9th National Convention on Statistics (NCS) EDSA Shangri-La Hotel October 4-5, 2004
A Geographic Information System-Based Decision Support System for Mapping Philippine's Higher Education Institutions by Carlos M. Pascual, Phebe M. Pasion and Ciriaco T. Ragual
For additional information, please contact: Author’s name: Designation: Agency: Address: Telefax: Email:
Carlos M. Pascual/Phebe M. Pasion/Ciriaco T. Ragual Professor IV and Chief/Information System Researcher I/ Instructor Mariano Marcos State University Management Information Service, Room 201, MMSU Admin. Bldg., Batac 2906, Ilocos Norte (077) 7923191/3879
[email protected]/
[email protected]/ batacs@digitelone
A Geographic Information System-Based Decision Support System for Mapping Philippine's Higher Education Institutions by Carlos M. Pascual, Phebe M. Pasion and Ciriaco T. Ragual1 ABSTRACT A geographic information system-based decision support system called map analysis program (MAP) was developed to build wealth of geo-referenced data and information of higher education institution (HEIs) and programs for policy research development. Thematic maps as major outputs of MAP showed various indicators that are useful for higher education research, planning, and policy options for rationalization and resource management of HEIs. The use of geographic information systems and database management systems are emerging information technology approaches and served as data banking tools to share statistical data , information and knowledge among stakeholders on geographic areas and related policy issues. A case study is presented to demonstrate the use of MAP for policy research options among HEIs in northern Philippines. Introduction Basic and vital to the detailing and implementation of rationalization projects and activities among higher education institutions (HEIs) are data on the programs, campuses and facilities within each community of geographic areas. Such database could be gathered and compiled under the profiling and typologies of public HEIs projects. However, the data elements for HEIs were initially established by the Commission on Higher Education or CHED (CHED, 1996), and such important database need to be fully operationalized and lacks geo-referencing for spatial analysis. This initiative needs to be continued to support the collection, organization, and packaging of such statistical data and information and could be packaged for subsequent statistical analyses and used by interested users for planning and policy option researches of HEIs. The lack of well organized, packaged statistical data and information on various sectors is of concern by the public and private education, research and planning institutions of most developing countries. There is a need for the creation of comprehensive statistical database that will serve to generate profile, technical, and socioeconomic indicators to identify homogeneous micro regions, as well as needed in the design of plans and development policies among decision makers. Moreover, the spatial distribution of HEIs, programs and facilities cannot be fully appreciated without visual representation of said 1
Professor IV and Chief, Information System Researcher I, and Instructor, Management Information Service, Mariano Marcos State University
database. Maps would enable planners to relate such data and information to the geographic, physical characteristics, infrastructures, feeder population and education needs of the service area of the HEIs. Mapping is a useful tool for environmental scanning and planning. The outputs of the project are needed not only for the design and implementation of rationalization measures but also for the conceptualization and projectization of financial/resource management reforms in higher education, including the introduction of normative financing. To manage such voluminous database, systematic tools can be used and assembled as a decision support system (DSS). A DSS is a computer-based of integrating database and analytical modeling methods such as artificial intelligence, decision analysis, optimization, modeling, etc. to support decision making (Aldeman, 1991). Walker & Zhu (2000) listed four reasons that would justify the development of research-based DSS for rural planning and resource management, such as: 1) increase of available information; 2) increase of complex decision making; 3) professionalization of resource management systems; and 4) increase of requirements to demonstrate “due process”. However, such DSS had been applied mostly on agricultural land use planning options, crop suitability using GIS and natural resources management (Lansigan, Pascual, Francisco & Cruz, 2000; Pascual, 1994), and being considered just lately in higher education and policy researches (Pascual, Pasion, & Ragual, 2003). This study aimed to: 1) develop a GIS-based decision support system (DSS) to establish geo-referenced data and information of HEIs and programs that incorporates and builds upon current understanding of the spatial distribution and cartographic modelling; and 2) operationalize such DSS for policy research development on HEIs. A case study was conducted in Region I and Cordillera Administrative Region, northern Philippines to demonstrate the use of such DSS methodology. Methodology Materials. Structured questionnaire which contained major data elements profile of a HEI was used by enumerators during field surveys. All available public and private HEIs, as well as feeder high schools within each province or geographic area of coverage were surveyed by assigned enumerator per province. A global positioning system (GPS) receiver was used by the enumerators to locate the latitude and longitude coordinates taken at each HEI, which represent the geo-reference point data of a HEI as an input database collectively in the geographic information system software. Such geo-referenced data conform to the standard map projection (universal traverse mercator or UTM at Zone 51) for mapping and other subsequent analysis given a set of objectives and criteria (business rules) and issues based on available information and knowledge generated from the surveys.
