i  

DRAFT : Version 4

DataQualityAssessmentFramework

(DQAF)

BOTSWANA

14–25February2011

Marc BERNAL, UIS Regional Advisor for Sub-Saharan Africa Chris VAN WYK, University Stellenbosch, South Africa

ii  

DRAFT : Version 4

ListofAcronyms

BDS Botswana Demographic Survey BEC Botswana Examinations Council BGCSE Botswana General Certificate School Education BOCODOL Botswana College of Distance and Open LearningBOTA Botswana Training Authority BTEP Botswana Technical Education Programme CSPro The Census and Survey Processing System CSO Central Statistics Office DOSET Department of Out of School Education and Training DPSR Division of Planning, Statistics and ResearchDQAF Data Quality Assessment Framework DSP&W Department of Student Placement and Welfare DTVET Department of Technical and Vocational Education and Training ESU Education Statistics Unit EMIS Education Management Information SystemsFTE Full-time Equivalent HIES A Household Income and Expenditure Survey IMPS Integrated Microcomputer Processing System ISCED International Standard Classification of Education ISSA Integrated System for Survey Analysis JSE Junior Secondary Examination M&E Monitoring and Evaluation MLG Ministry of Local Government MoESD Ministry of Education and Skills Development NSDS National Standards Development System MoLHA Ministry of Labour and Home Affairs O&M Organisation and Methods PSLE Primary School Leaving Examination RDMS Relational Database Management System RNPE Revised National Policy on Education SADC South African Development Community SACMEQ Southern and Eastern Africa Consortium for Monitoring Education Quality SASM Schools Administration and Student Management SLMS Student Loan Management System TCs Technical Colleges TEC Tertiary Education Council TEGER Tertiary Education Gross Enrolment Ratio TEMIS Technical Management Information System ToR Terms of Reference TSM Teaching Service Management TT&D Teacher Training and Development TVET Technical, Vocational Education and Training UIS UNESCO Institute for Statistics UNPD United Nation Population Division UNESCO United Nations Educational, Scientific and Cultural Organization

iii  

DRAFT : Version 4

TABLE OF CONTENTS 1.  BACKGROUND ............................................................................................................................................... 1 

2.  METHODOLOGY USED FOR THE DQAF ........................................................................................................... 2 

3.  GOALS AND OBJECTIVES OF DQAF ASSESSMENT ........................................................................................... 3 

4.  OVERVIEW AND FUNCTIONAL STRUCTURE OF THE EDUCATION SYSTEM IN BOTSWANA .............................. 3 

4.1.  THE EDUCATION SYSTEM .................................................................................................................................... 3 

4.2.  PRIMARY EDUCATION ........................................................................................................................................ 4 

4.3.  SECONDARY EDUCATION ..................................................................................................................................... 4 

4.4.  VOCATION EDUCATION AND TRAINING .................................................................................................................. 5 

4.5.  TERTIARY EDUCATION ........................................................................................................................................ 5 

4.6.  SPECIAL EDUCATION .......................................................................................................................................... 6 

4.7.  EDUCATION DEPARTMENTS ................................................................................................................................. 6 

5.  DATA COLLECTION PROCESSES ...................................................................................................................... 6 

5.1.  FIRST TERM DATA COLLECTION PROCESS FOR PRIMARY SCHOOLS ................................................................................. 7 

5.2.  FIRST TERM DATA COLLECTION PROCESS FOR SECONDARY SCHOOLS ............................................................................. 7 

5.3.  DATA COLLECTION PROCESS FOR ANNUAL SURVEY OF PRIMARY AND SECONDARY SCHOOLS ............................................... 8 

5.4.  DATA COLLECTION PROCESS FOR DEPARTMENT OF TECHNICAL, VOCATIONAL EDUCATION AND TRAINING (DTVET) ............. 8 

5.5.  DATA COLLECTION PROCESS FOR BOTSWANA TRAINING AUTHORITY (BOTA) ................................................................ 8 

5.6.  DATA COLLECTION PROCESS FOR TERTIARY EDUCATION COUNCIL (TEC) ....................................................................... 9 

5.7.  DATA COLLECTION PROCESS FOR CENTRAL STATISTICS OFFICE (CSO) .......................................................................... 9 

6.  FINDINGS OF THE DQAF ................................................................................................................................. 9 

6.1.  MINISTRY STRENGTHS ........................................................................................................................................ 9 

6.2.  PLANNING ..................................................................................................................................................... 10 

6.3.  DATA COLLECTION PROCESSES ............................................................................................................................ 11 

6.4.  LACK OF NORMS AND STANDARDS ...................................................................................................................... 12 

6.5.  HUMAN RESOURCES ........................................................................................................................................ 13 

6.6.  DECENTRALISATION ......................................................................................................................................... 13 

6.7.  INFORMATION SYSTEMS .................................................................................................................................... 14 

7.  DATA QUALITY ASSESSMENT FRAMEWORK ................................................................................................ 16 

7.1.  PRE‐REQUISITES OF QUALITY .............................................................................................................................. 18 

7.1.1.  LEGAL AND INSTITUTIONAL ENVIRONMENT ....................................................................................................... 18 

7.1.2.  RESOURCES ............................................................................................................................................... 19 

7.1.3.  QUALITY AWARENESS AND RELEVANCE: QUALITY IS A CORNERSTONE OF STATISTICAL WORK ....................................... 20 

7.2.  INTEGRITY: THE PRINCIPLE OF OBJECTIVITY IN THE COLLECTION, PROCESSING, AND DISSEMINATION OF STATISTICS IS FIRMLY 

ADHERED TO ................................................................................................................................................................ 21 

7.2.1.  PROFESSIONALISM: STATISTICAL POLICIES AND PRACTICES ARE GUIDED BY PROFESSIONAL PRINCIPLES .......................... 21 

7.2.2.  TRANSPARENCY: STATISTICAL POLICIES AND PRACTICES ARE TRANSPARENT. ............................................................ 21 

7.2.3.  ETHICAL STANDARDS: POLICIES AND PRACTICES ARE GUIDED BY ETHICAL STANDARDS ................................................ 22 

7.3.  METHODOLOGICAL SOUNDNESS: THE METHODOLOGICAL BASIS FOR THE STATISTICS FOLLOWS INTERNATIONALLY ACCEPTED 

STANDARDS, GUIDELINES, OR GOOD PRACTICE .................................................................................................................... 23 

iv  

DRAFT : Version 4

7.3.1.  CONCEPTS AND DEFINITIONS: CONCEPTS AND DEFINITIONS USED ARE IN ACCORD WITH STANDARD STATISTICAL 

FRAMEWORKS .............................................................................................................................................................. 23 

7.3.2.  SCOPE: THE SCOPE IS IN ACCORD WITH INTERNATIONALLY ACCEPTED STANDARDS, GUIDELINES, OR GOOD PRACTICE ....... 24 

7.3.3.  CLASSIFICATION/SECTORIZATION: CLASSIFICATION AND SECTORIZATION SYSTEMS ARE IN ACCORD WITH NATIONAL AND 

INTERNATIONALLY ACCEPTED STANDARDS, GUIDELINES, OR GOOD PRACTICES ........................................................................... 27 

7.3.4.  BASIS FOR RECORDING: DATA IS RECORDED ACCORDING TO INTERNATIONALLY ACCEPTED STANDARDS, GUIDELINES, OR 

GOOD PRACTICE ........................................................................................................................................................... 27 

ACCURACY AND RELIABILITY: SOURCE DATA AND STATISTICAL TECHNIQUES ARE SOUND AND STATISTICAL OUTPUTS SUFFICIENTLY 

PORTRAY REALITY ......................................................................................................................................................... 29 

7.3.5.  SOURCE DATA AVAILABLE PROVIDE AN ADEQUATE BASIS TO COMPILE STATISTICS ...................................................... 29 

7.3.6.  ASSESSMENT OF SOURCE DATA: SOURCE DATA ARE REGULARLY ASSESSED AND VALIDATED ......................................... 34 

7.3.7.  STATISTICAL TECHNIQUES: STATISTICAL TECHNIQUES EMPLOYED CONFORM TO SOUND STATISTICAL PROCEDURES, AND ARE 

DOCUMENTED ............................................................................................................................................................. 35 

7.3.8.  REVISION STUDIES: REVISIONS, AS A GAUGE OF RELIABILITY, ARE TRACKED AND MINED FOR THE INFORMATION THEY MAY 

PROVIDE  36 

7.4.  ACCESSIBILITY: DATA AND METADATA ARE EASILY AVAILABLE AND THERE IS ADEQUATE CLIENT (USER) SUPPORT ................. 37 

7.4.1.  PUBLIC AWARENESS AROUND DATA DISSEMINATION PRODUCTS ........................................................................... 37 

7.4.2.  EASE OF ACCESS TO INFORMATION ................................................................................................................. 37 

7.4.3.  METADATA: THE METADATA INCLUDE DISCUSSIONS ABOUT CONCEPTS, SCOPE, CLASSIFICATION, DEFINITIONS, BASE OF 

RECORDING, DATA SOURCES, METHODOLOGY, STATISTICAL TECHNIQUES AND ANY OTHER ISSUES AFFECTING INTERPRETABILITY .......... 37 

7.4.4.  ASSISTANCE TO USERS: CLIENT SUPPORT SYSTEM IS IN PLACE ................................................................................ 38 

7.5.  SERVICEABILITY: STATISTICAL DATA ARE TIMELY AND CONSISTENT ............................................................................. 39 

7.5.1.  PERIODICITY AND TIMELINESS: DATA ARE PUBLISHED IN A TIMELY AND PERIODIC MANNER ......................................... 39 

7.5.2.  CONSISTENCY: STATISTICS ARE CONSISTENT WITHIN A DATASET AND OVER TIME, AND WITH OTHER MAJOR DATA SETS .... 40 

8.  RECOMMENDATIONS .................................................................................................................................. 43 

8.1.  INSTITUTIONAL ARRANGEMENTS AND COORDINATION AMONG CONCERNED STRUCTURES ............................................... 43 

8.2.  DATA COLLECTION PROCESSES ............................................................................................................................ 43 

8.3.  INFORMATION SYSTEMS .................................................................................................................................... 44 

8.4.  DATA QUALITY ISSUES ...................................................................................................................................... 44 

8.5.  CAPACITY BUILDING ......................................................................................................................................... 44 

8.6.  NEXT STEP: PREPARATION OF ACTION PLAN .......................................................................................................... 44 

9.  CONCLUSION ............................................................................................................................................... 46 

10.  APPENDIX A: LIST OF RELEVANT REFERENCES AND DOCUMENTS ............................................................ 47 

11.  APPENDIX B: LIST OF PERSONS MET ........................................................................................................ 52 

12.  APPENDIX C: ISCED MAPPING ................................................................................................................. 67 

v  

DRAFT : Version 4

ListofFiguresFigure 7.1: TertiaryenrolmentbyGender(Botswana)‐2004/05–2008/09 .......................................... 31 Figure 7.2: CSO2009SingleAgePopulationProjections(SourceCSO) .................................................. 31 Figure 7.3: IllustrationofthefieldsinSummaryTableinSectionAcorrespondingwiththeEnrolmentsDetailsinSectionB ..................................................................................................................................... 41 Figure 9.1: Overallresults ......................................................................................................................... 46 

vi  

DRAFT : Version 4

ListofTablesTable 4.1: Numberofprimaryschoolsbyownershipandregionin2010 ................................................. 4 Table 7.1: Overviewofdatacollectionprocesses ..................................................................................... 25 Table 7.2: Primaryschoolsenrolmentperregionbygender2010 .......................................................... 26 Table 7.3: Masterlistofsecondaryschools(Source:ListobtainedfromMoESD) .................................. 26 Table7.4:Primaryschoolstrainedanduntrainedteachersperyear(Source:Statsbrief2010) ......... 30 Table7.5:Populationaged2yearsandoverwhoarestillatschool,bysexandstrata ........................ 32 Table7.6:HighestLevelofEducationAttainedbyGender ..................................................................... 33 Table7.7:PrimarySchoolLeavingExamination(PSLE)results2008................................................... 34 

1  

DRAFT : Version 4

1. BACKGROUNDEducation Management Information Systems (EMIS) were established as one of the priorities of the African Union’s action plan for the Second Decade of Education for Africa, as well as the education programme of the South African Development Community (SADC). EMIS inception and development aims at providing quality education statistical data that is complete, relevant, accurate, timely and accessible, as well as managed efficiently. This, in turn, makes effective decision making possible. In this vein, the Ministry of Education and Skills Development (MoESD) has recognised the important role that education statistics can play in the education sector, specifically for evidence-based decision making. The Revised National Policy on Education (1994), recommendation 124, recommended the establishment of the Division of Planning, Statistics and Research (DPSR) as soon as possible, with adequate resources to coordinate and commission research. The policy further emphasised that a priority for the Division should be the development of the Education Management Information System. The Organisation and Methods (2006) document of the final report on the restructuring of the MoESD, REC: 3.6.2.1.G, takes this policy recommendation a step further by proposing that the Division of Planning, Statistics and Research be elevated to department level. In order to support the development of an effective EMIS across sub-Saharan Africa, the UNESCO Institute for Statistics (UIS) is in the process of conducting diagnostic assessments, in line with its Mid-Term Strategy, of national education statistics systems within the global context of UNESCO’s support to the African Union Second Decade of Education. The ensuing report summarises the findings of a situation analysis of the quality of education data in Botswana, and proposes recommendations for improvement. At this stage it is appropriate to define and gain an understanding of what is meant by EMIS within the context of a national ministry of education. A good definition would be that of UNESCO (2011), which asserted: “A solid information system should not only aim to collect, store data and process information but help in the formulation of education policies, their management and their evaluation”. In our assessment of the quality of education statistics in Botswana we concurred with this definition and soon realised that the production of quality education statistics should not be thought of as a technical or operational process only. It includes important elements such as compliance and accountability issues through the legislation and policy framework of the organisation; adequate human resource allocation and the provision of training; sufficient budget allocation; integrated processes through the collaboration and cooperation of relevant units and sections; an integrated management information system; and proper usage of and demand for quality data through monitoring and evaluation. This integrated approach plays an important role in the production of quality education statistics and will be outlined with specific reference to the situation in Botswana.

