This paper attempts to outline the benefits of using a data warehouse for supportingthe management decisions in a pre-academic learning institution. One of the aspects is themeasurement of the scholar performance of the pupils, correlated with the financial efforts made bythe school management. The performance is analyzed from a multi-dimensional perspective, whichallows the extraction of high quality information.data warehouse, education, analysis, performance, multidimensional

Alexandru Lucian Manole

Catalina Motofei

Marian Liviu Matac

Mihai Paunica

English

Research Papers in Economics

Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  176
MEASURING THE PERFORMANCE OF EDUCATIONAL ENTITIES WITH 
A DATA WAREHOUSE 
 
 
Mihai Păunică
1  
Marian Liviu Matac
2  
Alexandru Lucian Manole
3  
Cătălina Motofei
4  
 
 
ABSTRACT: This paper attempts to outline the benefits of using a data warehouse for supporting 
the  management  decisions  in  a  pre-academic  learning  institution.  One  of  the  aspects  is  the 
measurement of the scholar performance of the pupils, correlated with the financial efforts made by 
the school management. The performance is analyzed from a multi-dimensional perspective, which 
allows the extraction of high quality information.   
 
Key words: data warehouse, education, analysis, performance, multidimensional 
 
JEL codes: I21, I22, C82, C88 
 
 
Introduction  
The information system used to design, allocate and use the monetary resources for the pre-
academic public educational system involves actors such as the educational institutions, the local 
public administration, and the educational management structures at local and national level (the 
inspectorates and the Ministry of Education, Research, Youth and Sport). 
The measurement of scholar performance is a key preoccupation of the management and of 
the entire educational personnel also. The next step is the evaluation of the correlations between the 
results (educational performance) and the financial allocations. One of the main indicator classes 
that describe the financial efforts made for education is made of the standard cost and its associate 
indicators. The level of the standard cost is a reference measure for the orientation of any decision-
maker, and also a basis of comparison, on time, space, educational domain criteria. 
The software system described in the paper offers the possibility of analysis for the results of 
the educational process. The "output" of the system presents, on one hand, the results achieved by 
pupils, and, on the other hand, a set of derivate indicators, that aim to correlate the results and 
financial indicators.  
 
Literature review  
The  main  aspects  of  database  design  were  outlined  by  Schneider  (2008).  The  need  for 
quality and performance measurement in the educational world was emphasized by Stanciu, Florin, 
Rădulescu and Aleca (2009); they have studied the case of the higher education management. The 
aspects  concerning the  structure  and construction of data  warehouse  were described by Inmon 
(2005), Popa, Udrică, Manole Vasilciuc, Gârba (2006). The instruments used for analysis, such as 
the MDX  language, the Reporting Services were presented by Harinath and Quinn (2006) and 
Mundy, Kimball and Thornthwaite (2006). 
 
                                                 
1 Academy of Economic Studies, Bucharest, Pta. Romana no.6, mihai63p@yahoo.com  
2 Academy of Economic Studies, Bucharest, Pta. Romana no.6, livius77@yahoo.com  
3 Artifex University, Bucharest, Economu Cezarescu no. 47 Street, alexandru.manole@gmail.com  
4 Academy of Economic Studies, Bucharest, Pta. Romana no.6, cmotofei@yahoo.com   Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  177
Research methodology  
The research  started from the premise that the management of the  educational  entity is 
interested  by  the  results  of  the  educational  process  and  the  efficiency  of  allocated  funds.  The 
complexity of data necessary for analysis suggests that the best solution is a business intelligence 
system, organized around a data warehouse. 
The software system designed includes the following components: 
a) the data sources, made of relational databases and worksheets; 
b) the data staging area, a SQL Server relational database;    
c) the data warehouse, implemented in SQL Server; 
d) the reporting component. 
 
The model proposed for data sources has been presented in a previous article (Păunică, 
Ţurlea, Matac, Manole, Motofei 2010). The data sources designed are: 
- a relational database, that records the scholar achievements of the pupils (the ones recorded 
in the catalogue and the results at competitions), the evaluation of the teachers (as a measure of 
interaction between the main actors of the educational process); 
- a set of worksheets, that refer standardized forms used by the educational entity (i.e. the 
budgetary execution). 
The  data  staging  area  proposed  for  the  data  warehouse  is  represented  by  a  relational 
database, under SQL Server. The advantages of using a relational database are evident, the most 
important of them are: 
- SQL Server offers instruments that allow the import of data from the platforms on which 
the data sources exist; 
- stored procedures or action queries can be used to clean and aggregate data; 
- the existence of mechanisms for the configuration of primary and foreign keys, with strict 
compliance towards the restrictions for these fields. 
The  main  functions  of  the  data  staging  area  are  the  creation  and  update  of  the  time 
dimensional tables and the fact tables. These functions are based on make-table (SELECT ... INTO) 
and append (INSERT ... INTO) queries. 
The  data  are  imported  from  the  data  sources  in  the  database  (data  staging  area),  the 
transformation operations are performed and then the structure of the data warehouse is prepared. 
The single data source for the data warehouse has several advantages, amongst them we outline the 
uniqueness of records, as the tables that will migrate to the data warehouse will include primary and 
foreign key restrictions to assure the integrity of data.   
The structure of the software system is presented in figure no. 1a. Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  178
 