Procedure. A set of information technology tools such as geographic information systems (GIS) and database management systems (DBMS) was assembled to gather, store, easy retrieval and analyze spatial geo-reference data/information and generate/disseminate information about HEIs. The methodological framework of the study is presented in Figure 1 showing the input database, process or analysis and the expected outcomes of the analysis. The Visual Basic Version 6 computer language was used as “front-end” to develop and shell the geo-referenced database for the various input and outcomes, as well as linkages on display at will various indicators defined by each thrusts as required by Office of Planning Policy Research and Information (OPPRI) of CHED. There were three thrusts considered, namely, the access and relevance, efficiency and quality thrusts. Special emphasis was paid to the step from geo-referenced point data to spatial analysis that is tailored to the type and quality of readily available attribute data gathered. Initial modules in GIS software such as spatial query (theme-on-theme selection), 50-km radius of influence of a HEI to generate a buffer zone for proximity analysis indicators to identify of homogeneous micro regions, overlaying tools of map themes based on a specific data elements or attributes were employed based on criteria and various indicators for each thrust required by the commission. Buffering is a fundamental spatial analysis operation in GIS. A buffer defines an area of inclusion or exclusion around a geographic feature, like HEI. Once the various data entry, GIS query modules, report generation and other pertinent documents were assembled and fined-tuned, a graphic user interface of Visual Basic and GIS software was applied to automate such DSS tool. Moreover, the DSS methodology developed was presented to heads and staff of CHED-OPPRI and other researchers for interactive process of DSS development. This makes the DSS dynamic, reusable, ease in encoding, and shareable to others. Results and Discussion Development of DSS Methodology The entity relationship consists of six entities namely the HEI Program (program/course offerings for each HEI), Region, Province, Municipal (the municipalities under the different provinces) and the FEEDER HS (the feeder high schools). For every municipality, there are HEIs that belong to a municipality. Its relationship with the municipal is there are several higher education institutions that belong to that municipality (many to one). The higher education institutions offer programs or courses. Different HEIs offers the same program or courses thus the relationship is many to many. Feeder high schools also belong to a municipal. There are several feeder schools that belong to the municipal thus the relationship is one to many. A municipal belongs to a province and there are several municipalities for every province. The relationship between the two is there are several municipalities for every province (many to one). Every province as well belongs to a region and there are several provinces for every region (many to one). With this input-output relationship, the data entry modules were constructed using Visual Basic 6 as “front-end” where MS Excel
files (initial data entry and files given by CHED-OPPRI) were extracted, for ease in data encoding. Such DSS methodology on its on-going development was presented to major stakeholders, such as the heads and staff of MIS division of CHED-OPPRI, other researchers and colleagues in the academe and other research institutions, as well as to planners in the government and private agencies. Their comments and suggestions were considered in the final development. Such productive interactive process development of such DSS to other stakeholders also brought interest for similar thinking and policy option research and development on their strategic planning and policy option activities. Application of Map Analysis Program for Policy Option The total number of HEIs as of May 8 2003 is 1,479, of which 1,305 are private while 174 are public; of the private HEIs, 980 (66.3%) are non-sectarian (PNS) while 325 (22%) are sectarian (PS). The public HEIs include 111 (7.5%) State Universities and Colleges (SUCs), 44 (3%) Local Universities/Colleges (LUCs), 12 Other Government Schools, one CHED Supervised Institutions (CSI), and five Special HEIs. The number of private HEIs has increased at an average annual rate of 3.14 percent for the last six years, 1995-2000. On the other hand, the number of public HEIs has decreased to 164 from 265 in 1998 as a result of the implementation of Phase I of the Integration Program wherein 31 CSIs were integrated to SUCs in 1999 (CHED, 2001). There appear to be more than enough HEIs to provide for the current higher education requirements of the country. All provinces have an HEI, including at least one state college per province except Batanes (located at the northern tip of the Philippines). The AY 2001-2002 enrollment translates to an average of 1,435 students per HEI compared to the standard 1500 minimum enrollment per state college (CHED, 2003). A task force on program rationalization was created by CHED in 1998 and recommended the phase out of programs with low Professional Regulation Commission (PRC) performance of graduates within the preceding five years. Phasing out/closing low quality programs and programs operating at less than break-even point is recommended. Low quality programs are those with zero-low percentage passing in professional licensure examinations and maritime programs not meeting STCW’95 requirements. Potential savings in the closure/phase out of SUCs programs with 5% and below passing could be used in upgrading other programs that are more relevant and have greater potential significance. But one important consideration for such phasing out/closing program strategy is the possible displacement of students enrolled in those affected programs. Hence, in order to ensure that student access would not be adversely affected by the closure/phase out of programs, the availability of alternative programs – within 50 km of the affected HEI and at varying tuition fee level can be explored and opted. The MAP DSS could show such alternative programs and HEIs where students affected by phase-out/closure could go to.