2  

DRAFT : Version 4

2. METHODOLOGYUSEDFORTHEDQAFIn the information age, quality data is essential for evidence-based decision making, and in this exercise the quality of education statistics is addressed in various ways. The main methodology used was interviews with key stakeholders, including government officials (technicians and decision-makers) and donor agencies, and a literature review of the relevant documents. The results show that that there is a need for quality data, and this is addressed through legislation, education policy and key education documents, which is encouraging, although at the same time there is room for strengthening the data processes at various levels. The assessment process was guided by the Data Quality Assessment Framework (DQAF). This methodology was originally developed by the International Monetary Fund (IMF), and developed further by UIS and the World Bank for an education context. The UIS is currently developing the framework further into a complete methodology for assessing national education statistics systems. The DQAF for Botswana was planned in coordination between UIS, UNESCO office in Harare, SADC secretariat and the MoESD in Botswana. The fieldwork and on-site situation analysis for Botswana were carried out from 14 to 25 February 2011. Data was collected through: Interviews: Semi-structured interviews were held with key stakeholders, such as the MoESD, the Ministry of Local Government (MLG), their agencies at national and regional level, and other government agencies in charge of data and educational institutions, as well as development partners involved in the collection, processing and use of data. Appendix B indicates all the groups and persons that were interviewed during the field visits. The interview sessions were the largest and single most valuable source of qualitative information collected. A round table meeting with the Permanent Secretary of MoESD, on 24 February 2011, during which the preliminary findings of the country visit were presented and discussed and additional information gathered. Archivalanalysis: This observational method was used to examine the accumulated documents as part of the research method to enhance the report. The documents included, but were not limited to: promulgated Acts; Policies; documents; official publications; strategic plans of the agencies; and questionnaires used to collect data. A list of the documents that form the basis of the analysis is attached as AppendixA. Analysisofdata: A basic analysis of the available data was done for consistency and accuracy, and for trends and relationships in the data that was collected. The mission was conducted by Marc Bernal, UIS Regional Advisor, Chris Van Wyk, private consultant from the University of Stellenbosch, Roselyn Mwangi-Wabuge of UNESCO Harare (during the first week), Frederic Borgatta, Cluster Advisor of UIS, Windhoek (during the second week), and Susan Matroos and Boikhutso Monyaku of Division of Planning, Statistics and Research(DPSR) in the MoESD.

3  

DRAFT : Version 4

The analysis in this report were contingent on the cooperation, consultation and input of national and sub-national staff from the key government departments and agencies, as well as school staff and other partners. Representatives from these organisations have been more than helpful, sincere thanks to them for their assistance. Special gratitude is also due to Susan Matroos and Boikhutso Monyaku, who accompanied the team, especially for their important support for this mission. We would like to thank all of those who participated in the interview sessions associated with this initiative. The valuable comments and observations, often expressed with deep conviction and concern, reflect the seriousness with which data quality issues are being considered by the MoESD. 3. GOALSANDOBJECTIVESOFDQAFASSESSMENTThe aim of the exercise was to create a realistic picture of the practical state of education statistics in Botswana at the institutional, regional and national level from an implementation and application point of view, and to discover how consistent it is with what the DQAF proposes. The aim is to try to inform the recommendations made for improving education statistics in Botswana. As such, the objectives of the situational analysis are the following:

Todevelopanaccuratepictureoftheavailability,levelandextentoftheuseofeducationstatisticsinBotswana

To identify and understand the challenges thatHeadquarters and regions face in theirdrivetooptimallyimplementEMISintheproductionofeducationstatistics

ToidentifygapsinthecurrentsituationandkeyprioritiesforfuturedevelopmentthroughtheDQAF

Toidentifybestpracticesthatcanberecommendedandusedinothercountries ToputforwardrecommendationstotheMoESDonwaystoimproveeducationstatisticsinBotswana 4. OVERVIEWANDFUNCTIONALSTRUCTUREOFTHEEDUCATIONSYSTEMIN

BOTSWANA

4.1. TheEducationSystemAccording to the Organisation and Methods (2006) document, the Ministry of Education and Skills Development (MoESD) is mandated by the Education Act, CAP 58:01, Section 3 (1) (1967) to provide for the proper development of education and for matters incidental to or connected with education from primary to post-primary level. However, the Revised National Policy on Education (RNPE) (1994) has increased the responsibility of the Ministry to include a portfolio responsible for pre-primary education. The formal education system in Botswana comprises of Early Childhood Education, 7 years of schooling in primary education, 3 years in junior secondary education and 2 years of senior secondary education. There is also post-school vocational and technical training that provides skills

4  

DRAFT : Version 4

for specific occupations. Tertiary education also takes place mandated by an Act of Parliament. There is also Non-Formal Education. It is worth noting that the education regions have increased from six primary and five secondary respectively, in previous years to 10 in 2010, as indicated in Table4.1 below. The Ministry of Education and Skills Development (MoESD) now manages and delivers primary and secondary education services across these ten regions: Central, Chobe, Ghanzi, Kgalagadi, Kgatleng, Kweneng, Northeast, Northwest, South and South East. Table4.1: Numberofprimaryschoolsbyownershipandregionin2010 4.2. PrimaryEducationPrimary education starts from standard 1 and continues to Standard 7. At the end of the primary stage (Standard 7), the student has to sit for Primary School Leaving Examination (PSLE) and automatically progresses to junior secondary school stage. At the primary level, the Ministry of Local Government is responsible for school infrastructure (buildings), pupils’ feeding and school supplies, while Ministry of Education and Skills Development is responsible for provision of textbooks, curriculum development, teacher training and development, teachers’ wages and welfare, . 4.3. SecondaryEducationThe secondary education is divided into two levels, namely Junior Secondary education, which consists of three forms (Form 1, Form 2 and Form 3) and Senior Secondary education, which is made up of two forms (Form 4 and Form 5). At the end of the Junior Secondary level (Form 3) there is a compulsory Junior Secondary Examination (JSE), which is a requirement for qualifying for Senior Secondary school. The final examination at the end of Form 5, the Botswana General Certificate School Education (BGCSE), or International General Certificate of Secondary Education (IGCSE) determines if the student is eligible for vocational, technical or tertiary education. IGCSE is awarded at private secondary schools within the country. The MoESD is responsible for secondary school

Government Private TotalCentral 250 14 264Chobe 10 1 11Ghanzi 22 1 23Kgalagadi 40 0 40Kgatleng 37 1 38Kweneng 91 10 101NorthEast 61 4 65NorthWest 65 5 70South 121 5 126SouthEast 48 19 67Total 745 60 805

5  

DRAFT : Version 4

infrastructure (buildings), students’ feeding and school supplies, provision of prescribed textbooks, teacher training and development, curriculum development, teachers’ wages and welfare. 4.4. VocationEducationandTrainingThere are two vocational training programmes in Botswana: certificate-level programmes under the auspices of the Botswana Training Authority (BOTA), and the MoESD-run Botswana Technical Education Programme (BTEP). To enter either the BOTA or BTEP programmes, a minimum requirement is the Junior Certificate. In Botswana, technical, vocational education and training is delivered at different levels and in different types of institutions. The institutions include eight government-owned technical colleges (TCs), 21 government brigades taken over from the community and 18 community-owned ‘brigades’(in the process of being taken over by government), which receive government subsidies, public and private universities, and private, independent vocational schools. The main programme offered by the TCs is the Botswana Technical Education Programme (BTEP) under the authority of the MoESD. The main programmes offered by the brigades are the Trade Test Certificates, leading to a National Craft Certificate and City and Guilds accreditation. In order to meet industry needs, the Botswana Training Authority (BOTA) was established in 2000 in the Ministry of Labour and Home Affairs (MoLHA) under the Vocational Training Act to act as the regulatory body charged with the coordination and monitoring of training programmes provided by vocational training institutions. The BOTA focuses on accredited TVET institutions up to and including certificate level. The Department of Technical and Vocational Education and Training (DTVET) administers the eight technical colleges and the 21 brigades already taken over from the communities. The Botswana Brigades offers skills based training programmes for employment. The MoESD is currently in the process of integrating the brigades into its vocational and training programme and institutions through the TCs. 4.5. TertiaryEducationThe MoESD considers all post-secondary education as tertiary. The Tertiary Education Council (TEC), established under the Tertiary Education Act of 1999, plans and coordinates post-secondary education, from post-certificate to university degree programmes, and determines and maintains standards of teaching, examination and research in post-secondary institutions. The BOTA and the TEC divide the regulation of the certificate-, diploma- and degree-granting institutions in the following way: the BOTA regulates the accreditation of vocational programmes offered in post-secondary institutions that issue certificates only, while the TEC is concerned with diploma and degree programmes for post-secondary institutions. As such there is an overlap of authority between BOTA and TEC in those tertiary institutions that offer both certificate and degree-level programmes (i.e., Limkokwing University of Creative Technology, University of Botswana, Ba Isago University College, Botho College e.t.c).

6  

DRAFT : Version 4

4.6. SpecialEducationAs a general rule, the school system tries to integrate students with special needs into the regular classroom setting; however, there is a policy to provide special education units in some schools when necessary. There are government-aided private schools offering special education programmes (e.g., for the blind and/or deaf), in addition to material provided by international organisations and/or funds provided by Non-Governmental Organisations. 4.7. EducationDepartmentsBotswana’s education system is managed by the Ministry of Education and Skills Development, which is made up of several departments. These include the following, of which the data collection processes were of particular interest to this situational analysis: ‐ The Department of Pre & Primary Education ‐ The Department of Secondary Education ‐ The Department of Out of School Education and Training (previously Non-Formal Education) ‐ The Department of Teacher Training and Development (TT&D) ‐ The Department of Tertiary Education Financing ‐ The Department of Teaching Service Management (TSM) ‐ The Department of Curriculum Development and Evaluation ‐ The Department of Technical, Vocational Education and Training (DTVET) Other important divisions of particular interest to this study are: ‐ The Division of Special Education ‐ The Tertiary Education Council (TEC) ‐ The Botswana Training Authority (BOTA) ‐ The Botswana Examination Council ‐ Division of Planning, Statistics and Research (DPSR) – in the restructuring of the MoESD it was recommended that this division be elevated to department level (O&M, 2006). 5. DATACOLLECTIONPROCESSESThe EMIS Unit is responsible for the data collection and dissemination of Education statistics as a whole, but should be noted that other sub-sectors such as DTVET and BOTA have their own data collection processes as outlined later in this section. The MoESD collects data pertaining to the school system twice per year. At the primary and secondary levels, both public and private, a short questionnaire commonly referred to First

Term(F5a for primary schools and 4a for secondary schools) uses January enrolment data for budgeting and planning purposes. The annualsurvey arises from the second collection of data which occurs in March and uses more comprehensive questionnaires (F6c for pre-primary, F5c for primary schools and 4c for secondary schools, tertiary education form)

7  

DRAFT : Version 4

that addresses the entire school system, both public and private. An annual survey questionnaire for technical and vocational institutions is currently being piloted.5.1. FirsttermdatacollectionprocessforprimaryschoolsThe first term data collection process in primary schools is done in the following way: The MoESD sends the questionnaire to the schools. This dissemination process is not well established yet, as the questionnaires sometimes are posted and on other occasions are sent via the regional offices. There is also no control system in place to verify whether or not the schools have received the forms. The circular that accompanies the questionnaire requested the schools to complete the form on the first day of school, while the instruction on the instrument requires it to be completed by the end of January. Although a manual on how to complete the questionnaire is available, it is not always disseminated with the questionnaire. After the form is completed it is sent to the education officer responsible for the area (inspectorate). A workshop is conducted in the inspectorate area for which all the head teachers gather. The forms are validated, verified and checked and a summary is compiled for the inspectorate area. However, it appeared during the interviews that there is lack of coordination and support from headquarters for the workshop approach. This summary is sent to the region, where a summary for the region is compiled from those of all the inspectorate areas. Then a national workshop is conducted to consolidate the inspectoral summaries to come up with the regional and national statistical summaries. This entire process is l to verify the questionnaires and to improve the quality of the data. This process ensures that there is a return rate of almost 100% for the first term questionnaires from the schools within the specified time period. However, there is no system in place to keep a record of the response rate in the MoESD. The regional summary is finally sent to head office, where a national summary is compiled This summary is used for the publication (Statsbrief) that is published every year in September. 5.2. FirsttermdatacollectionprocessforsecondaryschoolsThe first term questionnaire is disseminated to secondary schools via the posts and sometimes via regional education officers. There is no control system in place to verify if all the schools have received the form. The form is completed and sent to the HQ, but there is no verification process to check the quality of the data. At HQ all the forms are captured in a primitive DOS-based system (IMPS) that is no longer supported by IT (Refer to the section on informationsystems for a more detailed description on IMPS). The result is that no new information can be added to the questionnaire because the data-capturing systems cannot be modified. Furthermore, the captured data can only be obtained at aggregated (summary) level and not at school level. As a result the school data cannot be verified and checked per school and therefore influence the quality of the data produced.

8  

DRAFT : Version 4

Another important point to note in relation to the secondary data collection process of the MoESD is that there is no presence or focal point of EMIS at the regional level. The consequence is a lack of ownership or accountability at this level which results to a low response rate (±80%) 5.3. DatacollectionprocessforannualsurveyofprimaryandsecondaryschoolsThe annual survey questionnaire is sent to all schools to be completed and then sent to HQ. Because there is no presence of EMIS at the regional level, and also no involvement of education officers at the regional level, the response rate for all institutions is quite low (±80%), and this further affects the timeliness of the data. The MoESD annual reports are two to three years behind the current calendar year. The last time that data was available for the annual publication was in 2007 – a delay of almost four years. The data capturing for the annual survey is also in the IMPS database, with the same result: only summary data is available. 5.4. DatacollectionprocessforDepartmentofTechnical,VocationalEducationandTraining(DTVET)The mandate of the Department of Vocational Education and Training is according to O&M (2006) “to provide vocational and technical training to meet the manpower demands of the nation for artisans, technicians and professionals, and to assist in the coordination of other vocational and technical training in Botswana”. There are eight technical colleges and 39 brigades (21 public and 18 community based) under the DTVET. Brigades were community-based programmes that offered skills training for employment. The MoESD is currently in the process of integrating the Brigades into its vocational and training programmes and institutions through the technical colleges. DTVET has divided these institutions into two “regions” in Botswana. The department has developed two separate data collection instruments to collect data per student and per lecturer for each institution. Excel is the software tool that is used to capture and summarise the data. Officers of the Department go to each institution to verify and check the data. There is a focal point for data collection at each institution that makes it easier to communicate with the institutions. The data that is collected is for administrative purposes only and not shared with any other unit within or outside the MoESD. The MoESD is in the process of piloting a questionnaire that will collect data from vocational education and training institutions.

