A brief survey has been performed of foreign investigations on the modeling of the process of decision making about student choices of colleges and universities; a suitable simulation expert system has been developed at a conceptual levelyesBelgorod State Universit

Moskovkin, V. M.

DSpace at Belgorod State University

 ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2009, Vol. 43, No. 5, pp. 292–295. © Allerton Press, Inc., 2009.Original Russian Text © V.M. Moskovkin, 2009, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2, 2009, No. 10, pp. 19–21. 292 To the best of our knowledge, there are no Soviet-era scientific publications devoted to the choice ofhigher education institutions by secondary-schoolgraduates, because there has been no demand for suchresearch, as university and college education was freeand widely accessible and a tradition for such studieshas not yet developed over the post-Soviet period.The situation in the west appears to be absolutelydifferent. Our experiments with the search engine Goo-gle Scholar, which were organized as an advancedsearch using an exact phrase, showed that there is anestablished set of terms in this field (Table 1).It can be seen from Table 1 that over three monthsthe number of responses to the queries for the key termsof our research increased by about 5–10%. It is alsoclear that the number of responses to queries containingthe word “college” significantly exceeds the number ofqueries for terms containing the word “university.”As early as 18 years ago, Larry Litten wrote that theprocess of college decision making was hard and press-ing for many teenagers and their parents. Each of thesegroups spend an average of about 50 hours per year onthe collection and analysis of information, withoutallowance for personal visits to the campuses of thesehigher education institutions, which normally costabout $1500 [1].The most comprehensive review in this field waspublished by L. Henrickson in 2002 [2]. He pointed outthat over 40 years about 1900 publications weredevoted to “College choice” and “College access” andperformed a detailed analysis of the three categories ofmodels developed in this field of research:—process models,—theoretical models,—methodological models.The  process models  considered are three- to five-stage developmental models, in particular, a standardthree-stage model [3], in which at the first stage thewould-be student contemplates his intentions aboutcollege enrollment (from high school grade 8 tograde 12);the second stage involves acquiring and studyinginformation about the colleges the students are consid-ering; determination of the set of higher learning insti-tutions meeting his selection criteria; this can be basednot only on limited characteristics, such as location or Simulation Expert System for Making Students’ College Decisions V. M. Moskovkin Received July 9, 2009 Abstract —A brief survey has been performed of foreign investigations on the modeling of the process of deci-sion making about student choices of colleges and universities; a suitable simulation expert system has beendeveloped at a conceptual level. Key words : simulation expert system, Google Scholar search engine, simulation models of college choice, inte-grated indicator of education attraction. DOI:  10.3103/S0005105509050057 Table 1.  Queries for terms in the field of college choice made byGoogle Scholar search engine and responses to themTermsNumber of responses08.04.2009 08.07.2009College choice 7010 7250College access 3190 3460University choice 862 982University access 2390 2580University decision-mak-ing process75 85College decision-making processno measurements have been performed150University selection process 74 93College selection process no measurements have been performed656 AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS        Vol. 43        No. 5        2009 SIMULATION EXPERT SYSTEM 293 educational programs and syllabus, the student’s per-ception of the particular college, and the tuition fee;and the third stage includes the process of actualadmission (acceptance) or rejection of the student bythe college (admission process).The  theoretical models  considered include modelsof higher education, as well as sociological and econo-metric models.Further, L. Henrickson notes that over 60% of theworks devoted to students’ college decision making arebased on empirical data and their later processing bystatistical methods. He called this class of models methodological . Fifty percent of the studies of thisclass employ logarithmic probability functions com-bined with maximum probabilistic estimates and inaddition twelve per cent of the publications use a factoranalysis [2].L. Henrickson associates the development prospectsin this field of research with the use of nonlineardynamics methods and