Inter-Regional and Provincial Proximity of HEIs. This sub-section demonstrates the use of MAP for Region I and CAR, located in the northern part of the country. Figure 2 shows a 50-km radius distance buffer zones with SUCs as geographic centers within boundary provinces in Region I and CAR. More overlapping in the western portion are prevalent in La Union, Benguet and Pangasinan provinces. However, no SUC coverages within 50-km radius is seen in the eastern part of Region I and CAR. At the eastern portion where no SUCs, state colleges were present in those areas with minimal overlapping of coverages (Figure 3). The foregoing results showed that the five SUCs and state colleges with their campuses are strategically located in Region I and CAR. However, more overlapping of coverages among HEIs (public and private) in the interregional and provincial scale levels. Such scenario eventually resulted to overlapping of programs, especially that more over-crowding on some HEIs having high semestral program and tuition fees, yet low passing rate in PRC is prevalent. Sorting from the given data set showed that in Region I, the most preferred courses are Marine Engineering, and Computer Science while the least choice was Electrical Engineering. Initial data show that the program fee and tuition fee for the most favorite program (BS Marine Transportation) is P41,669 and P183, respectively. However, the second choice program (BS Computer Science) have a higher program fee of P50,025 and P250 tuition fee. Such program courses are being offered mostly by private HEIs. The least favorite program (BS Electrical Engineering) had the cheapest fees of P6,152 on program and P25 on tuition, being offered at by a state university. In the CAR as shown in Figure 7 revealed that the favorite courses are Accountancy, and Electronics and Communications Engineering while the least favorite is Elementary Education course. The most preferred course is BS Accountancy with a tuition fee of P260; followed by BS Electronics and Communications Engineering with a tuition fee of P251 and the least choice is BS Elementary Education at P154 tuition fee. Agriculture, fishery, environmental and veterinary medicine courses ranked very low among the subject preferential among students among Region I and CAR. Thus, there is a need to continuously pursue rationalization effort that will prevent program duplication. An equitable rationalization plan entails the ranking of regions based on their gross value-added and economic potentials (CHED, 2001). Such ranking shall serve as a guiding principle in assigning specific fields of specialization to the different HEIs. For example, HEIs with expertise in agricultural, forestry, fishery and veterinary medicine will be assigned to the largest agricultural producing regions of the country (like Southern Tagalog and Region 1 with gross-value added of 33.69 (rank 1) and 11.99 (rank 2) billion pesos in 1999, respectively) (CHED, 2001); and those with expertise in engineering and technology will b encouraged to locate in regions producing the largest value-added in mining, manufacturing, construction and utilities (like in CAR rank 1 with 76.54% average growth rate). One future initiative for agriculture and fishery courses is the bill extending AFMA (R.A. No. 8435) known as the “Agriculture and Fishery Modernization Act of 1997” by amending Section 109 and Section112 thereof up to year 2015 to attract jobs on these courses.
The CHED had made also a cursory assessment of all the regions of HEIs that would initially show in all types of higher education programs. However, a closer examination reveals that certain regions (like Region I and CAR) have HEIs which may not necessarily present due to differences in sectoral output production and economic potentials (CHED, 2001). Further scaling down considering the province of Pangasinan in Figure 4 shows the spatial display of dismal PRC examinations on some programs among HEIs and alternative HEIs using 50-km radius of coverage. PRC results on Licensure for Examinations for Teachers or LET during the year 2000 show dismal results in two HEIs (Asburry College and Pangasinan State UniversityUrdaneta Campus) having zero passing rate (CHED-OPPRI database). An alternative HEI for Asburry College, is the Great Plebian College which is 15.2 km distance apart (Figure 8). For PSU-Urdaneta, the suggested alternative due to distance is PSU-Sta. Maria with 12 km distance apart. However, there are other alternative HEIs for the LET program that students can opted to go to as shown in Figure 4. Using the spatial display of Figure5 for Benguet using the low passing rate on Accountancy program, one can opted to do the same analysis with Pangasinan. For the initial data on 2000, Baguio Central College has a low passing rate on Accountancy. Using the 50-km radius buffer zone, the nearest alternative HEI is Saint Louis University which is about 1.2 km only. However, one must be cautious for such analysis. The foregoing analysis should be continued to have a temporal or trend analysis for about four to five consecutive years of PRC board examination results for a given program to further strengthen on decision on whether to phase out or close those programs among HEIs with zero passing rates in PRC examinations. Notwithstanding the DSS methodology development of MAP, it is of utmost importance to involve stakeholders and end users from the beginning in the system design and frequent interactions between policy researchers and other stakeholders during development. The