5.5. DatacollectionprocessforBotswanaTrainingAuthority(BOTA)The establishment of BOTA was mandated through an Act of Parliament in 2000, namely the Vocational Training Act. BOTA regulates the accreditation of the vocational programmes offered in post-secondary institutions that issue certificates only. BOTA collects information on students four times a year. Although there is an overlap between BOTA and DTVET in collecting data from the same institutions, there is no sharing of data between these two data-producing agencies. BOTA has developed an information system that can register individual students with a unique identifier. BOTA seems to be quite successful in its data collection processes. During the investigation it was reported that the response rate for current data collection process (Spot Survey) is 100% every year. All

9  

DRAFT : Version 4

BOTA’s accredited institutions are displayed on the website by village and region (http://www.bota.org.bw), although no statistical publications were available. 5.6. DatacollectionprocessforTertiaryEducationCouncil(TEC)TEC is responsible for the coordination of tertiary education and the determination and maintenance of standards for teaching, examination and research in tertiary education in Botswana (http://www.tec.org.bw). Tertiary education has been mandated to the Tertiary Education Council (TEC) through the Tertiary Education Act of 1999, Section 5 (2)(a)-(f). TEC collects data from each institution from October to November. The Council has a focal point that was identified by each institution to be responsible for data collection. A fairly complicated Excel spreadsheet is sent to each institution to be completed and sent back to TEC via e-mail. Training is provided on an annual basis on how to complete the data collection instrument. TEC envisaged collecting data at the student unit level with the completion of the Technical Education Management Information System. The data is published annually and we could obtain a copy of this publication for 2008, Tertiary Education at a Glance for October 2008 (available from http://www.tec.org.bw), during our visit. 5.7. DatacollectionprocessforCentralStatisticsOffice(CSO)CSO is responsible for Household Surveys which includes education data that could be used to complement the data of the MoESD. CSO is also responsible for the population census in the country. The last census was conducted in 2001 and another census is planned for 2011. Population data available from population censuses is important to verify education age data and to calculate key education indicators such as enrolment ratios. The role of Household surveys and population census will be discussed in detail in the DQAF section. 6. FINDINGSOFTHEDQAFIn this section we will identify processes, systems and technologies that are (a) examples or evidence of good practice, and (b) opportunities to improve or strengthen current practices for optimal efficiency. The major themes extrapolated from the situational analysis have been summarised and are presented below: 6.1. MinistryStrengths

Government awareness: The situational analysis reveals that the government and the MoESD recognise the importance of quality data for evidence-based decision making, policy formulation and improving education delivery and outcomes. There is a strong awareness of data quality issues by the Ministry– mainly timeliness and reliability - as well as of the lack of functional planning. It seems that there is politicalwill to improve the situation. In this vein, the MoESD has recognised the important role that education statistics can play in the education sector. The Revised National Policy on Education recommendation 124 (1994) recommended the establishment of the Division of Planning, Statistics and Research as soon as possible, with adequate resources to coordinate and commission research. The policy further emphasised

10  

DRAFT : Version 4

that a priority for the Division should be the development of the Education Management Information System (EMIS). The Organisation and Methods (2006) document on the finalreport on the restructuring of the MoESD, 2006, REC: 3.6.2.1.G, takes this policy recommendation a step further by proposing that the Division of Planning, Statistics and Research be elevated to department level. Sound legislation: The mandates and functions of the departments in the Ministry are governed and guided by relevant legislation and related policies. The Ministry of Education is mandated by the Education Act (CAP 58:01) Section 3 (1) to provide for the proper development of education and for matters incidental to or connected with education from primary to post-primary level (Organisation and Methods, 2006). However, it must be said that the mandate of the Ministry in the Education Act does not reflect the important role of education statistics in educational planning, nor does it include any reference to data collection processes. Through the Tertiary Education Act of 1999, Section 5 (2) (a)- (f), the Tertiary Education Council (TEC) should play an important regulatory role in tertiary education. The Teaching Service Act (Cap 62:01) has mandated Teaching Service Management (TSM) to take over, amongst other, the function of personnel statistics. Furthermore, the Revised National Policy on Education (RNPE) White Paper No. 2 of April 1994 identified seven key strategy issues for the future development of education in Botswana. Personnel commitment: The other positive point that we identified during our mission is that the education personnel are committed and work hard to meet the requirements of their mandates. We encountered high levels of commitment throughout the education system, at the institutional, regional and national level. 6.2. Planning

Strategic document: We could not find a strategic document that drives the data management agenda and sets the targets for the education sector in the coming years. Such a document should be based on an economic analysis of the education system following a results-oriented approach. There seems to be an attempt within BOTA to analyse labour market needs in order to orient the provision of training by VET providers. This type of socio-economic approach should also be used to analyse the sector as a whole to help in decision making. Noplanningunit: The staff members in the planning unit that is currently in the MoESD are seconded from the Ministry of Finance and Development Planning. Their function is focused more on development projects and their core mandate is related to infrastructure. They do not fulfil the traditional planning function in the Ministry. There is no planning unit with a monitoring and evaluation (M&E) function that can act as champion for indicators to drive the data demand initiative. In the absence of a proper planning unit it seems that there is a lack of leadership around the M&E function. The result is that some departments with no planning mandate are doing some planning themselves because of the need for it, and often they conduct their own data collection, which creates a serious response burden at the school level because of overlapping data requests for the same information from different departments and even ministries (see Datacollectionprocesses

11  

DRAFT : Version 4

further on for more detail). Furthermore, although several staff members have been through high-level technical training in education planning, there is no ground for them to apply their competencies effectively. Nobudgetvote: The Division of Planning, Statistics and Research (DPSR) does not have a vote on the budget. This, together with slow financial procedures, has an impact on the implementation of planned activities regarding data collection. 6.3. Datacollectionprocesses

Adhocanduncoordinateddatacollectionprocesses:The implementation of the EMIS Unit has been done without a clear definition of its mandate. The ambiguity concerning who has responsibility for data collection, processing, data analysis and dissemination has created a data leadership vacuum within the Ministry. Ministries and departments need quality data that is complete, accurate and timely and that can be used by managers as a basis for making appropriate decisions that contribute to the quality, expansion and sustainability of their programmes and to fulfil their mandates. The data is unfortunately not provided by the main data-producing agencies (DPSR in MoESD and the Central Statistics Office (CSO)), and the data received is unreliable, incomplete, untimely and rarely pertains to the mandates they have to fulfil. The lack of leadership to address these data quality issues and the resultant leadership vacuum that exists causes each of these units to develop their own “silo-based” and parallel data collection processes, mostly under pressure to perform their tasks. The data collection processes therefore are rarely the result of a coordinated effort to address the information needs of education planners, policy makers and managers. The result is ad hoc, fragmented and uncoordinated data collection throughout the entire education system that results in the following: It leads to serious response burdens at the institutional level, because of overlapping data requests for the same information from different authorities, such as the MoESD and MLG in primary schools and authorities with the same accreditation, such as BOTA and TEC. Lack of timely reporting and a low response rate are the result of these fragmented activities. Units give preference to their own data collection rather than the official processes implemented by the Ministry. No single source of the truth exists. A single version of the truth means, by definition, that there is a single representation of critical data such as institutions, enrolment, teachers, and so on that is unique, complete and consistent, and which becomes the most reliable and authoritative information for the entire Ministry. However, we have found that the data is spread in silos across disparate systems. There is more than one reference list of institutions in different units in the Ministry. The Education Act, Section 13 (1-5) mandated the MoESD to be in charge of the registration and control of schools and “requires the Permanent Secretary to establish and maintain a register of all schools, in which shall be recorded the particulars required or permitted by or under the provisions of this Act. For the purposes of such register, a system of classification shall be adopted which shall distinguish primary schools from other schools, etc”. We found that there were different lists of the institutions, and that these are not maintained or updated at

12  

DRAFT : Version 4

one central place. Even within the DPSR there are several separately maintained lists of schools, even containing different school identification numbers. The School Registration Unit, in the same division as Statistics, has its own reference list but there is no collaboration or sharing between these two units since statistics has different codes for schools. There also are multiple data sets containing the same information (e.g. on enrolment), but with data collected by different entities. There is no EMIS policy.This policy should create a framework that allows for the coordinated and sustainable development of education information systems.

Weaklinkagesofstrategicpartners:The link between ministries and departments with the same data requirements seems to be very weak. The result is a lack of consensus and data sharing between these data producers and data users at each level of the education system regarding the information needed. Below are examples of data collection duplication and no data sharing amongst strategic partners: TT&D has set up a working committee for data management without any input from DPSR. TSM has its HR management system with apparently no link to any other database. The Department of Out of School Education and Training (DOSET) has developed an information system to be rolled out at regional level without any collaboration or consultation with Statistics and EMIS units. DTVET has designed his own data collection instrument and is seeking support to implement a database system. MLG is busy investigating the development of a database system without consulting and collaborating with MoESD. The “10-day grace period form” is used by MLG to collect data from primary schools at regional level, while MoESD has its own form for the first term and for the annual data collection processes. DPSR uses an official form to collect the first term and annual data from the secondary school department, while the secondary school department uses his own internal form to collect data from schools without sharing it with DPSR. The concept of sharing information is seriously underdeveloped. Both TEC and BOTA collect data from the same institutions, but neither data set is shared with the other or with the MoESD/DPSR. Several cases of duplication have been noted, such as data collection from the same brigades by BOTA and DTVET. TEC collects the same information from colleges that is needed by TT&D, and TT&D does not have access to this information nor is there sharing between these two agencies (even though they use the same information).

6.4. LackofnormsandstandardsAlthough CSO has overall responsibility for certifying all the data that Government publishes, and has its own statisticians supporting the Ministries, it appears there are no set data quality standards that the Ministries should adhere to. The National Strategy for the Development of Statistics (NSDS) that would coordinate data collection across

13  

DRAFT : Version 4

Ministries is not yet in place. In the absence of standards and coordination, departments resort to making up their own rules concerning the collection and management of their data. Training: Education officers (school heads and principal education officers) receive little if any training in data collection methods, and rarely have standardised instructions or manuals on how to collect the data. Feedback: Since education managers at the regional and local (institutional) level rarely receive feedback on the data reported to DPSR and higher levels, they have little incentive to ensure the quality of the collected data and to comply with reporting requirements. In fact, regional officers reported that their staff members regard the data collection and verification function as another burden and the inspectorate feels that it impacts on their core pedagogical function. Dataanalysisandusage – Data seemed to be collected for reporting purposes and we could only find little evidence of data analysis for planning needs. 6.5. HumanresourcesThere is only one staff member in EMIS; the others are seconded from CSO (Ministry Of Finance and Planning). There also is a need for a clear role clarification between the Education Statistics Unit (ESU) and the EMIS unit. All the staff members in the Education Statistics Unit have been seconded from the Central Statistics Office (CSO). This has limited the Ministry of Education in terms of building capacity and development of its own staff. There is no EMIS presence at the regional level in terms of functions or education officers.

6.6. DecentralisationThe Organisation and Methods (2006) document emphasises that the Ministry of Education realised the need to decentralise its functions from the centre to regional level as a result of the rapid expansion of the education system. However, there is no EMIS focal point or function at the regional level. We want to illustrate the importance of coordination at regional level with a success story. The first term data collection exercise is conducted successfully and within an acceptable timeframe essentially because it is closely coordinated with the regions and the inspectorates (refer to paragraph: First term data

collectionprocessforprimaryschoolsfor more details on this process). However, it must be added that this assignment is seen by the sub-national level as an additional task and it is not included in the job description of the staff involved in it. On the other hand, the annual report is seriously delayed (last published in 2007) because the national level has to link directly with the schools, without any involvement by education officers at the regional level. While 300 IT specialists have been deployed in the country (mainly in secondary schools), no significant effort has been made to improve planning at the regional level. It was reported that these IT staff members have been deployed without a clear job description.

14  

DRAFT : Version 4

The regions are doing their own planning without an information system and are not feeling concerned about the statistics collected by headquarters. They have their own “10-day grace period form” that informs them of the situation at the school level. 6.7. InformationsystemsThere was general concern about the extent to which information management functionality (i.e. to support the information activities of “creation”, “retrieval” and “storage”) has being implemented in government information systems. Some of the units that collect data also develop information systems with minimal input from the central IT unit and the DPSR. The result is that unstructured database systems are found throughout the Ministry of Education that do not adhere to the norms and standards required by the IT Department. Every unit we visited either had a system, or was in the process of developing one, or was planning to develop one without any collaboration with key strategic partners or relevant stakeholders. The following systems were encountered during our visits:

IMPS/CSPro: The Census and Survey Processing System (CSPro) “is a Windows software package for entry, editing, tabulation, and dissemination of census and survey data. The data is stored in (ASCII) text files described by data dictionaries” (available from: http://www.census.gov/ipc/www/cspro/aboutcspro.html). IMPS is an antiquated DOS-version of CSPro and is no longer supported by the CSO. The MoESD is using IMPS to capture data for the first term secondary and annual survey of all institutions. Three major drawbacks of the system currently are: No extra fields can be added to any data collection instruments because of the lack of support. This has a major limiting influence on the scope of the data sets (first term and annual) The pre-defined queries in IMPS generate only summary data by region. No data is available per school for reporting purposes (see paragraph, First term data

collectionprocessforsecondaryschoolsabove for more details). The result is that no data verification or checks can take place at school level. The data capturing form can be viewed per school, but analysis of trends and verification per school is not possible. This is a major constraint to improving the quality of the data. There is no database management (data is stored in ASCII text files), which is problematic in an environment such as the MoESD where time series (historical) data is of the utmost importance to analyse trends

FormDocs:This software has being piloted in MoESD with the possibility of using it as a data-capturing tool. FormDocs is software that allows to easily designing electronic forms and provides a user-friendly interface for data entry. The database functionalities of storing and retrieving data are not built into the software. The result is that access to and manipulation of the data becomes a cumbersome exercise and therefore this is not an ideal solution in a data-driven environment, such as the MoESD. TechnicalManagementInformationSystem(TEMIS): The Tertiary Education Council (TEC) is in the process of developing (outsourced) an information system that will be able to collect the data of individual students.