complexity theory (whichwould enable the consideration to take into accountfeedback and heterogeneity), as well as of sophisticatedcomputational algorithms. Some of the new computa-tional methods include advanced methods of data stor-age (e.g., GIS) or new methods for computer program-ming and modeling (e.g., computer modeling usinggenetic algorithms, neural networks, agent-based mod-els, or complex adaptive systems) [2]. Further, hedwells in detail on agent-based modeling and notes thatthis is a totally novel computing technique in social sci-ences (only two articles have been found devoted to thistopic) based on C ∗∗  object-oriented programming. Theagents in this model are a student and a college, each ofwhich acts relatively independently in making his/itsdecision, abiding by simple rules of interaction but theresulting macro enrollment patterns are very compli-cated.Numerous foreign empirical studies carried outwithin the frame of sociological model developmentare based on the typification of information sourcesconnected with the students’ college choice and thequantitative assessment of the college choice factors onthe basis of the weights of higher educational establish-ments generated by students [4, 5]. For example, Gal-otti in [4] uses a seven-point scale for assessing theimportance of information sources (from 1 to 7) and ascale from 0 to 10 for rating the significance of variousfactor weights in college choice made by students.This literary survey of publications sources in thefield of students’ choices of colleges and universitiesgives us better understanding of the main factors of col-lege choice when the students are able to assess the sig-nificance of these factors themselves. This is veryimportant when developing simulation models of col-lege choice. We have not, however, discovered anywork that synthesized the features of simulation model-ing with online facilities for the purpose of developingsimulation expert systems for seeking and choosingcolleges and universities for further education on a glo-bal scale. We have developed such a system on a con-ceptual level.* * *It will be assumed that high school graduates in theirchoice of the worldwide universities on a qualitativelevel usually take into account the following factors:college rating (prestige and status), tuition cost, cost ofliving (living wage and the cost of a market basket) inthe university/college location, living conditions(accommodation) and security conditions in the respec-tive community.On the basis of these five factors let us devise thesimplest formalized procedure to determine the attrac-tiveness of studying at the  i -th college/university. Tothis end, all these factors must be represented in theform of quantitative indicators. The first three indica-tors correspond to the measured (estimated) statisticaldata (hard data), the remaining two are based on ques-tionnaires (survey data). Let us consider these cases oneby one.1.  University ranking  is determined by means of anintegrated index of one of the international universityrankings (Chinese university ranking, Spanish webo-metric ranking, Times Higher Education ranking (GreatBritain) etc.). For the  i -th college/university this will bedesignated by I i . If there are no available data aboutintegrated indexes, rankings themselves can be used(university ranking positions). For example, for the “ ∈ –-500 of the Chinese university ranking, it will beassumed to be I 1  = l, I 500  = 500.2.  The tuition cost  at the  i -th college/university willbe designated as $C i .3.  Living wage  in the municipality where the  i -thcollege/university is located will be designated as$LW i . Here, it should be taken into consideration thatthere may be several colleges/universities in the samecity, therefore, the LW values for those colleges/univer-sities will be the same.4.  Living conditions  at the  i -th college/universityare determined on the basis of, say, a five-point score,BLC i . Thus, BLC i  = 5 corresponds to excellent livingconditions and BLC i  = 1 reflects very poor living con-ditions (seven- or ten-point scores can also be used).5.  