Recopilaci´ on de resultados y representaciones gr´ aficas

Business organizations usually begin their planning with a mission statement, corporate philosophy, or a dete rm i nation of which of their options they will pu rsue i n the long term. Gillenson and Goldberg (1 984) compare general business planning system s with information systems planning and note the similarities betwee n the two. They detail the i nformatio n systems strategic planning methodology developed by IBM Corporation which uses "top-down, bottom up" aspects of planning. Educational institutions can adapt some of these aspects of planning to encourage participation in organizational decision

making by department heads or school principals.

Naylor and Thomas (1 984) describe many of the compone nts of business planning and optimization models used in strategic planning. Some of the co ncerns are the same as those fo r educational i n stitutions, e.g . , which businesses should be supported for g rowth ; which should be managed for greater cash generation; should certain businesses be divested and/or should acquisitions be sought? If one reads "departments", "disciplines", or "subjects" for "businesses", the same considerations are true for educational institutions.

Differences between ki nds of o rg an izational goals arise when businesses determine the level of dividends to shareholders and the level of debt which they m ig ht be willing to assu m e to fi n ance g rowth and i ncrease p rofits. Educational outputs are more difficult to quantify and do not relate to profits. G lose ( 1 977) describes and defines relevant l iterary contri butions about planning in education and industry and does not differentiate between the two. Most authors , however, discuss the difficulty of quantifying the outputs of education as a major stumbling block to com mitment by some educational institutions to the planning process (Tierney, 1 981 ; Whetten, 1 981 ).

The syste m s approach can be u sed for either business or educational planning. This method categorizes variables as input, process, and output. The output of each model is input to the next. Changes can be made i n the output of a system by modifying the i nputs and/or the process. Changes in i n puts o r p rocess are determi ned by evaluating the o utputs according to predetermined criteria. The outputs m ust, therefore , be measu rable (Van Dusseldorp, et aI. , 1 971 ). Feedback from evaluation of outputs to changes in i nputs and process is the most i mportant part of the systems approach. Without feedback the system is out of control.

A systems model fo r a manufacturing business might have i nputs of raw materials, direct labor and factory overhead i nto the production process with finished goods as the output. The finished goods would be expected to meet company standards of quality and quantity. Depending on the evaluation of the p roducts, changes wou ld be m ade i n the i nputs o r i n the p roduction process within the factory. The finished goods would be i nput i nto another model , together with, perhaps, marketing , admi nistration, and capital. The output of the final business model is net income or profit.

Inputs and outputs in education are not so easily defined because they are not so easily quantified as in business. Input variables in the educational process have been defined by Burkhead (quoted in Tanner, 1971 ) as follows:

1 . Student Time

A. In the classroom B. At home

2. Personnel Time A. Administrative B. Teaching C. Clerical D. Maintenance E. Auxiliary

3. Materials and Supplies

4. Buildings and Equipment (p. 68)

Educatio nal organizations must find ways of quantifying the outputs in some way i n o rde r to evaluate the effectiveness of t h e system . The output of educational institutions is sometimes referred to as "education"; however, education is the process; and the output is educated people. Some of the examples offe red for measuri n g and quantifying the output of educational organizations are (1 ) the percentage of students g raduating from a university, (2) the number of students completing a high school mathematics course and receiving college credit, (3) analysis of student opi nion concerning elements of an educational program (Tanner, 1 971 , Chapter 5) . Measuring the output of e d u cational p rog rams is oft e n based o n test resu lts and achievement ( n u mbe rs 1 and 2 above) which are then compared with state or national norms. This strict testing approach, however, equates students with industrial products and may lead to a "cult of testing" (Tanner, 1 97 1 , p. 79). Tanner p ro poses that assessment of the valu e of an educatio nal program by the participants should be a part of t h e evaluation e l e m ent of the syste ms approach. He details the way in which this assessment can be quantified using the Bayesian program evaluation procedure (Tanner, 1 97 1 , Chapter 5). Another planning technique used in business which might have applications in educational organizations is operations research. Operations research began i n a military context i n Britain during World War I I and brought to business organizations numerous industrial applications invo lving mathematical and statistical forecasting which cou ld be used in the plann i ng process. In the 1 950s the new techniques for planning were being adopted by businesses in the U n ited States and were fou nd to be quite u seful, but there was a lag of twe nty years before i nte rest was shown by educational organizations. "A primary objective of operations research is still to improve decision making and plan n i ng through application of quantitative models" (Tanner, 1 97 1 , p. 7) , mixed with human relations and subjective judgm e nt. Operations research