15  

DRAFT : Version 4

Information System for TT&D: TT&D is in the process of investigating the possibility of developing an information system for data collection to support their function. A working committee has been set up for this purpose. INFINIUM:This is a system used by Teaching Service Management (TSM) to register and maintain MoESD teacher details and manpower planning. DOSETInformationManagementSystem: This is a system that was developed in-house by MOESD IT officer for the Department of Out of School Education and Training. The system will be developed in four phases, with Phases 1 and 2 having the functionality to register learners and facilitators, to track learners, etcetera. Phase 3 will deal with the management of learning material management and Phase 4 will manage the facilities. The system is developed with VB.NET on a MySQL database. The idea of the Department is to roll it out to regional level. No decentralisation strategy has been developed neither has any workload assessment been conducted (the system is aiming at data capturing at learner level). StudentLoanManagementSystem(SLMS)andForm4SelectionSystem: These systems are developed in an ORACLE database and run on the Linux operating system. They are used in the Department of Tertiary Education Financing (DTEF) The SLMS is used to capture information about students in tertiary institutions funded by the MOESD and the Form 4 selection system is used for Form 4 selections and placement in to senior secondary schools. MinistryofLocalGovernment: The MLG is responsible for the building of schools and the provision of textbooks in primary schools, but is also busy investigating the possibility of building an information system to manage their data. BOTAInformationSystem:BOTA has developed an information system that registers their students, assessors, unit standards, moderators, etc. The system is built on an ORACLE platform. Each student has a unique identification that is generated by the system when an “account is opened” for the student and hooks the student to the system. The functionality of the system is, among others, to register students and their programmes, assessors, moderators, unit standards and accreditations. It is obvious from the above that there are multiple separate database systems throughout the MoESD that do not ‘talk’ to each other, causing islands of information that are not integrated.

16  

DRAFT : Version 4

7. DATAQUALITYASSESSMENTFRAMEWORKThe principal objective of the DQAF evaluation is to produce a qualitative assessment of the education statistics used and produced by the education sector in Botswana. The tools and methodologies for this exercise have been adapted from earlier evaluations undertaken by the International Monetary Fund and UIS. Further adaptations by the UIS and World Bank sought to ensure a comprehensive evaluation, specifically focused on the quality of education statistics. To underpin the DQAF evaluation, a participatory and needs-based approach was adopted. The evaluation framework covers the different steps included in the statistical business process model at the national and sub-national levels, and assesses the strengths and weaknesses of the available structures, based on the six DQAF dimensions: a. pre-requisites of quality b. integrity c. methodological soundness d. accuracy and reliability e. serviceability f. accessibility Narrative descriptions are given of the state of the system in Botswana as per these dimensions and sub-dimensions, in addition to scores on each of the sub-dimensions. Scores are attributed according to international norms pertaining to the functions of the different elements of the statistical system. The scores on the sub-dimensions are then aggregated to arrive at scores on each of the dimensions. These scores should be indicative of where efforts for improvement of the statistical function could focus. In order to make this more explicit, specific recommendations are made. This diagnostic is intended to provide input for an action plan to improve the system of educational statistics in Botswana. For purposes of our assessment we have not assessed data sets per se but rather the entities responsible for managing the data collection and dissemination processes. We have grouped the departments within the MoESD into three categories, namely: (a) Basic Education (primary and secondary education), (b) Vocational Education and Training, and (c) Tertiary Education. This categorisation of entities was based on the following considerations: firstly, entities at a specific level of the education system with a specific legal mandate and secondly, entities with independent data management processes.

17  

DRAFT : Version 4

The data collection processes for BasicEducation (primary and secondary) is currently organised and managed by DPSR in the MoESD as recommended by the revised national education policy. According to O&M (2006), Basic Education is also regarded as one of the five major functions of the MoESD; it would therefore be a logical step to assess it as a separate category. The same principle applies for VocationalEducationandTraining. The establishment of BOTA is mandated through an Act of Parliament in 2000, namely the Vocational Training Act. BOTA regulates the accreditation of vocational programmes offered in secondary and post-secondary institutions that issue certificates only and DTVET is to provide vocational and technical training. We have decided to put both under the category Vocational Education and Training, because their data needs and requirements are very similar and they collect data in the same institutions. TertiaryEducation is a separate category for the following reasons: (a) tertiary education is listed as one of the groupings of major functions for MoESD (O&M, 2006), (b) the coordination of tertiary education has been mandated to the Tertiary Education Council (TEC) through the Tertiary Education Act of 1999 Section 5 (2) (a) - (f), and (c) they manage their own data collection processes. These three categories are similar to what the National Credit and Qualifications Framework (NCQF) (2010) proposes, namely General Education; Technical and Vocational; and Tertiary Education. The scores on the sub-dimensions for each category in the DQAF are aggregated to arrive at total score for the dimensions for each of these categories (Basic Education, Vocational & Technical and Tertiary Education).

18  

DRAFT : Version 4

7.1. Pre‐requisitesofqualityData quality is regulated by a framework of statistical laws, policies, standards and practices, and technical and human resources. This framework cannot exist in a vacuum. Pre-requisites of quality, as one of the dimensions of data quality, do not comprise a qualitative dimension, but refer to the evaluation and understanding of the institutional context in which the statistical processes exist and which is essential to the other dimensions. This dimension presents the integrated nature in which available statistical laws, as well as essential human and technical resources, impact on other quality dimensions. 7.1.1. LegalandinstitutionalenvironmentBotswana has a sound legal and institutional environment and is guided by the necessary legislation, related policies and relevant documents and frameworks. The Revised National Policy on Education (RNPE), Vision 2016, National Development Plan (NDP) 10, and international protocols and agreements such as the World Declaration on Education for All (EFA); Dakar Framework of Action, Millennium Development Goals (MDGs), New Economic Partnerships for African Development (NEPAD) and Southern African Conference (SADC) programmes have had a profound impact in the development and provision of education in Botswana (O&M, 2006). The Ministry of Education (MoE) is mandated by the Education Act (CAP 58:01) Section 3 (1) to provide for the proper development of education and for matters incidental to or connected with education from primary to post primary (O&M, 2006). It should be mentioned that the Education Act, which defines the work of MoESD, does not, in any way, mention statistics or any data collection activity. The Tertiary Education Council (TEC) through the Tertiary Education Act of 1999 section 5 (2) (a) - (f) would play an important regulatory role in tertiary education. The Teaching Service Act (CAP 62:01) has mandated the Teaching Service Management (TSM) to undertake, amongst other, also the function of personnel statistics. Other Acts that guide the education in Botswana are the Vocational Training Act, Local Government (District Councils) Act (give the Minister the authority to provide primary schools and other educational services in relation to primary education) and Botswana Examinations Council Act. The institutional arrangement between CSO and MoESD is not clearly defined. Although CSO has seconded staff members in the ministry, it seems as if the role between CSO and MoESD has not yet been clarified and no official document exists to spell out this arrangement. The agreement and collaboration between MLG and MoESD is also not strong, although an official structure exists through the inter-ministerial committee. There seem to be no culture of data sharing between agencies in the ministry that collect the same information in institutions. This results in uncoordinated data collection processes which in turn cause many disparate information systems across the ministry. This disparate information system landscape has a negative impact on the ability to share information, on collaboration, and on efficient communications between departments and units.

19  

DRAFT : Version 4

Examples of such practices are: (a) both TEC and BOTA collect data in the same institutions, neither of which is shared with each other or with the MoESD; (b) data collection by BOTA and DTVET takes place from Brigades, although the information is not shared; and (c) TEC collect the same information in colleges that TT&D need; however TT&D does not have access to this information, nor is there sharing between these two agencies although they use the same information. Consequently duplication and wastage is the result. Norms and standards for data collection processes and the standardization of information systems and software are not established and developed by the umbrella agency. 7.1.2. Resources

Staff: The number of staff and the capacity to perform data management functions differ from agency to agency. While the overall impression is that staffing in general seems adequate, this is certainly not the case for the EMIS Unit at MoESD. The EMIS has only one staff member and the Education Statistics Unit has seconded staff from CSO, a situation that seems hardly sustainable. Most units seem to have a presence at regional level to perform specific administrative functions and serve as a link between Head Quarters and the regions. MLG has officers at district and regional level who liaise with schools and collect the data required for textbooks, stationery, transport, infrastructure, building of schools, et cetera, by the MLG. EMIS however, has no staff at regional level which makes it virtually impossible to manage any data collection process at regional and local level. Decentralisation is essential to the restructuring process of the MoESD. However, according to O&M document (2006) there is no framework to guide the decentralization process in Botswana and it is therefore imperative for the Ministry of Education and Skills Development to develop a framework that could guide the decentralization process. It should be noted that EMIS is not included among the functions mentioned by the O&M for which there is a pressing need to be decentralized. The adhoc development of information systems further contributes to the fragmentation of datasets and silos of information across the ministries. It seems as if there is an over- provision of IT staff. 300 IT staff members have been deployed to schools at regional level without a proper job description. Computer resources: A situational analysis of the Schools Administration and Student Management (SASM) in April 2008 indicated that limited infrastructure capacity is available, (e.g. most primary schools have no computers and are not connected to the network), limited IT resources (e.g. some users share computers at regional offices and bandwidth is determined by Department of IT and is at times insufficient). Financial: DPSR has no direct access to its budget which makes planning very difficult.

20  

DRAFT : Version 4

7.1.3. Qualityawarenessandrelevance:Qualityisacornerstoneofstatistical

workThe fact that timeliness of the delivery of data has lagged for up to 3 years, seems to indicate that education statistics have not received enough attention from the educational leadership so far. The response rate from schools is low and the annual survey data collection process is three years behind. The last data set available for publication was in 2007. The low response rate we consistently encountered across the ministry in every data producing unit causes a serious timeliness issue. These points to the fact that the accountability and ownership framework for data management is weak compared to other accountability frameworks such as personnel, curriculum and finance. It is evident that no quality standards have been developed by CSO that Ministries can follow. Reviews of data quality meetings appear to take place under the CSO’s umbrella; however, the staff of MoESD does not seem to participate in these meetings. No consultations with users have been observed and improvement of data collection instruments is not organised within a comprehensive manner. Decisions pertaining to missing data is made very late in the data collection cycle which portrays the weaknesses in trade-offs in the quality dimension of the data. For example, the readiness and usefulness of the data is compromised for the response rate.

21  

DRAFT : Version 4

7.2. Integrity:Theprincipleofobjectivityinthecollection,processing,anddisseminationofstatisticsisfirmlyadheredtoThis dimension captures the notion that statistical systems should be based on adherence to the principle of objectivity in the collection, compilation, and dissemination of statistics. The dimension encompasses institutional arrangements that ensure professionalism in statistical policies and practices, transparency, and ethical standards. The three elements for this dimension of quality are:

Professionalism Transparency Ethical standards 7.2.1. Professionalism:Statisticalpoliciesandpracticesareguidedby

professionalprinciplesThe Statistics Act (1967), an Act to make provision for the collection of statistical data, specifically gives the Government Statistician the mandate for the compilation, analysis, tabulation and publication of statistics. The Act is specific as to which statistics may be collected and among others refers in section 3 (f) “to private sectors of the economy,including banking, finance and insurance, land, sea and air transport, travel and tourism,communication, storage, catering service, accommodation, health and health institutions,education, training and instruction, personal and professional services, recreation andamusement,laundryandcleaningservice,sanitaryservice”. A weakness in the Act is that the Government Statistician reports directly to the Minister. However during the investigation CSO officers indicated that the Act has been reviewed and is approved as law in 2009. This amendment recommends CSO to be autonomous in 2010 with the CSO board as the governing body. Government Statistician will be reporting to the board and not directly to the Minister of Finance and Planning. This recent recommendation for CSO to be autonomous, from formerly being a unit of the Ministry of Finance and Planning, indicates the recognition of the importance of its independence with the full authority to compile and disseminate statistical information. This function is underpinned by the Statistics Act, 1967, and clearly states it is an Act of Parliament “toprovide forthecompilation,analysis,tabulationandpublicationofstatistics” [Section 8(1-2)].

7.2.2. Transparency:Statisticalpoliciesandpracticesaretransparent.The publications that are available are transparent and freely accessible through acts, policies, documents (strategic plans) and websites of the respective agencies. The following are specific examples of such policies and practices: The Laws of Botswana are available on a public website (http://www.laws.gov.bw). This site contains all the Laws of Botswana and gives the user access to the legislation arranged chapter by chapter from Volume 1 to Volume XVI in pdf format. Acts of specific importance to this study are Statistics Act, Education Act, Tertiary Education Act, Vocational Training Act, Examinations Act and Teaching Service Act. These Acts specifically mandate the specific agency to be established and provide a specific educational service to the people of Botswana. However, not all of these Acts refer to the compilation and dissemination of statistics. The Statistics Act in Section 8 provides the terms and conditions for the compilation, analysis, tabulation and publication of

22  

DRAFT : Version 4

statistics and in Section 9 provides guidelines on the restriction on publications. This is a limitation in the Education Act. Based on evidence from the analysis of current policies, such as the Revised National Policy on Education (1994) and documentation, such as the O&M (2006) and the National Development Plan 10 (2009) with regard to the terms and conditions under which official statistics are compiled and disseminated, no reference could be found in these documents. The Revised National Policy on Education (1994) recommends that the Division of Planning, Statistics and Research “as soon as possible,with adequateresources,tocoordinateandcommissionresearch”. A priority for the Division should be developing the Education Management Information System. It must be noted that this did not happen; the EMIS unit in the MoESD has only one staff member and there is no EMIS policy that can guide the terms and conditions of the collection and dissemination of education statistics.

The O&M (2006) document on the restructuring of the MoESD acknowledged the key role of statistics and therefore recommended that the DPSR be elevated to a department level and possibly addresses all these data-related issues. Most publications that are available have been clearly identified, and the data-producing agency is usually acknowledged as the source. The Statsbrief publications and the annual reports of the MoESD should clearly stipulate the collaboration between CSO and the MoESD. The publication of TEC, TertiaryEducation at aGlance, doesn’t referenceCSOas

such.

7.2.3. Ethicalstandards:PoliciesandpracticesareguidedbyethicalstandardsClear guidelines outlining correct staff behaviour exist, for example in the Public Service Act, Section 31 (1998), but these are not focused on statistics and tend to be on the generic side. In discussion with staff from MoESD and CSO it appears that each CSO staff member has to sign an oath of secrecy. These ethical standards could also be addressed in an EMIS policy.

