metropolitan UniversityDr Nico Cloete: Director CHETProf Ian Bunting, DoE South AfricaMs Lisa Belding, CHET Consultant

STUDENT HEADCOUNT ENROLMENTS

GRADUATE OUTPUTS

FINANCIAL DATA IN NATIONAL CURRENCIES

FINANCIAL DATA IN US DOLLARS


Thank YouDr C J Sheppard

Director: Management InformationNMMU

[email protected]

using efficiency indicators to build leadership capacity in african universities

Documents

fte data

african countries

raw data

lack of fte student

time equivalent student

basic data elements

different universities

various universities