Security conditions  in the municipality wherethe  i -th college/university is located will also be deter-mined by means of five-point score, BSC i : BSC i  =5 corresponds to a very high security level and BSC i  =1 reflects a very low security level.Now it is necessary to use a procedure for matchingactually measurable (estimated) data from the firstthree indicators to the scores of the two last indicators.Here, attention should be paid to the fact that the scoresof the last two indicators are devised as “stimulators.”The integrated index of a worldwide university rankingis also a “stimulator” (the higher the integrated index, 294 AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS        Vol. 43        No. 5        2009 MOSKOVKIN the higher the college or university’s attraction will befor students), it can be converted to the 5-point score bythe following formula:(1)If I i  stands for a ranking position being an “stimula-tor” then(2)For example, in a Chinese university ranking (I 1  =I min  = 1, I max  = 500) Harvard University, which ranksfirst, will get the best score (BI 1  = 5), and the universityat the bottom of the list will have the worst score (BI 500 = 1).The second and third indicators are “destimulators”and the following formulas will hold for their conver-sion to scores:(3)(4)The results of calculations by formulas (1)–(4) arenot rounded to integer estimation scores, otherwise sig-nificantly differing values of the first three indicatorswill yield the same score.Note that a formula of type (1) is used for the con-version of actual statistical data to seven-point scoreestimates in the methods for the calculation of the coun-BIi4 Ii Imin–( )Imax Imin–( )--------------------------- 1.+=BIi4 Ii Imax–( )Imin Imax–( )--------------------------- 1.+=BCi4 Ci Cmax–( )Cmin Cmax–( )------------------------------- 1,+=BLWi4 LWi LWmax–( )LWmin LWmax–( )------------------------------------------ 1.+= tries’ competitiveness index of the World EconomicForum.Now, on an additive basis, the integrated index ofattractiveness of the  i -th college/university to prospec-tive students allowing for the weights of specific indi-cators (University Attractiveness Index, UAI):(5) where  = 1.It can be shown that i.e., the integrated index of college/university attrac-tiveness for a student will vary over the same range asits specific indicators. Therefore, on the basis of a uni-form scale for this indicator, the following five-levelcollege/university classification system can be devel-oped, ranking their attractiveness (Table 2).For generalized estimates of worldwide college/uni-versity attractiveness, the weighing factors can beobtained by the methods of pairwise comparisons andexpert estimation but this is not at all necessary forassessments of individual institutions.* * *The high-school graduate can make his own choiceof acceptable weights. For example, a financially well-off high-school graduate will assign a high priority tothe college ranking and security conditions and all therest of the indicators will be less significant for him. Byvarying the weights and trying various systems of col-lege/university ranking, a high-school graduate canplay out different scenarios of his college choice, thusobtaining a simulation expert system for students' col-lege choice.The set of colleges/universities used in making thechoice must be determined by the potential student’spreferences by means of limits imposed on specificindicators. For example, out of the first two hundreduniversities in the TOP-500 list according to some sys-tem of ranking universities, the high-school graduatewishes to choose universities satisfying the followinglimitations: C i   ≤  = $4000 a year; LW i   ≤  $50/day; BLC i  =3 or 4; and BSC i  = 4 or 5. For this set of limitations onparticular indicators the maximum of the integratedindicator UAI i  must be sought, i.e., we come to a prob-lem of linear programming.UAIi γ 1*BIi γ 2*BCi γ 3*BLWi γ 4*BLCi+ + +=+ γ 5*BSCi,γ ii 1=5∑UAIi{ }'min γ ii 1=∑ 1,= =UAIi{ }max 5 γ ii 1=5∑ 5,= = Table 2.  University/college classification based on attractive-ness for studentsVariation range of the integrated indicator UAI i Attractiveness for students0–1 very low1–2 low2–3 medium3–4 high4–5 very high AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS        Vol. 43        No. 5        2009 SIMULATION EXPERT SYSTEM 295 REFERENCES 1. Litten, L.H.,  Ivy Bound: High Ability Students and Col-lege Choice, New York: College Board, 1991.2. Henrickson, L., Old Wine in a New Wineskin. CollegeChoice, College Access Using Agent-Based Modeling,Social Science Computer Review, 2002, vol. 20, no. 4,pp. 400−419.3. Litten, L.H., Different Strokes in the Applicant Pool:Some Refinements in a Model of Student CollegeChoice, Journal of Higher Education, 1982, vol. 55,no. 4, pp. 383−402.4. Galotti, K.M. and Mark, M.C., How do High School Stu-dents Structure an Important Life Decision? A Short-Term Longitudinal Study of the College Decision-Mak-ing Process, Research in Higher Education, 1994,vol. 35, no. 5, pp. 589−607.5. Veloutsou, C., Lewis, J.W., and Paton, R.A., UniversitySelection: Information Requirements and ImportanceInternational Journal of Educational Management,2004, vol. 18, no. 3, pp. 160−171.

Simulation expert system for making students’ college decisions

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Simulation expert system for making students’ college decisions

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