23  

DRAFT : Version 4

7.3. Methodologicalsoundness:Themethodologicalbasisforthestatisticsfollowsinternationallyacceptedstandards,guidelines,orgoodpractice

This dimension covers the idea that the methodological basis for the production of statistics should be sound and that this can be attained by following internationally accepted standards, guidelines, or good practices. This dimension is necessarily dataset-specific, reflecting different methodologies for different datasets. This dimension has four elements, namely concepts and definitions scope classification/sectorization basis for recording

7.3.1. Conceptsanddefinitions:Conceptsanddefinitionsusedareinaccordwith

standardstatisticalframeworksNo official documents have been found on the concepts and definitions for key datasets in the MoESD. However, clarification notes are provided on the survey questionnaires, such as for trained and untrained teachers. For example, on the first term form for primary schools (F5a) the following definitions are provided: Relief teachers are relieving teachers either on sick leave or maternity leave. Teachers on study leave and those on interdiction are relieved by Temporary Teachers. Under un‐qualified include all teachers not trained to teach in primary schools, e.g. Form 5 leavers, teachers qualified in other disciplines other than teaching, for example secondary trained teachers. ECCE: refers to pupils currently in Standard 1 that have gone through Early Childhood Care and Education. Truancy refers to pupils who stay away from school for no apparent reason. ChildLabor refers to children leaving school to work on farms, lands, tuck shops, baby setting etc. In the publication of TEC, Tertiary Education at a Glance (2008), there is also a technical description of the data, a definition of the concept, purpose and calculation method. Here is an example of the Tertiary Education Gross Enrolment Ratio (TEGER) for the 18-24 years: Technical Description of Data: TertiaryEducationGrossEnrolmentRatioDefinition:Totalenrolmentofstudentsofallagesintertiaryinstitutionscomparedto

populationofofficialagegroupinagivenacademicyearPurpose:Thisindicatoriswidelyusedtoshowthegenerallevelofparticipationinagivenlevelofeducation.ItindicatesthecapacityoftheeducationsystemtoenrolstudentsofaparticularagegroupCalculation method:Dividethetotalnumberofstudentsofallagesenrolledattertiaryeducationbythepopulationaged18‐24,multiplytheresultby100 The concepts such as full-time and part-time enrolment and teachers are taken into consideration in the data collection processes of DTVET and BOTA.

24  

DRAFT : Version 4

In discussion with staff at the schools there seems to be no certainty whether to take the age of the child for the year irrespective his month of birth. This is also not specified on any of the survey questionnaire we obtained.

7.3.2. Scope:Thescopeisinaccordwithinternationallyacceptedstandards,guidelines,orgoodpractice

Data collection instruments exist within the DPSR in MoESD, TEC, BOTA, DTVET, MLG and regional offices of MoESD in an attempt to cover all levels of education system. MoESD collects data twice a year for primary and secondary education and once a year for all the other levels, such as pre-primary, vocational, technical and tertiary. Table 7.1 below presents a detailed analysis of all the data collection instruments that the team could obtain during its investigation. It must be noted that although some of the instruments exist, the datasets are not necessary complete or available. Pre-School Annual Return (MoESD F6C) Primary School First Term Summary (MoESD F5a) Primary School Annual Return (MoE F5c) Secondary School First Term Summary (MoESD F4a) Secondary School Annual Return (MoESD F4c) Technical, Vocational Education and Training Centres & Brigades Annual Returns (MoESD TVET) Teacher Training Colleges Annual Statistics Return, Ministry of Education and Skills Development TEC Instrument BOTA Instrument DOSET MLG 10-day grace period data collection tool Monthly reporting tool for education regional office Class allocation form used by MLG

25  

DRAFT : Version 4

Table7.1: Overviewofdatacollectionprocesses

PreschoolAnnualReturn2010

(FC6)

Primary‐1

stTerm2011(F5a)

Secondary‐1stTerm2011(F4a)

Primary–Annual2011(F5c)

Secondary–Annual2011(F4c)

Techn,VocEdandTrainAnnual

TeacherTrainingCollegesAnnual

BOTAQuestionnaire

Primary‐10daygraceperiodform

Primary‐MonthlyReportingTool

TECQuestionnaire

Enrolment gender X X X X X X X X X X X public X X X X X X X X X X X private X X X X X X X X X Standard/level X X X X X X X X X X Repeaters gender X X X X public X X X X private X X X X Standard/level X X X X Dropouts gender X X X X X X X public X X X X X X X private X X X X X X Standard/level X X X X X X X Reason X X X X X

Transfers In/Out gender X X public X X private X X Standard/level X X Enrolment by age gender X X X X X X X X public X X X X X X X X private X X X X X X X X Standard/level X X X X X X X Streams/Subjects/Program gender X X X X X public X X X X X X X private X X X X X X X Standard/level X X X X X X

Teachers gender X X X X X X X X X X public X X X X X X X X X X private X X X X X X X X Level of education X X X X X X X Length of service X Trained/untrained X X X X X X Non-Teaching Staff- 3rd Term 2008 where applicable gender X X X X X X X public X X X X X X X private X X X X X X X Standard/level Education Institutions Public X X X X X X X X X Private X X X X X X X X Standard/Level X School Facilities Public X X X X X X Private X X X X X

26  

DRAFT : Version 4

SCHOOL NAM

EDISTRI

CTOWNE

RSHIPREGIST SCHTY

PE

ADDRESS

PLACESelepa 2 2 8 CJSS P/Bag F133 FrancistownSelolwe 2 2 9 CJSS P/Bag 2 Monarch FrancistownMater-spei Stud 2 3 11 PRIVATE P/Bag F12 FrancistownT.K.M College 2 3 13 PRIVATE P.O.Box 21646 FrancistownPitikwe 3 2 5 CJSS P/Bag 0057 LobatsePhaphamang Priv 3 3 6 PRIVATE P.O.Box 952 LobatseKitso 3 3 7 Private P.O.Box 10273 LobatseCrescent 3 3 8 Private P.O.Box 20953 LobatseSelibe Phikwe 4 1 1 Sec. School P / Bag 2 Selibe PhikweBoikhutso 4 2 2 CJSS P/Bag 003,Botshabelo Selebi PhikweMeepong 4 2 3 CJSS P / Bag 4 Selebi Phikwe

The education system operates within 10 regions and 29 districts. The data collection process is also coordinated within these regions (10) and districts (29) in Botswana and is also published as such (Statsbrief 2010 and Annual Report 2007) as indicated by Table7.2 on the primary school enrolment in 2010 below. The master list of institutions for secondary schools obtained from the MoESD also makes provision for these geographical boundaries, as indicated in Table7.3 below. Note that a code for each district has been allocated in the list although no school code for each institution was indicated. In our investigation it was found that even within the DPSR there are several separately maintained lists of schools, containing different school identification numbers The purpose of a school code is to uniquely identify the institution in data sets and to link different data sets together. DTVET has divided their institutions in only two regions and BOTA collect information per institution. Table7.2: Primaryschoolsenrolmentperregionbygender2010

Source: Statsbrief2010Table7.3: Masterlistofsecondaryschools(Source:ListobtainedfromMoESD)

Boys Girls Total PercentCentral 59 898 56 906 116 804 35%Chobe 1 638 1 614 3 252 1%Gantsi 3 608 3 510 7 118 2%Kgalagadi 4 648 4 256 8 904 3%Kgatleng 7 414 6 869 14 283 4%Kweneng 24 041 22 745 46 786 14%North East 12 891 12 409 25 300 8%North West 14 641 14 033 28 674 9%South 21 917 20 577 42 494 13%South East 18 860 18 721 37 581 11%TOTAL 169556 161640 331196 100%

27  

DRAFT : Version 4

7.3.3. Classification/sectorization:Classificationandsectorizationsystemsareinaccordwithnationalandinternationallyacceptedstandards,guidelines,orgoodpracticesAlthough not providing all elements to identify the full set of ISCED level, the questionnaires for data collection, over the whole, provide adequate data to submit the international questionnaires. The listed data collection instruments attempt to cover all levels of the education. Although not strictly according to the ISCED classification, the data is categorized according to public and private: ECD, primary, secondary and tertiary education, and an agreed mapping of the education system exists and is applied. The 2007 Annual Statistics Report (not published) shows this classification of ISCED levels with the corresponding mapping of the education system of Botswana. A Data Plan1 exercise was recently conducted in the country by UIS. The latest version of the ISCED mapping2 is included in this report (see AppendixC) and the team responsible for the dataset is aware of it and is as such acknowledged. According to the UIS Data Plan (2011) in the international data collection, a school is classified as private if the governing board has the ultimate decision to close the school, irrespective of an education ministry’s control of a school’s human resources. In Botswana, the MoESD considers government-aided private institutions as public when it reports its enrolment counts. Since government-aided schools in Botswana have independent governing boards however, they must be classified as private to meet international criteria when reporting data with UIS Questionnaire A.

7.3.4. Basisforrecording:Dataisrecordedaccordingtointernationallyacceptedstandards,guidelines,orgoodpracticeThe data collection instruments, annual and first term in MoESD, collect detailed data on age by standard per grade (First term F5a, 2010), but it is not published in this format by age. These instruments collect data on repeaters and dropouts by gender per standard. Both the annual survey and the first term survey instruments in MoESD collect data on the number of streams per grade. DTVET collect data at student level per programme and BOTA uses a spot survey questionnaire to verify the enrolment per institution.

                                                            1 A Data Plan (DP) is a form of technical assistance that involves a proactive approach to assist countries in their ability to provide quality education data in international data collections. 2 An ISCED mapping clearly associates each national education program with an ISCED level.

28  

DRAFT : Version 4

In terms of recording system and, as discussed under section 6 above, we could not obtain any database system. The MoESD is currently investigating options for the development of an information system. It is important to note, from a technical point of view, and within a database-driven environment such as the MoESD, that to properly manage the data and to build-up historical data over time (time series data) a Relational Database Management Systems3 (RDMS) is required.

                                                            3 A Relational Database Management System (RDMS) is a system in which data is stored in the form of tables and the relationship among the tables is also stored in the form of tables. The relational structure makes it easy to query the database and to integrate large datasets from multiple sources. Data integration generally means linking different data sources through a common field across a collection of data sources. To be able to do this, unique identifier codes must be assigned to the datasets that are used for the integration. Another key concept in RDMS is referential integrity, a concept that ensures that relationships between tables remain consistent. In other words, when one table has a foreign key to another table, referential integrity states that one may not add a record to the table that contains the foreign key unless there is a corresponding record in the linked table (it includes concepts such as ‘cascading delete’ and ‘cascading update’). Normalization is another important concept that ensures that the data in the database is efficiently organized, by eliminating redundant data and ensuring that dependencies make sense. These functionalities are critical for data use and data analysis and make the integration of different datasets possible. 

29  

DRAFT : Version 4

Accuracyandreliability:Sourcedataandstatisticaltechniquesaresoundandstatisticaloutputssufficientlyportrayreality This dimension of quality is based on the principle that data produced give an adequate picture of the reality of the education sector in Botswana. Therefore, this dimension is specific for each data set and reflects the specificity of its sources and treatments. The elements of this dimension cover:

source data statistical techniques assessment and validation of source data assessment and validation of intermediate data and statistical outputs, and revision studies

7.3.5. SourcedataavailableprovideanadequatebasistocompilestatisticsAdministrative school census program: The education statistics on enrolment and education resources are collected through a regular administrative school census program by various units within the MoESD such as DPSR, DTVET, BOTA and TEC. For more details on the procedures and periodicity of each of these data producing agencies refer to point 5 above - “MoESD Data Collection Processes”. The MoESD has two data collection processes during the year. The first term data collection takes place at the beginning of the year for the primary and secondary schools. An annual school survey, the more comprehensive instrument for pre-primary, primary secondary and vocational and training institutions, takes place later in the year. The firstterm instrument collects data such as enrolment by gender per grade, enrolment by age and gender per grade, repeaters by gender and age per grade, total streams per grade, dropouts by gender and grade per reason, teachers by nature of appointment, and by gender and qualifications of teachers. From the first term survey instrument and the data elements that are collected, there is an adequate basis to compile statistics. MoESD collects data from all public and private schools and the MoESD is also organized according to regions and districts. The education statistics are therefore compiled by geographic areas, such as regional and national. The data for the MoESD was not available at school level. The education statistics are collected in terms of sub-groups such as male and female students and teachers, public and private schools, and trained and untrained teachers. These sub-groups are taken into consideration when data is compiled and published. The coverage of enrolment of students and teachers is published according to region by gender and school ownership (public and private). Table7.2above on Primaryschoolsenrolmentperregionbygenderindicates the coverage of enrolment for primary schools by region and gender as published in the Statsbrief (2010). Table7.4 below shows the data collected for teachers is published according to trained and untrained by gender (Statsbrief, 2010) per year. Data for secondary school is not published in the Statsbrief (2010), although the survey instrument was available.

30  

DRAFT : Version 4

2003 2004 2005 2006 2007 2008 2009 2010Trained 11,375 11,760 11,708 12,290 12,779 12,989 12,964 12,698Untrained 1303 957 939 722 201 388 209 69012,678 12,717 12,647 13,012 12,980 13,377 13,173 13,388

Table7.4:Primaryschoolstrainedanduntrainedteachersperyear(Source:Statsbrief2010) The annualsurvey instrument is even more comprehensive and collects data elements such as enrolment by grade and gender, teachers by gender and nature of appointment, repeaters by grade and gender, dropout by gender per reason, teachers by gender and qualifications, school facilities and support staff. With such data collections the ministry has adequate source data to compile education statistics and publish it on an annual basis. It was noted earlier that the low response rate seriously affects the timelines of the data. The annual data collections provide comprehensive data to calculate education indicators and determine the efficiency of the systems. Relevant indicators such as repetition rate, dropout rate and progression rates are calculated. The Department of Vocational Education and Training collects data in 8 technical colleges and 39 Brigades. These institutions are divided and organized in 2 regions. The department has developed two separate data collection instruments to collect data per student and per lecturer for each institution per programme. The data that is collected is for administrative purposes only and not shared with any other unit within or outside the MoESD and is also not published. The MoESD is in the process of piloting a questionnaire that will collect data from vocational education and training institutions. The data elements on the questionnaire are BTEP enrolments, teaching staff by gender and qualifications. TEC also collects data from institutions for students and teaching staff. The data elements collected with an Excel spreadsheet are individual teaching staff members’ personal details, experience and qualifications; individual non-teaching staff members’ personal details, experience and qualifications; student enrolment per programme by gender, full-time, part-time, repeaters by gender, dropouts by gender, student totals by age ranges (<18, 18, 19-23, 24, >24). From these, detailed data is compiled and published such as Tertiary Education Gross Enrolment, distribution of enrolment by type of institution, tertiary enrolment by gender (see Figure7.1 below), etcetera.

31  

DRAFT : Version 4

0

  5 000

  10 000

  15 000

  20 000

  25 000

  30 000

  35 000

  40 000

  45 000

  50 000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

UNPD

CSO

Figure7.1: TertiaryenrolmentbyGender(Botswana)‐2004/05–2008/09Source:TertiaryEducationataGlance(October2008)Household surveysandpopulation censuses:Household surveys are important instruments for data collection in all-statistical organisations over the world. They also collect data on specialised topics, which may have been covered during a census with greater depth. Survey data are also used to, at times, validate or complement data from other sources. Household Surveys became a programmed feature of the Central Statistics Office (CSO) work programme in 1983 (Available from: http://www.cso.gov.bw/). The populationcensus data projections are available by single age per year and published in the 2007 annual report. This data could be used to validate and or even complement the data that is collected by the MoESD. Figure 7.2 shows the analysis of CSO available population projections. Figure7.2: CSO2009SingleAgePopulationProjections(SourceCSO)

32  

DRAFT : Version 4

When analysing 2009 single age population projections from CSO one can observe a discrepancy with the United Nation Population Division (UNPD)4 projections in the years 1 to 9. This may not have much impact on the calculation of NER and GER (although it will in the coming years) but will be more meaningful analysing schooling profiles and age specific enrolment rates. This drop for the 4 year olds is not explained and it would be recommended that CSO work on some technique to smooth the projection. A HouseholdIncomeandExpenditureSurvey (HIES) was conducted in 2002/3 and include important tables on elements such as highest educational level attained (Table7.5 below) that can be used to validate or complement the education data collected by the first term or annual survey. Unfortunately data is published by age groups which don’t correspond with the age groups for primary and secondary education levels. Table7.5:Populationaged2yearsandoverwhoarestillatschool,bysexandstrata Source:HouseholdIncomeandExpenditureSurvey2002/03The Botswana Demographic Survey (BDS) 2006 is the third to have been conducted in Botswana. The first and second BDS were conducted in 1987 and 1998 respectively. The main objective of the 2006 BDS was to update statistics collected during the 2001 Population and Housing Census and to provide data that can be used during the intercensal period. The Household questionnaire was divided into various sets of questions with among the major ones, education and training. Table7.6, included in the BDS, shows that the majority of the population who has ever attended school has either attained primary (43.4 percent) or secondary (53.3 percent) education. Approximately 1.2 percent has non-formal education. There are more females compared to males who have attained secondary education.

                                                            4 The United Nation Population Division is responsible for monitoring and appraisal of the broad range of areas in the field of population.

33  

DRAFT : Version 4

Table7.6:HighestLevelofEducationAttainedbyGender Source:BotswanaDemographicSurvey (BDS) 2006 Literacy survey:The objective of the literacy survey that was conducted in 2003 was to measure the country’s literacy levels by educational attainment at both formal and non-formal schools. The data collected in the survey covered topics such as demographic data, educational background, socio-economic background, nature of literacy program attended, impact of literacy program, workplace literacy program, graduate rate for literacy program, dropout rate, functional literacy, et cetera. The 2003 survey indicated a national literacy rate of 81% compared to 68.9% in 1993 (Report of Second National Literacy Survey, 2003). Statistics on assessments of student achievement:TheBotswana Education Council (BEC) under Section 5 of the Botswana Examinations Act No. 4 of 2002, “conduct(s) schoolexaminations and any other examination forMoESD and issue(s) certificates in respect ofsuch examinations”. BEC has taken over the responsibility for the development and administration of all national and primary school examinations including the Primary School Leaving Examination (PSLE), Junior Certificate Examination (JCE) and the Botswana General Certificate of Secondary Education (BGCSE). Assessments include background questionnaires of students and school administrators (principals), for the purpose of being able to study the relationships between family, socio-economic, and school factors contributing to learning outcomes. Data is collected on individual students. Each student has an identification number that is assigned on registration and each institution (examination centre) has a unique number. Assessments cover key competencies in areas of learning such as reading and writing, mathematics, and science. Table 7.7 shows the Primary School Leaving Examination results by subject and gender for 2008.

34  

DRAFT : Version 4

Table7.7:PrimarySchoolLeavingExamination(PSLE)results2008 Source:BECAnnualReport2008/09The Junior Certificate Examination (JCE) assesses the achievement of learners who have completed the last three years of the 10-year basic education programme. According to the BEC Annual report 2008/9 a total of 38293 sat for the 2008 JCE. The number of candidates awarded merit increased from 9 in 2007 to 69 in 2008. The Botswana General Certificate of Secondary Education (BGCSE) assesses the achievement of learners who have completed 12 years of formal education. The total number of candidates in 2008 were 30560 (22452 Government and Government-aided, 2103 Private schools and 6005 private candidates) as recorded in the BEC Annual report 2008/9. Botswana is one of the countries in the Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) survey. SACMEQ has a dataset that contains information on school-based surveys, including students’ tests, from 15 countries that belong to the Southern and Eastern African Consortium for Monitoring Educational Quality.

7.3.6. Assessmentofsourcedata:SourcedataareregularlyassessedandvalidatedAlthough no official audit takes place to validate and assess the data that is provided by the head teachers there are specific measures in place to verify the data. The primary schools have a process in place where they collectively check and validate the data of each school during the first term. After the form is completed it is sent to the education officer responsible for the area (inspectorate). A workshop is conducted in the inspectorate area where all the head teachers gather. The forms are validated, verified and checked and a summary is compiled for the inspectorate area. This entire process is a measure at regional level to verify the questionnaires and to improve the quality of the data. There is no dedicated person at regional level to deal with the data-related requirements. The secondary schools use the pastoral visits of the inspectors to verify and check the survey questionnaires. A pastoral visit is a visit by an inspector to check the effectiveness and efficiency of service delivery at institutional level. BOTA uses a “spot survey” once a year to ensure that the enrolment provided by institutions is accurate. The training that is

Male Female AllSetswana 20168 20806 40974English 20426 21040 41466Mathematics 20420 21035 41455Science 20423 21036 41459SocialStudies 20425 21036 41461Agriculture 20423 21037 41460ReligiousandMoralEduca20409 21012 41421

35  

DRAFT : Version 4

provided to ensure that survey questionnaires are completed correctly is provided on an irregular basis. During our visits to the schools it seems that the use of school registers is very effective and helps to improve the quality of the data. It was confirmed by head teachers visited that students dropping out are removed from the register and this was appropriately indicated in the registers that we could obtain. It was also indicated on the register when a student is transferred to another school. There is an official form used to facilitate this process. The school register seems to be an important official document that is used to improve the quality of the data. It serves as an official document to record all the students in the school. Details of students, such as date of birth, are recorded using official source documents such as a birth certificates or hospital cards. The head teachers ensured us that it is not really a problem to obtain these documents in Botswana. 7.3.7. Statisticaltechniques:Statisticaltechniquesemployedconformtosound

statisticalprocedures,andaredocumentedSome cross-table checks and data entry formats are integrated in the system. In the annual survey questionnaire, a summary table for enrolment by gender per standard is provided. This table can be used as a “control table” to cross-check all the enrolment-related data such as enrolment by age and gender. A summary table for teachers by gender and nature of appointment is also provided. An instruction in the questionnaire “Totalno.ofteachersinSectionH,should tallywith totalnumberof teachers inSectionA”, refers to this table and uses it as a control table. Although a master list of institutions could be obtained, it seems that more than one master list of institutions exists in different units in the ministry. We found that there are different lists of the institutions which are not maintained and updated at one central place. Even within the DPSR there are several separately-maintained lists of schools with a different school identification number. At this juncture it is appropriate to mention that the School Registration Unit in the Division Planning, Statistics and Planning (DPSR) is the most logical place to maintain and update the master list of institutions. Procedures for opening and closing of institutions already exist within this unit. Currently it is a manual process which should be changed to an electronic system. The purpose of such a system is to assign a unique identifier to every institution in the country. The basic functioning of the School’s Unique Identifier System works in the following way:

The School Registration Unit assigns each school a unique national institutional identifier (code) that can be used to match records accurately across years. The School Registration Unit develops procedures to ensure that two institutions are not assigned the same number. The Master table has a specific number of key data fields, such as a field to link EMIS data with other data sets. Other fields that could be included in this table could be geographical areas (local, regional and national) and groups such as public and private.

36  

DRAFT : Version 4

Missing data treatment at MoESD seems to be processed by imputation of previous year data but this can be improved by defining enrolment progression. VET and Tertiary Education seem not to address this issue. 7.3.8. Revisionstudies:Revisions,asagaugeofreliability,aretrackedandmined

fortheinformationtheymayprovideLittle to no evidence was found of revision studies, or general reviews of methodologies used.

37  

DRAFT : Version 4

7.4. Accessibility:Dataandmetadataareeasilyavailableandthereisadequateclient(user)supportThis dimension is based on the principle that data and metadata should be presented in a clear and understandable way and should be easily available to users. Metadata should also be relevant and regularly updated. In addition, assistance to users should be available, efficient and performed in a reasonable time frame.

7.4.1. PublicawarenessarounddatadisseminationproductsThere was no pre-announced schedule available with dates indicating the release of the statistical data to users. The publications however, are available on the website of CSO (http://www.cso.gov.bw). Education data of the first term is published in the Education Statsbrief every September and the Education Statistics Report is published annually. These publications are hampered by the fact that timeliness is not good. Only primary school data is published in the last Education Statsbrief 2010 and the last Education Statistics Report was published in 2006. The Literacy Survey Report is also available on the website. 7.4.2. EaseofaccesstoinformationIn the Stats Brief 2010 and the Education Statistical Report 2006 the education data is published in a clear manner, charts and tables are disseminated with the data to facilitate the analysis. In these publications education indicators such as enrolment ratios, dropout and repeater rates; progression rates and teacher details are supported with tables and charts. In the publications of TEC, Tertiary Education at a Glance 2008, tertiary education indicators such as Participation at Tertiary level, Tertiary enrolment by Gender (see Figure 3 above) and Tertiary Education Gross Enrolment Ratio the data analysis is also supported by tables and charts. BOTA has a monthly Bulletin, but no publications are available to the public and DTVET also don’t publish their data. The education data that is available does not offer adequate details to the public. In the Statsbrief 2010 the data is published at regional level and in the Education Statistical Report the data is available at district level. The data in the MoESD is not available at school level. The antiquated information system makes it impossible to retrieve data from the system per school because no competencies are available to perform this function. 7.4.3. Metadata:themetadataincludediscussionsaboutconcepts,scope,

classification,definitions,baseofrecording,datasources,methodology,statisticaltechniquesandanyotherissuesaffectinginterpretabilityMetadata is useful to data production agencies and users of the data. Metadata describes the content, quality and other characteristics of the data and helps users to locate and understand the data. Metadata provides a standard and common language about a set of terminology and definitions for the end user. We could not find an official document describing the data elements used in the datasets of MoESD. However, clarification notes are provided on the survey questionnaires, such as for trained and untrained teachers. A manual also exists for the annual survey questionnaire for primary schools (F5c) and annual survey questionnaire for secondary schools (F4c) that provides guidance on the

38  

DRAFT : Version 4

completion of these forms and consistency checks. The primary school manual for example tries to clarify the difference between temporary and reliefteachers. The manual further explains what is meant by streamswith a specific example, and ‘dropouts’ reflect pupils who dropped out of school during the previous school year. In the publication of TEC, Tertiary Education at a Glance (2008), a definition on concepts such as Tertiary Education Gross Enrolment Ratio (TEGER) for the 18-24 years and Participation in tertiary education is provided. 7.4.4. Assistancetousers:clientsupportsystemisinplaceThere seems no structured assistance to users in the MoESd and no organised and formal system in place to manage the interactions with the end-users of the data. There is also no technical support provided to the users of the data. The data that is available is through the publications such as the Statsbrief and the Statistical Reports. Data in these publications is only available in aggregated format. No technical support is available for users and the data in the MoESD or on the website is not available at the school level. Catalogues of publications, documents, and other services, including information on any changes, are also not available. Users’ requests are managed on an adhoc basis and no record is kept of these data queries. Keeping a record of the requests of users could help building up a history of the kind of data that is required by users. This information can be taken into consideration and will facilitate processes when data collection questionnaires are revised.

39  

DRAFT : Version 4

7.5. Serviceability:StatisticaldataaretimelyandconsistentThe quality dimension of serviceability looks at the extent to which statistics are useful for planning or policy purposes. It refers, mainly, to periodicity and timeliness, and consistency. Data is timely when it is current or up-to-date as defined by the owner of the data. Data must be on time and available when it is required, otherwise the credibility of the information system diminishes. Given that data is actually accurate, it looks at the extent to which they reflect a reality either of the moment or of the past. 7.5.1. Periodicityandtimeliness:Dataarepublishedinatimelyandperiodic

mannerData must be on time and available when it is required, otherwise the credibility of the information system diminishes. In this process to maintain the timeliness of data, accuracy could sometimes be impacted. The CSO's primary function, through the collection, processing and analysis of data, reporting, dissemination and publications of results, is to provide Government Ministries and Departments, Non-Governmental Organisations and members of the public in general, with information for monitoring, evaluation and formulation of development plans and programmes. The education statistics collected through the first term questionnaire is published in September each year. The Statsbrief is a summary of the education statistics of this first term school census and is published by CSO in collaboration with the MoESD. The Statsbrief 2010 (Available from: http://www.cso.gov.bw/) includes only primary school information, according to Statsbrief 2010: “theresponserateforsecondaryeducationstoodat44percentwhen the report was compiled hence no information was included in this report”. The Statsbrief 2010 is meant to give users an information snapshot for 2010. This publication includes education indicators such as enrolment, dropout rates and teacher qualifications aggregated by gender, standard and region. CSO in collaboration with the MoESD also collects and compiles education statistical information from institutions through a more comprehensive annual questionnaire which is also disseminated and published annually. The response rate for all institutions of this questionnaire is very low (±80%) and seriously affects the timeliness of the data. The MoESD annual statistical reports are two to three years behind the current calendar year. The last time that data was available for the annual publication was in 2007 a delay of almost 4 years. However the Statistics Report 2007 is not yet published at the time of writing this report, the latest Statistics Report 2006 was available on the website of CSO. CSO also collects data through household surveys, an important vehicle for data collection in all-statistical organisations the world round. The data collected at TEC is also compiled and disseminated on an annual basis in their publication Tertiary Education at a Glance. However the last publication that is available is Tertiary Education at a Glance 2008, a delay of 2 years. BOTA has a monthly Bulletin, but no publications are available to the public and DTVET also don’t publish their data.

40  

DRAFT : Version 4

No financial data could be found in the available publications or on the websites of CSO or MoESD. The questionnaires that we obtained also do not require any financial data to be reported. There is however, a Finance Unit in the MoESD with staff members seconded from the Ministry of Finance and Planning. This unit deals with the recurrent and development budgets and oversees the budget for the MoESD. It advises on issues such as budget preparation, monitoring and evaluation, and manages the day-to-day running of the Ministry. The Unit has access to the Government Accounting and Budgeting System that reports on all accounting and expenditure issues. According to the latest Data Plan (2011) the Questionnaire on Statistics of Educational Finance and Expenditure for 2010 was completed with the support of UIS. 7.5.2. Consistency:Statisticsareconsistentwithinadatasetandovertime,and

withothermajordatasetsTo ensure this data consistency, the MoESD has provided specific guidelines and checks (a) on the annual survey questionnaire, (b) in the data collection manual and (c) through a workshop verification process. Survey Questionnaire: On the annual survey questionnaire specific definitions and descriptions are provided to ensure that concepts and terminology are understood. Tables are used to verify and cross-checked against other tables with the same data. For example, an instruction note on the annual questionnaire for primary schools (F5c) reads: “Totalno.of teachers in SectionH, should tallywith total number of teachers in Section A”. In this questionnaire the summary tables on enrolment and teachers in section A are provided as control tables to cross-check the totals of tables with similar information.

Data Collection Manual: This manual is meant to assist schools to complete the questionnaire with minimal errors. Specific examples in the manual are given for consistency checks. For example, the total number of pupils in question 1.1 should tally with the figure given for reception enrolment in the summary table. A key instruction is about consistency in the information provided by the schools over the years to check the previous years’ returns, especially those that remain constant, such as facilities (number of classrooms). The data collection manual for primary schools explains the key function of the summary table in Section A: The sum of boarders and non-boarders gives the total enrolment. In cases where there are no boarders, as is the case with most of the schools, the number of non-boarders is equal to the enrolment for each standard and finally for the total. The information in this table tallies with the (a) enrolment figures for each standard with the respective tables in Section B; (b) number of boarders for each standard with the totals in Section E; (c) number of streams for each standard with the totals in Section C. The data collection manual for secondary schools explains for example how to complete the table with the teaching staff details. There is usually confusion of who should be treated as temporary or relief teacher. Therefore please note that the relief teachers (somebody who replaces another who is unable to work) are those teachers standing in for those on maternity or sick leave only. A temporary teacher fills in a vacant post or replaces a

41  

DRAFT : Version 4

teacher on study leave. The sum of teachers in posts and temporary teachers in the summary table should tally with the sum of citizen and non-citizen in Section I Q8 as well as Q12. Such consistency should also be reflected by sex. For example, if Section A shows 5 males and 20 females, then Section I Q8 and Q12 should also show the same. The enrolment details in Section B in this manual are also explained in detail. Students are categorised according to age and sex in their respective Forms. Therefore the total in each table in Section B should correspond with the total enrolment for each Form in Section A. For each Form there is a question for the disabled. The number of the disabled or students with special needs in a class cannot be more than the total number of students in that class. The sum of individual age totals should give an overall total which tallies with the Form enrolment in the summary table. The manual, represented diagrammatically in Figure7.3 below, provides an illustration on the corresponding fields in the different tables that should balance. Figure7.3: IllustrationofthefieldsinSummaryTableinSectionAcorrespondingwiththe

EnrolmentsDetailsinSectionBFORMSinSummaryTableinSectionA

1 2 3 4 5 6 Total

Enrolment 20 Boarders NonBoarders Streams/classes

SectionB:EnrolmentDetailsbyform,ageandsex. Q1.0Howmanystudentsareenrolledinform1bytheirages? Form1:

AgeSex Less

than12

12 13 14 15 16 17 18 19 olderthan19

Total

Male ‐ 1 3 3 3 ‐ ‐ ‐ ‐ ‐

Female ‐ 1 4 3 2 ‐ ‐ ‐ ‐ ‐

Total ‐ 2 7 6 5 ‐ ‐ ‐ ‐ ‐ 20

WorkshopProcessandotherdata verificationmethods:Another important process in the MoESD to ensure consistency of the data during the first term is the conducting of a workshop at inspectoral level for all primary schools. After the form is completed it is sent to the education officer responsible for the area (inspectorate). A workshop is conducted in the inspectorate area where all the head teachers gather. The forms are validated, verified and checked and a summary is compiled for the inspectorate area and sent to the region where in turn a summary for the region is compiled from all the inspectorate area. This

42  

DRAFT : Version 4

entire process is a measure at regional level to verify the questionnaires and to improve the quality of the data. This process ensures that there is almost a 100% return of questionnaires from the schools within the specified time period. The secondary schools, through their pastoralvisits, involve the inspectors to do some data checks and verification. DTVET has focal points in each institution as a contact person appointed to be responsible and mandated for the data. Officials from DTVET physically go to the institutions to verify the data and speed up the data collection process. An officer from DTVET goes to the college to cross-check student totals against the register and teacher time against the teacher’s time table. BOTA uses a spot survey process once a year to verify their data to ensure consistency. The information system (IMPS) in the MoESD, although it is much outdated and not supported anymore, has a built-in facility (validation rules) to validate and check the data that is entered. This ensures that no illegal data entry takes place. However, there are no verifications / comparisons of data from schools to schools over years. Intra/inter‐agency consistency (departmental entities reconcile their data when differentprocessesareusingdifferentinformationtoproducesimilaroutputs): The concept of sharing information is seriously underdeveloped in the MoESD. The MLG has a 10-day grace period form to collect data from primary schools at regional level, while MoESD has their own form for the first term and the annual data collection processes. These data sets are not reconciled or compared. DPSR uses the an official form to collect the data for first term and annual from the secondary school department, while the secondary school department uses his own internal form to collect data from schools without sharing it with DPSR. Both TEC and BOTA collect data in the same institutions neither of which is shared with each other or with the MoESD. This silo-based approach has resulted in fragmentation of processes and islands of datasets and eventually the duplication of effort and unnecessary wastage.

43  

DRAFT : Version 4

8. RECOMMENDATIONSBased on the above situational analysis and the accompanying findings, recommendations are put forward for the following categories: 8.1. Institutionalarrangementsandcoordinationamongconcernedstructures

Implement the recommendations of the O&M (2006) in terms of the organisational restructuring of the Monitoring and Evaluation function and provide more autonomy to DPSR in controlling its own funds. Staff the EMIS Unit with at least one additional officer at Headquarters and place at least one person (focal point) with an EMIS function at the regional level (there are no EMIS staff members in the regions, while most of the other Ministries and Departments have a presence at the regional level). Reinforce and institutionalise the relations between CSO and MoE and clarify the roles of EMIS and CSO in terms of questionnaire design, questionnaire dissemination, data collection processes, data compilation, data analysis, reporting and publications. It is recommended that CSO strengthen the ongoing effort for the promotion of the National Strategy for the Development of Statistics (NSDS). Establish a Quality group that associates EMIS and CSO staff and that should meet regularly according to a predefined schedule. It is recommended that the Education Act be revised to include statistical responsibilities and that the mandate of MoESD is clearly identified in terms of the scope and periodicity of the production of educational statistics. 8.2. Datacollectionprocesses

Develop, in collaboration with the Central Statistics Office (CSO), an EMIS Policy that will define data collection processes and data dissemination procedures. This policy could create a framework that allows for the coordinated and sustainable development of education information systems. It could also create a framework for establishing and maintaining effective and sustainable standards governing education statistics, data and information systems in Botswana. Improve forms regarding information on respondents’ rights and responsibility and make sure manuals are available Establish a survey registration system to ensure there is control over the different data collection requests that go to the schools. This should help to eliminate unnecessary duplication of effort and could ensure that there is only one entry point into the schools reducing the response burden at school level. All data collection processes should be harmonised through the control and management of a national unit (DPSR), taking into account the data needs at regional level. Implement a data collection and dissemination schedule with clearly defined deadlines of all the steps involved in the process. This schedule should be well distributed within and outside education.

44  

DRAFT : Version 4

Investigate the possibility of having only one data collection exercise for Headquarters during the year. 8.3. Informationsystems

Develop terms of reference (ToR) for an integrated information system that adheres to the functionality that is required within the MoESD and sub-national levels in a medium-term perspective of a decentralisation down to the institution level if required. Organise a consultative structure in the MoESD with the objective to finalise the strategy for the development / acquisition of a new information system. Develop norms and standards as requirements for the development of any system in the future in order to enhance and promote capacity building and harmonisation in the different departments of the MoESD. 8.4. Dataqualityissues

Prioritise support for and management of the regular Statsbrief publication. Update and maintain the master list of all educational institutions in Botswana. The School Registration Unit in the DPSR performs such a function but this is currently a manual process which should be automated and linked to the information system through a national unique school ID. Conduct regular revision studies and methodology assessments through data quality meetings. Develop procedures and activities to ensure that annual publications meet the necessary quality standards (completeness, timeliness and reliability). To that effect missing data treatment should be improved and generalised. Assistance to users should be structured and data requests kept under record for further improvement of data collection processes. 8.5. Capacitybuilding

Conduct a more detailed skills inventory and design a capacity building strategy which would be grounded in the ministry activities hands on application of acquired competencies. Conduct training and capacity-building based on the national needs and objectives. A practical exercise would be the production of a Country Status Report that would provide a clear picture of the education sector performance following a socio-economic approach. Adequate training should be conducted for head teachers (school registers, data collection, education indicators), inspectors (data quality) and regional staff (EMIS, planning). 8.6. Nextstep:preparationofActionPlan

There is a need for the development of an Action Plan that will identify and prioritise a set of actions needed to address the weaknesses identified by the diagnostic study. It will also identify a coherent framework for capacity building of education statistics, and will identify the costs associated with implementation of the plan. It will serve

45  

DRAFT : Version 4

therefore as the basis for discussion, and for mobilising additional resources as required to cover the in-country costs associated with implementation of the plan. The plan should include:

o The development and implementation of an EMIS Policy and identification of an EMIS expert to lead such a process. o The development of a “hands-on learning by doing” capacity-building strategy for education planning and EMIS. o A sound assessment of potential technical solutions that will guide the choice of a proper information system.

Prior to the development of the plan and to ensure its successful implementation, it is again recalled that the implementation of the O&M recommendations should be prioritised, and in particular: o The establishment of a dedicated planning unit (structure) o The staffing of the central EMIS Unit o The allocation of regional EMIS officers

 

46  

DRAFT : Version 4

9. CONCLUSION

Figure9.1:Overallresults

1

2

3

4Pre-requisites of quality

Integrity

Methodological soundness

Accuracy and reliability

Accessibility

Serviceability

Gen Ed

TEC

VET The current situation of EMIS in MoESD in Botswana demands the decision-makers to take initiative and introduce changes for collaboration among the various data producing agencies and integrate the data collection processes and systems where possible. The integrated approach will not only save the resources but will also improve the efficiency of the information systems and the quality of the data, but will eliminate unnecessary duplication and wastage. The following points highlight the overall findings and accompanying recommendations for improving the Botswana system for educational statistics. Statistics is not only about computers and processes. We wanted to emphasize that statistics is more than just a technical solution, but that the important aspects of legislation, governance, regulation, resources, people, systems and processes should be part of such a process. The findings and accompanying recommendations are based on these aspects in support of government decision making and service delivery and are discussed throughout the document in the following categories, namely, legislation, planning, processes, resources, decentralisation, and information systems.

47  

DRAFT : Version 4

10. AppendixA:ListofrelevantreferencesanddocumentsNr Details Medium Year Issuer 1 The Revised National Policy on Education, Government Paper No. 2 of 1994 Document April 1994 MoESD 2 Organisation and Methods Document: The Report on the Organisational Restructuring of the Ministry of Education: Final Report Document October 2006 MoESD 3 National Development Plan 10 April 2009-March 2016: NDP10 Towards 2016. Document December 2009 Ministry of Finance and Development Planning 4 UNESCO (2011): Available from: http://www.unesco.org/new/en/education/themes/planning-and-managing-education/policy-and-planning/emis/). Reference March 2011 UNESCO 5 Schools Administration and Student Management (SASM), Part 1, Situation Analysis Report Report April 2008 MoESD 6 National Credit and Qualifications Framework (NCQF) Briefing Documents, Report November 2010 NCQF

48  

DRAFT : Version 4

7 Data Plan Ministry of Education and Skills Development Republic of Botswana, Prepared by the UNESCO Institute for Statistics, UNESCO Cluster Office in Windhoek, Namibia Report February 2011 UIS, Windhoek 8 Botswana Examinations Council Act (Chapter 58:03). Act November 2002 Parliament 9 Education Act (Chapter 58:01). Act February 1967 Parliament

10 Local Government (District Councils) Act (Chapter 40:01). Act December, 1965 Parliament 11 Public Service Act Act 1998 Parliament 12 Statistics Act (Chapter 17:01). Act October, 1967 Parliament 13 Teaching Service Act (Chapter 62:01). Act April, 1976 Parliament 14 Tertiary Education Act (Chapter 57:04). Act April, 1999 Parliament15 Vocational Training Act (Chapter 47:04). Act January Parliament

49  

DRAFT : Version 4

2000 16 STATS BRIEF 2010: No: 2010/18 Publication 2010 CSO17 STATS BRIEF 2009: No: 2009/14 Publication 2009 CSO 18 STATS BRIEF 2008 Publication 2008 CSO 19 Education Statistics 2006 Publication 2006 CSO/MoESD 20 Education Statistics 2006 Publication Unpublished CSO/MoESD 21 Tertiary Education at a Glance 2008 Publication 2008 TEC 22 Report of the Second national Survey on Literacy in Botswana Publication 2003 CSO/Non-Formal 23 Botswana Examinations Council Annual Report 2008/2009. Publication 2009 BEC 24 The Department of Vocational Education and training, Annual Report 2007-2008 Publication 2008 DTVET 25 THUTHO, Issue 54 Publication MoESD 26 Baseline Survey of the Vocational Training Sector in Botswana, Volume 1&2. Botswana Training Authority Publication 2006 BOTA 27 Household Income and Expenditure Survey 2002/2003, main Report, Publication December 2004 CSO

