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The State of U.P. Admissions System
By : GM Franco ,Office of Admissions, UP
The Present Admission System (2006-2011)
Prior to UPCAT 2006, the various Constituent Units (CU) of the University of the
Philippines have been implementing a single admission system known as the ExcellenceEquity Admission System (EEAS) that used one equation, the University Predicted Grade
(UPG), as basis for campus admission. While the EEAS was only initially implemented in
1998, the UPG equation has been in use as the main selection criterion since 1978. The UPG
is computed using a regression equation that predicts a student’s academic performance in the
University on the basis of his/her High School Weighted Average (HSWA) and UPCAT
Scores. The regression equation was presented at the 1976 U.P. Faculty Conference, held in
U.P. Los Baños, and was first used in AY 1978-79 as the sole admission criterion into each
U.P. campus. The basis of the equation was 4,978 random samples of students who were
admitted through the 1973 UPCAT into the College of Arts and Sciences and the UP College
in Manila.
The unified system also employed course-specific predictors like the MPG (Mathematics
Predicted Grade), BSPG (Biological Science Predicted Grade), and PSPG (Physical Science
Predicted Grade) as basis for assigning campus qualifiers to the various degree programs. All
of these are regression equations intended to predict the performance in the University of the
UPCAT applicants.
Regression analysis was used to develop these equations to avoid arbitrariness in the
distribution of weights to the various UPCAT subtests and the HSWA. Prior to the UPG, the
distribution of the scores was based primarily on the intuition of the Dean of Admissions.
Moreover, to avoid wasting the valuable resources of the University, choosing which
applicants will enjoy these resources must perhaps be based on the probability of their doing
well in the University.
The EEAS is implemented in two rounds: Pass 1 and Pass 2. An applicant is processed based
on his/her EPG (Effective Predicted Grade), which is simply the UPG plus an equity factor
(“palugit”) of .05 for those coming from public general, vocational or barangay high schools
and those belonging to cultural minority groups. A factor of 0.1 (“pabigat”) may also be given
to the applicant depending on his/her secondary choice of campus. The “palugit” is intended to
help those coming from underprivileged backgrounds have a better chance of qualifying. The
“pabigat”, on the other hand, is intended to give priority to those coming from Visayas and
Mindanao to their regional campuses over those coming from Luzon who chose these
campuses as their secondary choice.
Pass 1 is open competition, meaning everybody gets to compete. At this stage of the selection
process, 70% of the campus slots are filled up. In Pass 2, the remaining 30% are filled up. But
Pass 2 is a restricted round, since only those coming from underrepresented provinces are
allowed to compete. UP dependents are also allowed to compete at this stage regardless of
where they came from. Technically speaking, Pass 2 is therefore a geographic round. If there
are any slots that remain due to migrations (from secondary campus choices to primary ones)
or the stipulated cut-off being reached before all the slots are filled, a third round or Pass 3 is
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implemented to fill-up the open slots. Pass 3 is unrestricted competition like Pass 1, and is
therefore a continuation of Pass 1.
An unfortunate choice of nomenclature has since led to misconceptions regarding the EEAS.
By referring to Pass 1 as the Excellence Round and Pass 2 as the Equity Round, people were
led to believe that the Excellence and Equity components of the selection system are
implemented separately via the two distinct passes. Therefore, Pass 1 should be a pure
excellence round, meaning, no equity factors should be incorporated into the UPG’s of
applicants at all. It did not help that the original statement of the procedure apparently
vacillated between the inclusion of the “palugit” in Pass 1 in one paragraph and the exclusion
of the same another. All socio-economic and geographic equity considerations must then be
considered only in Pass 2, being the Equity Round. This would be a very neat separation of the
Excellence and Equity components, leading to a clear identification of who is an Excellence
qualifier and who is an Equity qualifier. However, the EEAS is, after all, the ExcellenceEquity Admission System: the two components are procedurally intertwined and therefore
inseparable into the two separate rounds. It is not the Excellence and Equity Admission
System.
This alternative vision was implemented by UP Diliman in 2006. With the revision of the
EEAS procedure, known as REEAS, Pass 1 became a pure excellence round. Pass 2 was
subdivided into two passes: Pass 2a which is the socio-economic round, where the “palugit” is
applied, and Pass 2b which is the geographic round, where no “palugit” is applied and the
competition restricted. Pass 3, the fill-up round, where the “palugit” is again applied, becomes
in fact a continuation of the socio-economic round. But Pass 3 should really be a continuation
of Pass 1 since most of the migrations anyway are from that round.
The original EEAS does not recognize socio-economic equity in terms of a separate pass.
Rather, socio-economic equity is implemented by the addition of the “palugit” to the UPG and
the use of the resulting EPG throughout the selection process. Pass 2 is strictly geographic
equity. Perhaps, there is something absurd about withholding, giving, withholding and then
giving the “palugit” to the applicant throughout the selection. What one hand gives, the other
takes away. The “palugit” is an equity factor that implements our own version of affirmative
action. Maybe, the subsequent vacillation that we find in the REEAS is indicative of our
ambivalent attitude towards affirmative action.
The REEAS also introduced in 2006 a new equation, the UP Admission Index (UPAI), to
replace the UPG. Since then, the selection of qualifiers into UP Diliman and UPEP Pampanga
has been through the UPAI. However, the other campuses retained the UPG.
In 2007, a committee was formed by President Roman to review the predictive ability of the
UPG and to develop alternative models that will address the inadequacies of the UPG. The
committee came out with a new equation, the Predicted Grade Weighted Average (PGWA).
This equation was subsequently revised to become the Revised PGWA (RPGWA) and was
approved by the PAC in January 2008 as an additional selection criterion for the campuses that
still use the UPG. The RPGWA was supposed to replace the UPG, but was only approved for
the selection of additional qualifiers as part of its continuing study. These additional RPGWA
qualifiers must be primary (1st choice) qualifiers and their UPG must be within the stipulated
cut-off. Hence, the use of the RPGWA in the EEAS was limited and experimental. UPG
remained as the main equation for campus selection; all who qualified under the UPG were
admitted. Up until 2011, this was the admission system followed by the other campuses.