Fig. no. 1a – The MANFIN data warehouse - the core of the BI system 
 
The overall structure of the data warehouse is presented in figure no. 1b. Likewise, the 
structure  was  presented  and described in  a  previous work  by Păunică,   urlea, Radu,  Ciocoiu, 
Manole (2010). 
    
 
 
Fig. no. 1b – The MANFIN data warehouse - the conceptual model 
 
 
Data 
Warehouse 
 
MANFIN 
SSAS 
Data source 
(data staging 
area) 
Data sources 
Financial 
analysis 
reports  
Performance 
analysis reports Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  179
 
The data warehouse reflects the following components: 
 - a set of dimensions that describe the actors involved in the educational process, the 
curricula and the scholar competitions; 
 - a set of fact tables that include the measures desired for analysis (the evaluation of 
teachers  by  their  subjects,  the  results  at  competitions,  the  total  and  average  scores 
obtained by pupils); 
 - a subset of tables describing the financial indicators involved in the analysis. 
 The creation of the two time tables is justified by the two levels of aggregation for the 
measures: on one hand, we attempt to include the notes and competition-related results (that are 
characterized by a certain randomness in time), on the other hand, we aim to offer the possibility to 
compare aggregate data (semester-based aggregation). This set of functional objectives explains the 
set of fact tables, along with the different dimensions that describe the measures included in the 
model. 
Therefore, the model of the data warehouse complies with the constellation schema, a model 
template that, despite complexity, allows the share of dimensions between multiple fact tables and 
the implementation of all aggregation levels necessary for the users. 
The physical model of the data warehouse is described in figure no.2. 
 
 
 
Fig. no. 2 – The MANFIN data warehouse - the physical model 
   Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  180
The  data  loading mechanisms  are  represented  by  a  set  of  INSERT...INTO  queries,  that 
perform the following functions: 
- the update of the time dimension: involves the insertion of new members from all sources 
(scholar  evaluation  -  notes,  teacher  evaluation,  results  from  competitions).  The  other  time 
dimension is updated by using an INSERT ... INTO query based on the first dimension. In order to 
assure the integrity of the operation, all the queries were merged into a stored procedure. 
- the update of the fact tables. 
The other dimensions extract their data directly from the corresponding tables in the data 
staging database. 
The data processing mechanisms aim to transform the data into useful information for the 
decision makers. The analysis follows several directions. 
First, we analyzed the  notes achieved by pupils during the semester. By using the SQL 
Server Analysis Services, the cube presented in the following figure was created: 
 
 
 
Fig. no. 3 – The cubEVALUARE_NOTE - the physical model 
     
The structure of the cube allows for the analysis of the results achieved during regular class 
time, offering the option to analyze every dimension and aggregation level. 
The first report indicates the total score obtained, grouped by disciplines, a score calculated 
based on the notes achieved. The disciplines can be grouped according to their degree of importance 
(marked by the GradIMPD attribute in the dimension). This report offers high-level information 
about the level of knowledge achieved by children at that discipline. 
The MDX query for the report is presented in the figure no.4: Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  181
 
 
Fig. no. 4 – The MDX Query, taking into account the importance of the discipline 
 
  The structure of the report is presented in the following figure: 
 
 
 
Fig. no. 5 – The Report, taking into account the importance of the discipline Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  182
The system also offers further analysis, by comparing the results of the pupils in function of 
the level of scientific achievement of the teachers, analysis of the results for every pupil and making 
them  available  for the respective  teachers,  in order to  improve the  scholar achievement  of  the 
children. 
As previously stated, the analysis is not complete without taking into account the correlation 
between the results and the financial indicators. In order to perform this, we have defined a system 
of derivate indicators, that include both the financial efforts and educational results. 
The main indicator designed is the "educational efficiency of the standard cost", calculated 
as a report between the total standard cost and the conventional score (points) achieved by the 
pupils during the reference period (an average standard cost). The conventional score is determined 
as a sum of the average score from the catalogues. 
The calculation formula of the indicator is: 
 