tools are "potential vehicles for providing relevant information (feedback) at the appropriate time for decisions (Van Dusseldorp, et aI. , 1 971 , p. 4) and as such can facilitate educational as well as business decision making.

Othe r educational planning techniques, i ncluding the Bayesian statistical decision theory mentioned above , are listed by Tan ner ( 1 9 7 1 ) with their functions as follows:

• Bayesian statistical decisi o n t h e o ry ( eval u atio n of student

achievement; esti matio n of long range prog ram effectiveness, and short range assessment of program components) ;

• cost/effectiveness analysis (for allocation of resources; program planni ng , program evaluation; curriculum revision; explanation of resou rce al l ocat i o n to p o l icy m ake rs a n d i nt ra - p ro g ram comparisons);

• fo recasting evaluation of p rogra m ; p rediction of student trends, long range budget analysis; definition of long range aims and immediate objectives; prediction of policy variables, staff requirements, forecast facilities needed) ;

• simulation (provision of feedback for program design based on e x pected be havi o ra l c ha n g e s ; faci l itat i o n of p l a n n i ng by prese nti n g alte rnative routes for policy form u lati o n ; t rai ni ng personnel);

• c ri ti cal pat h m e t h o d a n d p ro g ra m evaluat i o n a n d review

tech nique (schedu led review of objectives, program analysis, budget development, and project monitor; early identification of t rouble spots ; facilitation of statement of objectives and task definition); and,

• l i near p rog ramming (allocation of resources, o pt i mizatio n of

budget expenditures; computerized school lunch menu planning) (Tanner, 1 971 , p. 1 0).

Critics of rational systems for planning i n educational organizations feel that management science and models might be suitable for business and industry but are inappropriate for educational organizatio ns. Clark (1 981 ) states that traditional planning systems rest on the view of o rg anizations as goal-based e ntities whereas educati onal o rganizati o n s are bette r characte rized as "organized anarchy" (pp.42-60).

Jordan and Webb (1 986) point out, however, that in contrast with the moral concerns about experimental research techniques using students, business

adm inistrative practices adapted to education from business are likely to meet with minimum resistance.

Busi n ess p ractices from p rivate a n d public s e rvice agencies conceivably could be transferred or adapted to education without i nterferi ng with the desired level of support for the i nstructional program (Jordan & Webb, 1 986, p. 1 9 1 ).

To facilitate the i ntroduction of planning techniques and business practices in educational institutions then, those operations which do not affect students may be the best places to start. More i mportant for the entire organization, however, are models which provide pertinent i nformation to decision makers in a timely manner to assist them with academic decision making.

Right decisions about agg regative and quantitative planning factors can provide the necessary conditions for academic excellence--the environment in which teaching and scholarship can flourish. But sufficiency requires, in addition, sensitive and i nformed judgments of a q u a l itative ki n d . S u c h j u d g m e nt s a re t h e e ss e nce of outstanding academic leadership (Hopkins & Massy, 1 981 , pp. 9-

1 0).

Models can be used in educational organizations to help "organize thinking and display it for systematic review" (Hopkins & Massy, 1 981 , p.1 6), develop a plan , or make a decision.

3.3.2 TERTIARY ED UCATION AND SCHOOL DISTRICTS COMPARED