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In January 2011, after the release of the results for UPCAT 2011, the RPGWA was
proposed to replace the UPG as the main equation. However, the Chancellors raised their
concerns about an equation that included a campus coefficient. The RPGWA, it must be noted,
had a campus dummy variable and therefore varied by campus. In short, the RPGWA was not
approved as replacement for the UPG. The RPGWA equation was subsequently revised,
leading to the RRPGWA, an equation which does not contain campus coefficients. But no
action was taken regarding this new equation.
So given the non-approval of the RPGWA or the RRPGWA, the admission systems revert
back to the equations that have been approved not only at the PAC level but, perhaps more
importantly, also at the University Council level: UPAI for UP Diliman and UPG for the other
CU’s.
The UPG Equation
The UPG has had a long history of use, and is readily understandable since it follows the UP
grading system. It remains the basis for walk-ins, that is, applicants who did not initially
qualify but were accepted by the other campuses upon reconsideration. Almost 50% of the
incoming freshmen of the other CU’s like UP Los Baños are walk-ins. The Registrars decide
on their admission based on their UPG’s. Even the UPEP in Pampanga, which is under UP
Diliman, uses the UPG as basis for evaluating walk-ins. In UP Diliman, financial and athletic
scholarships are still decided using the UPG. After 6 years of use, the UPAI remains
something of a mystery even to those whose task it is to evaluate students in UP Diliman.
Perhaps, it just is not as easy to interpret since it does not follow the common grading systems.
The UPG is computed using the following equation:
UPG = 2.8101 - 0.047147*ZMA - 0.046402*ZRC - 0.1381*ZLP
- 0.15531*ZHSWA - 0.025178*ZSC*ZLP*ZHSWA.
The Z scores are the standardized scores, viz., score-mean/standard deviation. There are four
UPCAT subtests that go into the equation: Mathematics, Reading Comprehension, Language
Proficiency, and Science. The other component is the HSWA. The intercept and coefficients
were the values derived when the high school grades and UPCAT scores of the 1973 sample
were regressed to their subsequent UP grades. A statistical analysis of the coefficients gives
the basis for the approximate 60% UPCAT and 40% HSWA distribution.
A point made against the UPG is that it is antiquated. It was based on 1970’s data and there is
a feeling that it does not apply anymore at present. But it has to be pointed out that the
regressions made in relation to the UPAI seem to validate the 60:40 distribution found in the
outdated UPG equation. However, the more serious argument against the UPG is the
undesirable effect of its interaction term. The equation includes an interaction term as its last
term, where three variables interact, viz., science, language proficiency and HSWA. Since the
science score occurs only in this part of the equation, it can sometimes have an unexpected
effect. When both language proficiency and HSWA scores lead to a negative score, science
yields a negative effect. The negative score obtains when either ZHSWA > 0 and ZLP < 0, or
ZHSWA < 0 and ZLP > 0. In both cases, a higher science score will result to a lower UPG (higher
numerically) while a lower science score will result to a higher UPG (lower numerically).
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Since with the UPG, the lower the score the better, these applicants seem to be penalized with
high science scores, and rewarded with low science scores.
The science subtest was not included in the original UPCAT developed in 1967. It was
introduced only in 1970. Perhaps, with the 1973 batch, the effect of the science score was
negligible. That is why it did not have its own coefficient but instead interacted only with
other variables. In his subsequent reports, Dr. Manlapaz, the person who developed the UPG
equation, even proposed the elimination of the science subtest from the UPCAT. However,
regression analyses done in 1998 and 1999 have shown that science can now be assigned its
own weight. And in the absence of a viable alternative, procedures for mitigating the perverse
effect of the UPG’s interaction term have been adopted.
So to address this seeming flaw in the UPG equation, the PAC approved in 2006 a “win-win”
scheme whereby the UPG is computed as is and computed without the interaction term, and
whichever of the two is lower, is chosen as the actual value of the UPG. The undesirable effect
for many applicants is that the science score gets cancelled out.
The PGWA equation
The PGWA equation was developed in 2007 to address the structural problems of the UPG
equation. It was envisioned as a new version of the UPG without the problematic interaction
term. The initial models the committee developed still showed a negative effect for science.
And these models also have low R Square values, from 7-14%. By comparison, the coefficient
of determination of freshman GPA and SAT in the USA can be as high as 27%. (Bridgeman,
et al, 2000)
Eventually, the committee settled on a model with an R Square = 22.34%. The PGWA
equation included a number of variables that are not found in the UPG equation like, campus
and high school type variables.