DSS MAP developed served as a tool to share information and knowledge as well as policy issues and concerns to rationalize HEIs. There is a need to transfer the knowledge of the DSS methodology to regional CHED MIS and HEIs for full operationalization of CHED HEMIS incorporating MAP. Conclusion The user-friendly and dynamic DSS called MAP developed by the Mariano Marcos State University-Management Information Service (MMSUMIS) used indispensable information technology tools such as the GIS and DBMS. Such software can be coined as a DSS tool for statistical database banking for subsequent analyses of HEIs and their respective programs. Such DSS have provided users with the quantitative information as presented on various indicators on major performance thrusts on higher education, providing users to examine various scenarios for decision making on rationalizing HEIs within a given geographic area of interest. Such database must be accessible and available to users when and where
needed, and in a form that is understandable by the user. Thus, there’s a need for more strategic planning for rationalizing HEIs to identify the best location and help stem the uncontrolled growth in the number of HEIs and programs. Furthermore, there’s a need to eliminate some programs that are being duplicated (with zero to low passing rates on PRC examinations on various programs) and overlapping using 50 km radius buffer zone should be set as standard distance of measure of buffer zones between and among HEIs. Such decision should consider temporal and trend data in four to five years on various programs. Utmost is a need to institutionalize the use of CHED Higher Education Management Information Systems (CHED HEMIS) to include MAP to explicitly indicate point coordinates (latitude and longitude) and other spatial data elements in each HEI, among other physical and/or socioeconomic features in the area for geospatial analysis. HEMIS could focus on ready-toupdate statistical database for a national compilation or atlas showing important data elements integrated with spatial and non spatial context that students or parents, faculty, researchers or planners can access such operational data and information with good quality, accuracy and security. The MIS of CHED-OPPRI and HEIs should be strengthened in providing, at the best value, a responsive and effective system of data collection, analysis and dissemination, as the basis of a comprehensive management information system for HEIs. The DSS development of MAP provides an opportunity to establish links between researchers and planners/policymakers. It is important to present the DSS to users and other experts from early stage and continue to refine based on their new ideas for improvement. Likewise, a web-based GIS architecture should be pursued to improve the MAP interface software developed by MMSU MIS, for wide dissemination and sharing of data and information on the basis of information technology. Such web-based platform could be linked at the CHED HEMIS website as the central data bank and the MAP could also hyperlinked to other HEIs for data encoding and for specific analysis on their geographic areas of interest. This need an investment before synergies in methodology development comes to the forefront.
Figure 1. Conceptual framework of the mapping methodology.
INPUT
PROCESS
OUTPUT
Phase I CHED HEIMIS (Georeferenced HEIs/schools data elements, and secondary data using GPS, digitized base maps)
ArcView
GIS ANALYSIS
Spatial/geo-coded HEIs/Schools (Maps, Figures, Charts)
Policy Views, Criteria, Plans and Issues/Concerns
Phase II (optional) – Data and Information Integration
WEB-BASED GIS SERVICES (Clients/Users)
Figure 2. Proximity at 50 km radius distance among main campus of SUCs in Region I and
Figure 3. Proximity at 50 km radius distance among main campus of SUCs in Region I and
CAR.
CAR.
Figure 4. Spatial display showing dismal PRC performance among HEIs and alternative HEIs at 50-km radius of coverage in Pangasinan.
Figure 5. Spatial display showing dismal PRC performance among HEIs and alternative HEIs at 50-km radius of coverage in Benguet.
REFERENCES Aldeman, L. (1991). Experiments, quasi-experimentals and case studies. A review of empirical methods for evaluating decision support systems. IEEE Transaction on Systems, Man and Cybernetics 21(2). CHED (1996). Data elements manual No. 96-1. Commission on Higher Education. July 1996. CHED, 2001. Medium-term higher education development and investment plan. Commission on Higher Education. August 2001. CHED, 2003. Statistics: Distribution of HEIs by type. July 2003 from http://www.ched.gov.ph. ESRI. 1999. ArcView GIS Manual. Environmental Science Research Institute, USA. Lansigan, F.L., Pascual, C.M., Fransico, S.R. & Cruz, R.T. (2000). Exploring land use option for the Ilocos Norte Province, Philippines. In: Proceedings on System research for optimizing future land use in south and southeast asia, IRRI, Laguna, Philippines. Pascual, C.M. (1995). A GIS-based approach and water balance simulation modeling in the extrapolation of rice environment. Unpublished PhD Thesis, CLSU, Philippines. Pascual, C.M., Pasion, P.M. & Ragual, C.T. (2003). Map Analysis Program: A GIS-based graphic user interface. In: 2003 Philippine ESRI GIS User Conference. Holliday Inn, Galleria Manila, October 15, 2003. Walker, D.H. & Chu, X. (2000). Decision support for rural resource management. In: Workshop Proceedings “Deepening the basis of rural resource management”, February 2000, ISNAR, The Hague, The Netherlands.