50  

DRAFT : Version 4

Volume 1 28 Questionnaire’s Manual Primary School Annual Return (Form 5c) Document MoESD 29 Questionnaire’s Manual Secondary School Annual Return (Form 4c) Document MoESD 30 2011 Primary School First Term Summary (Moesd F5a), Ministry of Education and Skills Development Questionnaire 2011 MoESD 31 2011 Secondary School First Term Summary (MoESD F4a), Ministry of Education and Skills Development Questionnaire 2011 MoESD 32 2010 Pre School Annual Return (MoESD F6c), Ministry of Education and Skills Development Questionnaire 2011 MoESD 33 2011 Primary School Annual Return (F5c) Questionnaire 34 2011 Secondary School Annual Return (F4c) Questionnaire 2011 MoESD 35 2010 Technical, Vocational Education and Training Centres & Brigades Annual Returns Questionnaire 2011 MoESD 36 Teacher Training Colleges Annual Questionnaire 2011 MoESD

51  

DRAFT : Version 4

Statistics Return, Ministry of Education and Skills Development 37 Primary Schools Monthly Reporting Tool, 2010 Questionnaire 2010 MoESD 38 Primary schools 10 day grace period form Questionnaire 2010 MLG 39 BOTA questionnaire Questionnaire 2010 BOTA 40 TEC Questionnaire Questionnaire 2010 TEC41 http://www.bec.co.bw/ website 2011 BEC 42 http://www.bota.org.bw website 2011 BOTA 43 http://www.cso.gov.bw/ website 2011 CSO 44 http://www.laws.gov.bw) website 2011 Parliament 45 http://www.moe.gov.bw/ website 2011 MoESD46 http://www.tec.org.bw website 2011 TEC 47 (http://www.census.gov/ipc/www/cspro/aboutcspro.html). website 2011 U.S. Census Bureau

52  

DRAFT : Version 4

11. APPENDIXB:ListofPersonsMet

DepartmentofPlanning,StatisticsandResearch–14February2011Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Elisha Rangaka Principal Education Officer School’s Registration eranga@gov.bw Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bwBoikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Mmamiki P. Peloewetse Statistician I Education Statistics ppeloewetse@gov.bw K. Mathabathi Research Officer Research Unit kmathabathi@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Ince Chepete Principal Systems Analyst II IT Unit kchepete@gov.bw Tshoganetso Madema Acting CEO DPSR tmadema@gov.bwDavid Brocks Tlhage Principal Planning Officer II Planning Unit dtlhage@gov.bw

HIV/AIDSUnit–14February2011Name Surname Position Unit EmailAddressBoikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Purene Bareetsi Ministry AIDS Coordinator HIV/AIDS Unit pbareetsi@gov.bw Thekiso Clement Zulu M & E Officer HIV/AIDS Unit tzulu@gov.bw Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bw Marc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za

Pre‐andPrimaryEducation–15February2011

53  

DRAFT : Version 4

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Tiny Ntshinogang Principal Education Officer I Pre-School tntshinogang@gov.bwKhoza Ramahobo Acting Director Pre- and Primary Education kramahobo@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org DepartmentofSecondaryEducation–15February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Susan Matroos Principal Statistician Education Statistics sumatroos@yahoo.com sumatroos@gov.bw Simon Coles Acting Director Secondary Education scoles@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer DPSR bmonyaku@gov.bw SchoolRegistrationUnit–15February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Elisha Rangaka Principal Education Officer School’s Registration eranga@gov.bw

54  

DRAFT : Version 4

Susan Matroos Principal Statistician Education Statistics sumatroos@yahoo.com sumatroos@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org EMISUnit–15February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Susan Matroos Principal Statistician Education Statistics sumatroos@yahoo.com sumatroos@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za DepartmentofVocationalEducationandTraining(DVET)–16February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Batsho Mabechu Intern Policy and Development - DVET Batsho_mabechu@yahoo.com Dalitsho Chitema PTEO II (Policy) Policy and Development - DVET dchitema@gov.bwSusan Matroos Principal Statistician Education Statistics sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org

55  

DRAFT : Version 4

Mosetsana Maripe Principal Technical Education Officer I Policy and Development - DVET mkgosiemang@gov.bw ITUnit–16February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Millicent Kgosintwa Assistant Systems Analyst IT Unit mmakumetsa@gov.bw Abel Sono Systems Analyst I IT Unit asono@gov.bw Stephen Keorete Principal Systems Analyst IT Unit skeorete@gov.bw Ince Chepete Principal Systems Analyst II IT Unit kchepete@gov.bw Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bwBoikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw TertiaryEducationCouncil– 16February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Noah Salakae Knowledge Officer Data Management nsalakae@tec.org.bw Masego Mokubung Director Knowledge Management mmoubung@tec.org.bw Morake Matlhaga Manager, Financial Planning Institutional Funding mmatlhaga@tec.org.bw Reinhart Dreves Technical Advisor - ESO Tertiary Education Council rdreves@tec.org.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bw

56  

DRAFT : Version 4

Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw MeetingwithActingDeputyPermanentSecretary– 16February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Kgopotso Ramoroka Director ICT & Media Services kramoroka@gov.bw thukharam@yahoo.com Acting Deputy Permanent Secretary Tshoganetso Madema Acting CEO DPSR tmadema@gov.bwRoselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org PlanningUnit–16February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za David Brocks Tlhage Principal Planning Officer II Planning Unit dtlhage@gov.bwSteinberg Gabanakemo Planning Officer Planning Unit sgabanakemo@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bw FinanceUnit– 17February2011

Name Surname Position Unit EmailAddress

57  

DRAFT : Version 4

Marc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) Ponalo Ditshotlo PFO Finance Unit pditshotlo@gov.bwChris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bwRoselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bwPerformanceImprovementUnit–17February2011

Name Surname Position Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Chabaesele Oaitse PIC - Regions Performance Improvement Unit coaitse@gov.bw Moses Tshetlhana PIC - HQ Performance Improvement Unit mtshetlhana@gov.bw Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw EuropeanUnion–17February2011

Name Surname Position Unit/Organisation EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Vincent Vire Programme Manager European Union vincent.vire@ec.europa.eu Rigo Belpaire Head of Operations European Union rigo.belpaire@ec.europa.eu

58  

DRAFT : Version 4

Susan Matroos Principal Statistician Education Statistics sumatroos@gov.bwRoselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw RainbowPrimarySchool–17February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Stephen Hamombe H.O.D. CAPA Rainbow Primary School stephenha1953@yahoo.com Philip Huebsch Headmaster Rainbow Primary School philip.huebsch@gmail.com Ladla Altschul Deputy Rainbow Primary School l.altschul@info.bw Alexander Ghanney Senior Teacher Rainbow Primary School alexghanney@yahoo.com Petronilla Mhundwa H.O.D. Junior Rainbow Primary School Moses Kazevu H.O.D. Middle School Rainbow Primary School moska_1958@yahoo.com Samuel Kalavina H.O.D. Upper Primary Rainbow Primary School samkalavina@yahoo.com Hillary Siandula H.O.D. Std 7 Sets. French Rainbow Primary School hsiandula@yahoo.com Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Susan Matroos Principal Statistician M.O.E & S.D. (CSO) sumatroos@gov.bwBoikhutso Monyaku Principal Education Officer M.O.E. & S.D. (EMIS) bmonyaku@gov.bw KumakwanePrimarySchool– 18February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za

59  

DRAFT : Version 4

Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bwMompoloki Batlhophany Deputy School Head Kumakwane Primary Bokane Monepe School Head Kumakwane Primary Ednah Ntloyamodimo H.O.D. L/D Kumakwane Primary Eva Desai H.O.D. Upper Kumakwane PrimaryDithare Tswalle H.O.D. Middle Kumakwane Primary Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org KagisoSeniorSecondarySchool– 18February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Mojuda Sebina H.O.D. Kagiso S.S.S. Gothata Mothusi Bursar Kagiso S.S.S. Segelelo Baratlang A-A Kagiso S.S.S. G. Tilly Dinku Deputy Kagiso S.S.S. Seeletso Selolwane Admin Officer Kagiso S.S.S. Tiny R. Tubego H.O.D. Kagiso S.S.S. Lapologang Kolagano School Head Kagiso S.S.S. Roselyn Wabuge-Mwangi Programme Specialist Education – UNESCO (Harare Cluster Office) r.wabuge-mwangi@unesco.org Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bwTeacherTrainingandDevelopment–21February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics m.bernal@uis.unesco.org

60  

DRAFT : Version 4

(Nairobi) Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Opaletswe Baipoledi PEO II (PDD) Teacher Training and Development obaipoledi@gov.bw Dugmore Tselayakgosi PEO I (IDM) Teacher Training and Development dtselayakgosi@gov.bw Phillip S.R. Tsumake Director Teacher Training and Development ptsumake@gov.bwSusan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics f.borgatta@uis.unesco.org

KgatlengRegion– 21February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Sinah Mogobye Principal Education Officer Inspectorate (Kgatleng Region) Raymond Themba Principal Education Officer II Education (Kgatleng Region) Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw

61  

DRAFT : Version 4

CentralStatisticsOffice–21February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Malebogo Prisca Kerekang Deputy Government Statistician Central Statistics Office mkerekang@gov.bw Diemo Motlapele Chief Statistician: Social Division Central Statistics Office dmotlapele@gov.bw Susan Matroos Principal Statistician Central Statistics Office sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer EMIS bmonyaku@gov.bw Maclean Gwafila Principal Statistician Central Statistics Office mgwafila@gov.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics

DepartmentofOutofSchoolEducationandTraining(DOSET)–22February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za

62  

DRAFT : Version 4

Baipidi Kgabi Principal Adult Education Officer DOSET bkgabi@gov.bwAnnah Maruatona Principal Adult Education Officer DOSET amaruatona@gov.bw Mmoloki Nkele Assistant System Analyst M.O.E. & S.D. mmnkele@gov.bw Katoto Hambisa M&E DOSET kahembisa@yahoo.com O Leteane A.S.A. DOSET orleteane@gov.bw Antoinette Motiki Principle Adult Education Officer II DOSET amotiki@gov.bw M Seshika Principle Adult Education Officer II DOSET mseshika@gov.bw Thebenala Thebenala Director DOSET tthebenala@gov.bw Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics

MinistryofLocalGovernment–22February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za

63  

DRAFT : Version 4

Tebogo Rapalai P.E.P.O. DLGFP trapalai@gov.bwJimmy Singabapha Assistant Director DLGFP jpsingabapha@gov.bw Gideon Tembo Principal Architect DLGTS gtembo@gov.bw Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD(EMIS) bmonyaku@gov.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics BotswanaTrainingAuthority(BOTA)–23February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Chandapiwa Mogobe Economic & Strategic Planner BOTA cmogobe@bota.org.bw Boipelo Frank Nthawe Systems Analyst BOTA bnthawe@bota.org.bw Matthew Phiri Executive Coordinator BOTA mphiri@bota.org.bw Sannah Bathai SR BOTA sbathai@bota.org.bw Tshireletso Baloi Intern BOTA tbaloi@bota.org.bw Canaan Mathendele IT Manager BOTA cmathendele@bota.org.bw

64  

DRAFT : Version 4

Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics ResearchUnit–23February2011

Name Surname Position Institution/Organization/Unit EmailAddressMarc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Fernando Siamisang Head (Research Unit) DPSR – Research Unit fsiamisang@gov.bw Onkabetse Mmereki PEO II (Research) DPSR – Research Unit oammereki@gov.bw Marc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw

BotswanaExaminationsCouncil(BEC)–24February2011

Name Surname Position Institution/Organization/Unit EmailAddress

65  

DRAFT : Version 4

Marc Bernal Regional Advisor UNESCO Institute for Statistics (Nairobi) m.bernal@uis.unesco.org Chris Van Wyk Consultant Stellenbosch University chrisvanwyk@sun.ac.za Trust Mbako Masole Senior Researcher BEC masoletm@gmail.com Chendzimu Makobole Senior Data Administrator BEC cmakobole@bec.co.bw Othusitse Siele SPO BEC osiele@bec.co.bw Kgosi Motshabi Senior Researcher BEC kmotshabi@bec.co.bw Moreetsi Thobega Senior Researcher BEC mthobega@bec.co.bw Frederic Borgatta Cluster Advisor UNESCO Institute for Statistics Susan Matroos Principal Statistician MoESD (CSO) sumatroos@gov.bw Boikhutso Monyaku Principal Education Officer MoESD (EMIS) bmonyaku@gov.bw

66  

DRAFT : Version 4

MeetingwithPermanentSecretary –24February2011

Name Surname Position Institution/Organization/Unit EmailAddressMmanane Mavis Kelebemang Secretary General Botswana National Commission for UNESCO mkelebemang@gov.bw Tshoganetso Madema Acting CEO Division of Planning, Statistics & Research tmadema@gov.bw Kgopotso Ramoroka Acting Deputy Permanent Secretary – Support Services Ministry of Education kramoroka@gov.bw Grace Muzila Permanent Secretary Ministry of Education gmuzila@gov.bwFrederic Borgatta Cluster Advisor UNESCO Institute for Statistics

67  

DRAFT : Version 4

12. APPENDIXC:ISCEDMapping

Nameofprogram ISCEDlevel InstitutionsEntranceage

Duration

Preschool (public) ISCED 0 – Pre-primary Preschools that include preschool program for children ages 4 to 5 4 2 years Preschool (private) ISCED 0 – Pre-primary Private preschools that include preschool programmes for children ages 0-3, 4 to 5

4 2 years Primary Education (public, Government dependent private and independent private) ISCED 1 – Primary MoESD schools, BOCODOL 6 7 years Secondary Education (public, Government dependent private, and independent private) ISCED 2A – Lower Secondary Brigades; BOCODL: JC equivalency, repeaters of Forms 1-3 (also private schools)

14 3 years Secondary Education (public, Government dependent private, and independent private) ISCED 3A – Upper Secondary Brigades; BOCODL for repeaters of Forms 4-5, BGSCE equivalency

17 2 years Secondary Technical/Vocational education (Public and some private) ISCED 3C – Upper Secondary BOTA, BTEP Technical colleges (TCs) NOTE: JC and/or BGSCE to enter 17 6 months to less than 2 years Pre-university foundation program (bridging) ISCED 4A LU 18 12 months Certificates, diplomas ISCED 4A, 4B Form 6 (“A” level) private high schools, BOCODOL (certificate after 12 months, diploma after 24 months), Ba Isago, Colleges of Education, Institutes of Health, Agriculture, TCs, LU, UB

18 3 months- 24 months (BOCODOL), 2-3 years