PGWA = 2.09475-(hswa_z*0.10173)-(score*0.05142)-(score2*0.03108)
+(lb*0.22207)-(mla*0.06167)+(ilo*0.17901)+(bag*0.17302)
+(min*0.17876)+(hs1*0.16402) +(hs2*0.14692) +(hs3*0.12922)
+(hs4*0.05211)+(hs6*0.11534) +(hs1*hswa_z*0.01474)
-(hs2*hswa_z*0.01395)+(hs3*hswa_z*0.00648)
-(hs4*hswa_z*0.06613)-(hs6*hswa_z*0.02647)-(lp_z*lb*0.01514)
+(lp_z*mla*0.03576)-(lp_z*ilo*0.01133)-(lp_z*bag*0.02437)
-(lp_z*min*0.01050)-(sc_z*lb*0.02752)+(sc_z*mla*0.04790)
-(sc_z*ilo*0.01704)+(sc_z*bag*0.01923)-(sc_z*min*0.05115)
+(ma_z*lb*0.02147)+(ma_z*mla*0.01982)-(ma_z*ilo*0.07335)
+(ma_z*bag*0.00174)-(ma_z*min*0.03237)-(rc_z*lb*0.03991)
-(rc_z*mla*0.00522)-(rc_z*ilo*0.03686)+(rc_z*bag*0.01470)
-(rc_z*min*0.00229)
Where,
Score = (0.24101*lp_z)+(0.2647*sc_z) +(0.23615*ma_z)
+(0.25814*rc_z)
Score2 = Score*Score
lb, mla, ilo, bag, min = 1 or 0 depending on the campus choice of applicant
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hsn = 1 or 0 depending on the high school type: 1=public, 2=science, 3= SUC, 4=UP,
6=foreign (private hs type is the default)
This complicated equation is simplified once the campus and high school variables are
instantiated. The structure of the equation comes out thus:
PGWA = 2.09475 – (hswa_z*0.10173) – (score*0.05142) – (score2*0.03108)
± (campus coeff) + (hstype coeff) ± (hstype*hswa_z coeff)
± (campus*lp coeff) ± (campus*sc coeff) ± (campus*ma coeff)
± (campus*rc coeff)
The default high school type is private. So for a UP Manila private school applicant, the
equation would reduce to the following:
PGWA = 2.03308 -(hswa_z*0.10173) -(score*0.05142) -(score2 *0.03108)
+(lp_z*0.03576) +(sc_z*0.04790) +(ma_z*0.01982) -(rc_z*0.00522)
For a UP Los Baños private school applicant, the equation is the following:
PGWA = 2.31682 -(hswa_z *0.10173) -( score*0.05142) -(score2*0.03108)
-(lp_z*0.0151) -(sc_z*0.0275) +(ma_z*0.0215) -(rc_z*0.0399)
The overall default PGWA is for a UP Diliman private school applicant. For this applicant,
the equation is really simple:
PGWA = 2.09475-(hswa_z*0.10173)-(score*0.05142)-(score2*0.03108)
A number of issues were raised regarding the original equation, specifically on the squaring
of the score and the positive signs before some of the terms. Both of these can lead to perverse
effects. The score component of the PGWA is computed using standardized scores. Some
scores therefore have negative values. A negative score when squared becomes positive, so a
standard score of 3 will have the same score2 as a score of -3. In a normal distribution, 3 and 3 are extreme values, the best and the worst respectively. So for score2, the best and the worst
will have the top score2, while the average will have the worst score2. In terms of score2, the
worst performer gets higher marks than an average performer. And the weight of score2 is
around 16.9%, which is quite significant.
The positive or plus sign before some of the terms will also lead to perverse effects since the
higher the score, the higher the PGWA, which translates into a lower ranking. Normally, a
higher score should lead to a higher ranking. This can explain why the PGWA range is narrow.
Those with very low scores get pulled up while those with very high scores get pulled down,
either by their score2s or by the positive terms of the equation. So in the end, the PGWA’s tend
to congregate rather than disperse. Already, a ranking of U.P. Manila PGWA’s shows that
among the top 1000, more than half have negative scores in some of the subtests.
To address these issues, a simplified equation was introduced, the RPGWA.
RPGWA = 2.12860 -hswa_z*(0.10035) -score*(0.11705) -score2*(0.00542)
+lb*(0.16626) +mla*(0.06910) +ilo*(0.10225) +bag*(0.17271)
+min*(0.12091) +hs1*(0.16534) +hs2*(0.14717) +hs3*(0.13141)
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+hs4*(0.03879) +hs6*(0.10658) +hs1hswa*(0.01256)
-hs2hswa*(0.00749) +hs3hswa*(0.00770) –hs4hswa*(0.06683)
-hs6hswa*(0.01971)
The structure of the revised equation is simpler:
RPGWA = 2.12860 – hswa_z*(0.10035) – score*(0.11705) – score2*(0.00542)
+Campus coefficient +HS type coefficient ±HS type x hswa coefficient
For a UP Manila private school applicant, the equation is now shorter.
RPGWA = 2.1977 - hwa_z*(0.10035) - score*(0.11705) - score2*(0.00542)
The weight of score2 in this new equation is also smaller: 2.4% compared to 16.9% in the
original equation. Since the weight of score also went up, from 27.9% to 52.5%, the value of
score2 relative to score went down from 60.6% to a mere 4.6%. So the new model seems to
address the apparent perverse effect of squaring the score. The new equation also does away
with the campus-UPCAT subtest coefficients. However, the high school type-HSWA
coefficients have positive signs in public and SUC (State Universities and Colleges) applicants.
This translates into higher RPGWAs, that is, lower-ranked RPGWAs, for higher HSWAs in
these high school types.
For example, take two applicants A and B from the same public school. Suppose A’s HSWA
is 2 standard deviations higher than the mean and B’s HSWA is 2 standard deviations below
the mean. It could be that A’s HSWA is 93.26 while B’s is 78.85. A will then get a +0.02512
to his RPGWA while B will get a -0.02512 to his RPGWA. Since the resulting RPGWA for A
will then be lower in ranking, and B’s RPGWA higher, the net gain for B over A is 0.05024, a
value that is greater than the equity factor. The perverse effects do remain.
Campus coefficients are also included in the new equation. But are they really necessary?
Since these values are constant, they will not have any bearing on the final ranking of the
applicants with respect to each campus applied for. Since everyone is ranked only against
applicants to the same campus, and never ranked across campuses, the ranking will not be
affected at all by the campus coefficients. As long as the selection is by quota, and not by a
predefined cut-off, the chances of qualifying for each applicant will remain the same.
As noted earlier, the range of the RPGWA is quite narrow. For UPCAT 2011, UP Manila
RPGWA’s range is from 1.5994 to 2.8559. By comparison, the UPG’s range is from 1.501 to
3.808. The RPGWA scores are therefore less spread out. A minimum value of 2.8559 for the
RPGWA would mean that no applicant is predicted to fail by the equation, since a grade of 3.0
is still a passing one. Moreover, the narrow range puts in doubt the applicability of a 0.05
equity factor. Within such a narrow range, the value of the equity factor increases greatly.
The “palugit” is applied only to public school students and members of cultural minority
groups. A closer inspection of the RPGWA equation shows that public school students are
effectively given a +0.16534 in the form of the High School type coefficient. For private
school students, the High School type coefficient is null. This means that a public school
student with the same scores as a private school student is already left behind by +0.16534.
This difference is further increased by the HSWA coefficient which is positive for public
school students. Disregarding the HSWA coefficient whose effect varies, the addition of the
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0.05 “palugit” would be tantamount to having a High School type coefficient of +0.11534,
roughly equivalent to that of foreign students. Hence, it can be said that the 0.05 “palugit” is
not even enough to make the public school students at par with private school students. It only
enables them to be at par with foreign students. The “palugit” ceases to be an equity factor at
all. It would be much better to simply cancel the high school type coefficient for public school
students. Public school students would be much better off without the high school type
coefficient than with the “palugit”.
The real beneficiaries of the “palugit” then will be the members of cultural minorities studying
in private schools. Since the High School type coefficient for private school students is null,
private school students have an advantage over all other students. Thus, a private school
student will have a double advantage with the “palugit”.
A rescaling of the RPGWA is therefore necessary, so as to be able to apply the approved equity
factor too. The values used for RPGWA from 2008 to 2011 are rescaled values. The rescaling
is done by regressing UPG on RPGWA with the following formula:
UPG = Intercept + (Coefficient*RPGWA)
Still, the raw value of the RPGWA has a narrow range. While studies have shown that the
predictive error of the RPGWA is smaller than that of the UPG, one wonders, given the narrow
range of the RPGWA, whether it is precisely this narrower range that accounts for the smaller
predictive error. If one’s predictions are not too extreme and congregate around the mean, the
chances of being correct increases.
In general, the RPGWA is a regression equation similar to the UPG, except that it is based on
more recent data (1998-2003), and gives higher weight to the HSWA. The ratio is variable,
although the default is roughly 55% HSWA: 45% UPCAT. It also includes high school type
coefficients, which are analogous to the adjustment factors in the UPAI, and campus variables
that account for the differences among the campuses.
Due to the concerns raised at the January 2011 PAC meeting, the model was further revised to
eliminate campus dummy variables. The resulting equation is the RRPGWA.
RRPGWA = 2.26976 - hwa_z*(0.12513) - score*(0.20373) + score2*(0.00235)
+ hs1*(0.174444) + hs2*(0.15263) + hs3*(0.14576)+ hs4*(0.09116)
+ hs6*(0.13472) + hs1hswa*(0.0000912)– hs2hswa*(0.00772)
- hs3hswa*(0.00110) – hs4hswa*(0.05400)– hs6hswa*(0.03137)
The RRPGWA has a slightly lower R Square = 20.06% compared to the original model, which
has an R Square = 22.34%. The error rates are still very low at 13.74% 10.52% (from
13.48%).
The U.P. Admission Index
Recent studies have been made to regress the GWA’s of students from their UPCAT scores
and high school grades to come out with a more up-to-date UPG. The problem generally has
been the low R Square rating of the models developed. An admission index was introduced
instead by UP Diliman because the R Square ratings of the resulting models were not high
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enough. For the RPGWA, preliminary models were similarly problematic. Additional
variables, specifically campus and high school type variables had to be introduced to get
favourable R Square ratings. To get a good fit, it became necessary to introduce a very
complicated equation.
The UPAI is not a regression equation. The committee that proposed it recommended its
adoption while regression models that are better predictors than the UPAI are being developed.
Since 2006, the UPAI has been used as the sole criterion for selecting qualifiers to U.P.
Diliman and Pampanga.
The UPAI is computed using the following equation:
UPAI = 0.6*(AVE_T) + 0.4*(HSWA_AF)
Where,
AVE_T = Average score of 4 UPCAT subtests
HSWA_AF = Adjusted HSWA = HSWA_T*Adjustment Factor
The UPAI follows the 60:40 ratio of the UPG, meaning 60% UPCAT and 40% HSWA. More
importantly, the UPAI makes use of adjustment factors (AF’s), unique for every school, to
adjust the HSWA’s. The AF is thought to be an objective basis for adjusting the high school
grades of students. It is supposedly a measure of a school’s strictness or leniency in grading.
The AF is intended essentially to address the problem of grade inflation and deflation. Given
that some schools are lenient while others are strict, the same numerical grades given by
different schools may therefore have to be appreciated differently. Whether a school is lenient
or strict is now determined by the performance in the UPCAT of the school’s students. For a
school whose AF  1, an adjustment in the High School Weighted Average (HSWA) therefore
becomes necessary. An AF that is less than 1 indicates a lenient grading system while an AF
greater than 1 indicates a strict one.
Every school with more than 10 UPCAT examinees for the past five years has its own AF. For
schools with 10 or less, the cluster AF is used. Clustering is per high school type per region.
Adjustment Factors range, for 2011, from 0.79 (Cuyo) to 1.301 (Quezon City).
The adjustment to the grades is accomplished by standardizing the HSWA’s and then
multiplying the result by the appropriate AF. This adjusted HSWA is equivalent to 40% of the
applicant’s UPAI. The effect of high school adjustment factor is to actually give more weight
to high schools performing well on the UPCAT. In other words, it is like giving a ‘plus’ factor
to these schools – a premium, so to speak. These schools happen to be mostly private, NCR
high schools, as well as the better science high schools. It should not come as a surprise that
these are the schools that would do well on the UPCAT. After all, they have the most
resources and probably the best teachers.
To get an idea of the effect of the adjustments, we can compare the HSWA part of the UPAI
for three schools based on 2006 data, the first time the UPAI was used. Let us take 3
applicants with the same raw (transmuted) HSWA = 85, which upon standardization to 50 as
the mean HSWA score becomes equivalent to 47.
School HSWA_TAF HSWA_AFWeightUPAI (40%)
Siargao47
0.76836.1
0.4 14.44
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Bulacan47
Manila 47
1.0 47
1.30761.43
0.4
0.4
18.8
24.57
Note that upon the computation of 40% of the UPAI of the 3 students, the difference
between the Siargao student and the Manila student is already more than 10 points. Just to get
level, the Siargao student would need a 97 grade (an 85 grade in Manila is roughly equivalent
to a 97 grade in Siargao based on the given AF’s). But the maximum grade upon
transmutation is only 95. Therefore, nobody from Siargao can possibly be as good as a less
than average student from Manila. This is what is implicitly expressed by the adjustment
factors.
Since the Siargao school has the lowest AF for UPCAT 2006, it is considered as the most
lenient in grading; while the Manila school, which has the highest AF, is the strictest. The
Bulacan school is said to grade accurately. The point differentials given above are only for
grades slightly below the average. If the raw grades were as high as 92, which the top students
usually get, the UPAI difference between Siargao and Manila goes up to 14 points. If the
calculus were sound, the Manila student should score in the UPCAT by around that much over
the Siargao student. What if they get the same UPCAT scores? The Manila student will still
be 14 points ahead.
How significant are the differentials? Keep in mind that the “palugit” for REEAS is 1 point.
Now, if the resulting point differential can be as high as 14 points or even higher between two
schools, it is as if one applicant has been given 14 times more “palugit” than another. In many
cases, the “palugit” given would not even make up for the number of points effectively
subtracted because the grades are from schools deemed lenient. An average grade in Siargao is
separated from an average grade from Bulacan by at least 4 points. So it is like the Siargao
student is penalized and the “palugit”, if given, would only make up ¼ of what he lost.
So can we really describe an applicant who came from a school with an AF which is less than 1
and who qualified by virtue of the “palugit” as an equity qualifier? Most probably, such an
applicant would have qualified anyway even without the “palugit” if his grade were not
adjusted downwards or if points were not deducted from him.
Effectively, the equity factor or “palugit” of 1 point ceases to be an equity factor, for it is
very small compared to the adjustments given to schools deemed strict. In the case of the
Siargao student, it is not even enough to recover just 10% of what he lost compared to the
Manila student. Morally, how is it different from giving a child one candy after taking away
from him ten candies? After all, social justice cannot even take away what one is entitled to.
Indeed, the Siargao student is disadvantaged, but not because of his socio-economic or
geographic condition. Rather, it is UP itself which put him at a disadvantage by deducting
points that he might really have deserved if not for the bad performance of his predecessors.
(And who can blame those students for doing poorly in the test when they had to cross the sea
on a ferry or boat to take the UPCAT.)
In 2011, an applicant from San Fernando, Romblon (who also had to cross the sea on a ferry
or boat to take the UPCAT) failed to qualify because her UPAI was pulled down by her
school’s low adjustment factor which was 0.85. Her AVE_T was 63.3 and her raw HSWA was
92.93. Upon adjustment, her HSWA_AF became 58.84. In U.P. Diliman, the only campus
where she wanted to study, the UPAI cut-off was 62.13. Obviously, her UPCAT performance
warranted her qualification. It was her adjusted HSWA that eventually pulled down her UPAI.
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Had her HSWA not been adjusted at all, (or had San Fernando, Romblon seceded and not been
under Philippine jurisdiction and therefore deemed foreign) her UPAI would have been 65.67,
good enough for her to qualify under Pass 1. (We need not mention her UPG, she was born six
years too late for that.)
Obviously, the AF could result in unfair downward adjustment of grades for good students who
happen to come from a not-so-good school, or an unfair upward adjustment of grades for notso-good students who happen to come from a good school. But should a particular student’s
grade be dependent on previous UPCAT performance of other students from the same school?
This would be like applicants riding on the previous reputation of their school. It would be
biased against someone who happens to be really good, but did not have enough money to go
to a good school and so ended up in a school where previous batches did not do well. It would
also favor those who are not that good but have the resources – and maybe connections – to get
into and stay in a ‘good school’ – one with a generally good performance on the UPCAT.
The REEAS seems to contravene the spirit of the EEAS. Let us consider a hypothetical
example. Suppose two students, one from La Salle Greenhills and another from Libertad
National High School (LNHS) in Romblon, were to get the same scores in the UPCAT. If the
HSWA of the La Salle student is 85 while that of the LNHS student is 92, which of the two
will or must be admitted into Diliman? Based on the UPAI formula, it will be the La Salle
student. Even if the LNHS student were given the “palugit”, the La Salle student will still be
ahead by 1.386 points. Thus, even if the “palugit” were to be applied twice to the LNHS
student, it will be the La Salle student who will get into UP.
Is it then right to utilize an adjustment factor that is computed based on the performance of
other applicants from the same school? Should the rating of one applicant depend on the
performance of his/her predecessors? Is the AF even stable?
The AF appears to vary depending on which among the students in a batch take the test. One
study done with respect to a big private school shows that if only the best or above average
students were to take the test, the resulting adjustment factor can be 20% lower than if only the
below average students were to take the test. So, public schools do not get favourable
adjustments, not necessarily because they are lenient in grading, but because only the top
students take the test.
In one case, it was the UPCAT examinees with low grades rather than those with high
grades who have a positive effect on their school's AF. This means that schools where only the
top students take the test are effectively disadvantaged. In the five-year data, Ateneo students
were selected and their standardized HSWA averages computed for each year. Then, those
that fell below the average were excluded and the AF recomputed. If only the above average
students were to be included in the computation, Ateneo’s AF would fall from 1.307 to 1.215.
The difference is very significant. Consequently, it can be said that it is the below average
students that pull up the AF and not the bright students. In fact, if only below average students
were included, the AF shoots up to 1.416. It is not an accident that Ateneo had the highest AF
for the last few years. They have the highest number of examinees and almost all their
students take the UPCAT. Their HSWA's are therefore more widely distributed. The
inescapable conclusion is that the more below average students from a certain school take the
exam, the higher their AF. This effect will obtain as long as the other schools are represented
only by their best students. This finding is alarming, since accessibility of the UPCAT and
affluence become significant factors for admission if the hypothesis is indeed true.
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Moreover, there is data which suggests that the AF’s do not necessarily indicate grade
inflation or deflation.
Consider the following table:
IDNO
1
2
3
4
HSNAME
A
B
C
D
AVE_T
65.5
65.5
62.3
62.3
HSWA
88.378491
88.377416
91.112795
91.109714
AF
1.180
0.986
0.953
1.124
UPAI
65.87
61.50
61.77
66.14
The table above contains a summary of 4 actual cases for UPCAT 2007. The ID Numbers and
High School Names have been coded for security reasons.
Applicants #1 and #2 have identical Average T-scores and almost identical High School
Weighted Averages. It might be expected that since both applicants have almost identical
scores, their UPCAT results would not vary at all. But since they came from different high
schools with different adjustment factors, their admission indices differ. In fact, the difference
between their admission indices is 4.37 points, which is very significant since applicant #1
qualified for UP Diliman while applicant #2 did not. Even if applicant #2 were given a
“palugit” (1 point), it would not have been enough for him to qualify much less get the same
rating as applicant #1.
With applicants # 3 and #4, we find the same case. The same Average T-scores and High
School Weighted Averages lead to different results owing to different adjustment factors. The
difference is again 4.37 points. (There is no significance to the identical point differentials.)
While applicant #4 qualified to UP Diliman, applicant #3 did not. Moreover, even if applicant
#3 were given the “palugit” 4 times, he would not be able to draw level with applicant #4 who
has the same scores but has the (mis)fortune of studying in a school with a supposedly strict
grading system. The latter circumstance made all the difference.
One may therefore question the basis for classifying one school like D as strict or another
school like C as lenient. In actuality, such a classification is based on the UPCAT performance
of the examinees vis-à-vis their high school grades. But in the case of each pair of applicants,
it can be said that because of their identical UPCAT performance, they probably deserve the
grades that they got relative to each other. Taken in isolation, applicant #1’s grades are neither
inflated nor deflated compared to applicant #2’s grades. The same goes for applicants #3 and
#4. To adjust the grades in these cases seems to be counter-intuitive, if not downright unfair.
If the addition of a “palugit’ to applicants coming from “disadvantaged” schools is a
contentious issue, should not an upwards adjustment of grades equivalent to around 4 times the
“palugit”, just because the applicant’s schoolmates performed better than expected in the
UPCAT, be more so?
These four cases are not isolated. They hardly constitute an exception. Examples like this
would make one think twice whether the adjustment factor is really an indicator of grade
inflation or deflation. Another plausible interpretation would be that the adjustment factor is
an indicator instead of test preparation. That 93% of the top 100 high schools (based on AFs)
are private while 70% of the bottom 100 are public high schools, and that around two-thirds of
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the top 100 are in Metro Manila while half of the bottom 100 come from the poorest regions of
the country, seem to support that interpretation. If that view were true, then we might be
adjusting in favor of wealth and privilege. Indeed, given two applicants with the same UPCAT
scores and high school grades, UP Diliman will be taking in the one with the higher adjustment
factor, who is more likely to be a Metro Manila, private school student who therefore has more
resources to prepare for the UPCAT.
The school with the lowest adjustment factor at the moment is located in Cuyo, Palawan. Does
this mean that the teachers there are the most lenient when it comes to grading? I do not think
they subscribe to the view that educators should cultivate above all the self-esteem of students
by say, giving high, inflated grades. Nor do they believe that by having high grades, their
students will thereby gain more opportunities for advancement. I may be mistaken. But it is
easier to believe, that being in a remote island, the applicants there have less resources for test
preparation, and therefore, do not perform as well as their more privileged co-examinees. It
must be added that among the 75 UPCAT testing centers, Cuyo is the most difficult to reach.
As Dr. Robert J. Sternberg pointed out in his Manila Lecture, societies stratify themselves in
various ways: wealth, race, height or test scores, each of which is self-perpetuating. If we
make it easy for some schools to have qualifiers and difficult for others by ranking them using
adjustment factors, in a couple of decades, we will start believing that those coming from the
preferred schools are definitely better than those from the other schools. And this is only
because more opportunities, specifically those afforded by a UP education, were given to them.
It will be no different if admission were based on height, something that is much easier to
measure. (For the latter statement, you might think it was the actor Adam Sandler speaking
and not Dr. Sternberg.)
Normalizing the AF’s to 1 addresses merely the perception, not the reality. Further adjusting
grades downwards would only add unnecessarily to the inequities brought about by a basic
education system whose quality depends too much on one’s access to resources.
Normalization will only aggravate the situation of applicants from schools with low AF’s. The
real value of the “palugit” will go down for these applicants since the value subtracted from
their adjusted HSWA’s will also be greater. Just so it cannot be said that HSWA’s are inflated,
almost all the HSWA’s, except for a few hundreds, are deflated sometimes almost by half. So
as to avoid the perception that some children are favoured, we stop giving candies altogether
and instead get more candies from the rest.
School HSWA_TAF (norm)HSWA_AFWeightUPAI (40%)
Siargao47
0.59
27.73
0.4 11.09
Bulacan47
0.77
36.19
0.4 14.48
Manila 47
1.0
47
0.4 18.8
One suggestion to address these concerns regarding the adjustment factor is to compute an
applicants’ adjustment factor based on his/her own performance. This is a more reasonable or
fair computation. Indeed, the rating of an applicant should depend on his/her own performance
alone. An applicant will do well based solely on his own efforts. No one can blame his
predecessors for not getting a fair adjustment.
The individual AF uses the same formula as the high school AF where the AVE_T and
THSWA are the applicant’s AVE_T and THSWA respectively. With an individual AF, the
UPAI of the San Fernando, Romblon non-qualifier would be 64.46, good enough to qualify in
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Pass 1 (her own AF=0.956). Indeed, the use of the individual AF will bring in qualifiers with
higher AVE_Ts (better performance on the UPCAT).
The individual AF will also address the problems associated with clustering. Applicants from
the schools with very few applicants may end up having AFs applied to them that are in no
way reflective of their own school’s grading system. This is because they will be assigned a
cluster AF which is computed based on all the schools that belong to that cluster. New schools
are disadvantaged in the present system. A case in point is a new private high school in the
south that was established by a well-known educational institution. For their first batch of
UPCAT applicants, a cluster AF was used, which was low. For that batch, there were no UP
Diliman qualifiers. The following year, the school had earned their own AF, which turned out
to be relatively high. Perhaps, it is expected since the school is associated with a very good
(exclusive) educational institution. So for the second year, a good number of their students
qualified into UP Diliman. One cannot help but think that the pioneers were disadvantaged. In
effect, their role was to make it possible for the succeeding batches to succeed; they were only
sacrificial lambs needed to establish their school’s reputation.
However the AF may be computed, the UPAI might not be an appropriate course predictor.
An admission index like the UPAI simply averages the UPCAT scores. So when such a rating
is used in selecting qualifiers to specific degree programs, its effect could be counter-intuitive.
An applicant who does very well in mathematics but not so well in the remaining subtests
would not fare as well as an applicant who does not do well in mathematics but does very well
in the other subtests. With simple averaging of scores in different subtests, an applicant who is
not so good in mathematics has a better chance of getting into a mathematics program than
someone who is very good in mathematics.
In 2011, the competition for the B.S. Mathematics program was stiff. Because UPAI was
used in ranking the applicants to the program, someone who scored in the 77th percentile in
mathematics (formula score=18.25), actually made it at the expense of someone who scored in
the 99th percentile in mathematics (formula score=40.5)! Of course, the UPAI of the first
applicant is better since he did better in the other subtests. But it stands to reason, that
someone who got more than double the number of questions right in the mathematics subtest
should be preferred for the B.S. Mathematics program. With the UPAI as predictor, we may
be excluding the math geniuses from the mathematics-related programs. Needless to say, there
is no way for a Ramanujan from Cuyo or Siargao to qualify.
Towards a new UPG
Recent regressions have generally considered the GWA’s of students upon completion of
the course. These GWA’s generally include 4 to 5 years of grades. Studies however have
shown that the predictive value of admission tests like SAT and UPCAT with high school
grades extend only up to 2 years of college. It is admitted that beyond that, there are simply
too many factors outside the test scores and high school grades that determine graduation.
Recent studies have shown that even the percentage of part-time faculty in a university can
affect the rate of its graduation. So it is very likely that the low R Squares of the recent
equations are due to the fact that they are regressed to the 4- or 5-year GWA’s of the students.
The most recent attempt to regress was based on the 1-year and 2-years GWA’s. The
GWA’s of students from the different CU’s who entered in 2008 and 2009 were considered.
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At that point, the 2009 batch have earned only one-year worth of grades and the 2008 batch
earned two-year worth of grades. The resulting model has a better R Square rating = 20.7.
The new UPG (UPGn to avoid confusion) presented here is based on the GWAs and
UPCAT scores of freshmen students admitted in 2008 and 2009 as of 2010. Students are from
the various units: Baguio, Diliman, Los Baños, Manila, Visayas, and Mindanao. The total
number of observations is 14,035.
UPGn = 2.67937 – 0.23963*ZHSWA – 0.10187*ZLP – 0.06749*ZMA – 0.06066*ZRC –
0.04041*ZSC
A comparison with the old UPG can be made by noting the relative weights of the scores. The
new equation is pretty straightforward, and so there is no interaction term. With the general
improvement in the UPCAT itself as noted by those involved in the construction and review of
the test, the science subtest itself has become predictive of college performance.
SubtestUPG (%)UPGn (%)
LP
33.5 ±2 19.97
MA 11.43 13.23
RC 11.25 11.89
SC ±2
7.92
HSWA37.68 ±246.98
The range of the UPGn is broader. The old UPG never comes near the value of 4.0. Shouldn’t
a UPG model at least predict some failures?
YearMin Max
200713194063
200812054075
200913054123
201012644248
201113704267
Simulation was done using the new equation using UPCAT 2010 and 2011 data. The main
values given are for UPCAT 2010; values in () are for UPCAT 2011. Campus assignment was
based only on raw UPGn, without any equity factors like “palugit”, “pabigat”, or geographic
equity. Hence, only one round was implemented, which was strictly “pataasan”. The outcome
was a bit surprising.
The resulting public school–private school ratio of 51.7:47.6 (50.1:49.2) is more favourable to
public schools than the actual ratio of 51:48.5 (49.8:49.6). Although the differences are
minimal -- around 1% -- the result is rather surprising since the latter ratio already accounts for
equity factors intended to increase the chances of some public school students (i.e., those from
public general, vocational and barangay schools). The UPGn ratio seems to be more equitable.
In fact, the percentage of qualifiers from these disadvantaged schools using the UPGn equation
is 29.46 (28.42), while using the old equation with equity the percentage is 28.59 (27.91).
Moreover, even without a geographic equity round, the outcome appears to be more
democratic since the percentage of NCR qualifiers went down from the actual 28.3% (28.3) to
24.1% (24.6). This means that there were more qualifiers from outside Metro Manila using the
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new equation. In the case of U.P. Diliman, the drop was bigger, from 59.6% (60.3) to 45.3%
(45.9). Note that the 4th year high school enrolment percentage of NCR is only 13.24.
So despite the fact that the simulation did not include any equity or geographic factors, the
outcome was more equitable and democratic than the actual run which ostensibly incorporates
these elements. Considering that affirmative action is becoming unpopular and difficult to
defend particularly in student admissions, an admissions procedure that does not utilize it but
results in a more favourable outcome might be preferable. Perhaps it is time to rethink equity
as implemented in the present EEAS.
So how is it that a pure excellence procedure nevertheless results in a more equitable and
democratic outcome? The answer, as we came to realize, lies in the equation itself. The
equation gives greater weight to the HSWA. The average HSWA of public general high
schools (86.81,87.2) is higher than that of private schools (85.23,85.34). This does not
necessarily mean that public school grades are inflated. Generally, only the top students of
public schools take the UPCAT, which is not necessarily the case in private schools. The
standard deviation for public schools is lower (2.8,2.7) as opposed to the same for private
schools (3.95,3.89). So the grades of private school students taking the UPCAT are a lot more
diverse.
The Language Proficiency (LP) subtest has a lower weight in the new equation. Generally,
private school students have higher LP scores than public school students. So the former do
not have the same advantage as before. Meanwhile, the Science subtest, where public school
students usually perform better than private school students, has a higher weight in the new
equation.
The equation is more democratic because the HSWA, which appears to be the most
democratic measure (least variation among regions), has a greater weight. In the UPAI,
adjustment of HSWA seems to be undemocratic since the adjustment factors appear to
correlate inversely with distance.
The other group targeted by equity considerations, the members of cultural minority groups
(CMG), are not adversely affected by the new system as simulated. The qualifier percentage of
CMG’s based on the new equation is 266/1373 or 19.37% (17.84). Overall, it is 266/12354 =
2.15%. On the other hand, the qualifier percentage based on the actual run is 281/1373 or
20.47% (18.39), overall, 281/12550 = 2.23%.
These are the cut-offs for the new equation.
Campus 20102011
Baguio 24802510
Cebu
25442536
Pampanga24482407
Diliman 21232138
Iloilo
25492537
Los Baños23302341
Manila 21312152
Tacloban 25132528
Mindanao25712548
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The cut-offs are higher than the usual cut-offs for the old UPG since the mean for the new
equation is higher. The intercepts of the UPG equations may be considered as the respective
means since an applicant who gets the exact mean values for each subtest will only have the
intercepts left. Adopting the new equation will then entail adopting a new absolute cut-off. A
value around 2.68 might be a more reasonable absolute cut-off than 2.8 for the latter value
indicates barely passing grades in UP.
Course predictors
Admission into the various degree programs is usually done using course predictors. When
the UPG was first used in 1977, there were other predictors like EPG (English Predicted Grade)
and MPG (Mathematics Predicted Grade) that were introduced.
MPG = 4.4846 - 0.42403*0.00001*TSC*TMA*HSWA
EPG = 4.5306 - 0.4008*0.001*TLP*HSWA
PSPG = (CPG + PPG + MPG) / 3
BSPG = (BPG + CPG + EPG + PPG + ZPG + MPG) / 6
These predictors likewise need to be updated. So the grades of UP Diliman students in specific
subjects were regressed to develop course-specific equations. Among the various equations
generated, two with the best R Square ratings were chosen. It is safe to say that the two
equations, together with the new UPG, can be used to select qualifiers to the various degree
programs.
MPG = 3.21183 – 0.39658*ZMA – 0.16119*ZHSWA – 0.0601*ZSC
– 0.03301*ZLP – 0.02255*ZRC
R Square = 19.9
SPG = 3.05457 – 0.22893*ZHSWA – 0.16162*ZSC – 0.10143*ZMA
– 0.08972*ZLP – 0.08503*ZRC
R Square = 24.3
Eliminating affirmative action
Affirmative action is part of the mission of the University. So in spite of the arguments
against it, it would not be easy to eliminate affirmative action in the admissions process. What
can be done is perhaps to revisit its implementation. Simulations based on the new UPG
equation show that the present system may actually be less equitable and democratic than a
system that foregoes equity considerations. For applicants coming from disadvantaged
backgrounds, perhaps the more equitable option for them is not to adjust their grades at all.
The affirmative action in their case is not to take action, that is, not to take away points from
their grades.
Note on the alleged “flaw” of the old UPG
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A hypothesis has been suggested regarding the so-called “flaw” of the UPG equation. The
negative effect in the UPG equation occurs only when the LP_Z (standardized Language
Proficiency score) and HSWA_Z (standardized HSWA) scores have opposite signs. Now, it is
only expected that both are correlated since language proficiency enters into all the academic
subjects comprising the HSWA. So, it seems something is wrong when the two do not
correlate. Perhaps the HSWA is inflated or it is deflated. Or, if the HSWA is just right, then
the LP scores should not have been as high or as low as they actually are. Some adjustment is
therefore necessary. This can only be done in the last term of the equation. Based on this
hypothesis, the negative effect of the interaction term in the UPG equation serves only as an
adjustment for a perceived anomaly.
In fact, whatever adjustment occurs in the interaction term does not exceed what has been
gained or lost in the LP term or the HSWA term of the UPG. Based on 2007 data, among
those with opposite signs in LP and HSWA, the minimum score for the interaction term is 0.1668 while the maximum is 0.1239. The applicant with the minimum score had gained
0.2343 in the LP term which is now adjusted downwards. The applicant with the maximum
score on the other hand had lost -0.3150 in the LP term which is now adjusted upwards. In the
UPG equation, the interaction term is 8.6% of the combined weights of LP and HSWA.
It may appear counter-intuitive to us but it may be the equation’s way of adjusting for what it
perceives is an anomaly, the opposite signs for LP and HSWA. In such a case, the opposite
signs would give rise to suspicions about the accuracy of the HSWA. So in the end, what the
UPAI does with the HSWA using AF’s might be the same kind of adjustment that the
interaction term of the UPG equation does – except that the adjustment being done in the latter
is confined to limits set by the HSWA or LP terms, while the adjustment in the UPAI is
theoretically without limits (such that it becomes impossible for Cuyo’s genius to even be at
par with average Ateneo students in terms of the adjusted HSWA). In the UPG equation, it is
the formula itself that makes the adjustment, whereas in the UPAI, the adjustment is done
externally, using 5-year data that may even be incommensurable as it involves different cohorts.
Regression equations like the UPG have a way of surprising us. After all, the retrograde
motion of the planets, which was a source of wonder for ancient men for being counterintuitive, hides a deeper and wider symmetry.
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