Pcm
Ch
Ics s       (1) 
where: 
Ics -  index of educational efficiency of the standard cost 
Chs - Cost standard, total 
Pcm- Conventional score 
 
By taking into account the components of the standard cost, the indicator can be further 
analyzed according to the formula: 
 
Pcm
Ch
Pcm
Ch
Pcm
Ch
Pcm
Ch
Ics
e perf ms p         (2) 
 
where: 
  p Ch - personnel expenses 
ms Ch  - material related expenses (utilities) 
perf Ch - personnel training expenses 
e Ch - other material expenses 
  The analysis is performed by defining a new cube, with the structure presented in the following 
figure: 
 
Fig. no. 6 – The cubEVALUARE MEDII cube, the physical model Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  183
    The structure of the cube allows the analysis of the index, as a total and by factors of 
influence,  with  the  help  of  an  additive  analysis  model  based  on  formula  (2).    The  drilldown 
instrument helps in easing the presentation of information. The influence of any component of the 
standard cost can be observed and measured. By further detailing the structure of the standard cost, 
more factors of influence are obtained and the granularity level for the analysis increases.   
  The structure of the report (based on high granularity) is presented in the following figure: 
 
 
Fig. no. 7 – The "Index of educational efficiency of the standard cost" - analysis report 
 
Conclusions  
  The business intelligence systems are an important component in contemporary decision 
support  environment.  The  described  data  warehouse  solves  some  of  the  issues  related  to  the 
performance in pre-academic education. The structure of the data warehouse allows for multiple 
analysis  fronts,  such  as  the  educational  performance  (scores,  average  scores,  competitions, 
performance of the teachers), the efficiency of the costs (measured with the use of a system of 
indicators derived from the standard cost - an item widely used in educational environment).  
 
 
References 
1.  Harinath, S., Quinn, S. R., 2006. Professional SQL Server Analysis Services 2005 with 
MDX, Wiley Publishing, Inc., Indianapolis 
2.  Inmon, William H., 2005. Building  the Data Warehouse, Fourth Edition, Wiley & Sons 
3.  Mundy, J., Kimball, R., Thornthwaite, M., 2006. The Microsoft Data Warehouse Toolkit 
With SQL Server 2005 and the Microsoft Business Intelligence Toolset, Wiley Publishing 
Inc. Indianapolis 
4.  Păunică, M., Ţurlea, C., Matac, L.M., Motofei, C., Manole, A., 2010. Software model for 
the  financial  management  in  pre-academic  education-data  sources,  Romanian  Statistical 
Review, Issue 3/2010 - Supplement 
5.  Păunică,  M.,  Ţurlea,  C.,  Radu,  C.,  Ciocoiu,  R.,  Manole,  A.,  2010.  Model  of  a  data 
warehouse  for  the  educational  management,    Romanian  Statistical  Review,  Issue  6  - 
Supplement 
6.  Popa, Gh., Udrică M., Manole A., Vasilciuc, B., Gârba, M., 2006. Microsoft SQL Server, 
Economica Publishing House, Bucharest Annales Universitatis Apulensis Series Oeconomica, 12(1), 2010 
 
 
  184
7.  Schneider,  M., 2008.  A general model for the  design of data  warehouses,  International 
Journal of Production Economics, Elsevier, Vol. 112 (2008), Issue 1 (March) 
8.  Stanciu, A., Florin, M., Rădulescu, C., Aleca,. O, 2009. Solutions for decision support in 
university management, Economia Journal, Management Series, Faculty of Management, 
Academy  of  Economic  Studies,  Bucharest,  Romania,  Volume  (Year): 12  (2009) 
Issue (Month): 1 (June) 
9.  Fact  constellation  schema  architecture,  available  at  http://etl-
tools.info/en/bi/datawarehouse_constellation-schema.htm, consulted October 1st, 2010 
 
 

A general model for the design of data warehouses,

Building the Data Warehouse, Fourth Edition,

Fact constellation schema architecture, available at http://etltools.info/en/bi/datawarehouse_constellation-schema.htm, consulted

Model of a data warehouse for the educational management,

Software model for the financial management in pre-academic education-data sources, Romanian Statistical Review, Issue 3/2010 - Supplement

Solutions for decision support in university management,

with MDX,

MEASURING THE PERFORMANCE OF EDUCATIONAL ENTITIES WITH A DATA WAREHOUSE

http://oeconomica.uab.ro/upload/lucrari/1220101/16.pdf

MEASURING THE PERFORMANCE OF EDUCATIONAL ENTITIES WITH A DATA WAREHOUSE

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics