Modelling the Effect of Academic Performance on National

Achievement Test (NAT)

Nathalie Joy G. Casildo

College of Information Sciences and Computing, Central Mindanao University, Musuan, Maramag, Bukidnon, Philippines

Keywords: Academic Performance, National Achievement Test, Educational Data Mining.

Abstract: The title of this study is Predicting the Effect of Academic Performance on the National Achievement Test

Using Data Mining. Students from Central Mindanao University's Senior High School (SHS) provided the

data for this study. The goal of this study is to develop a model that can predict how academic performance

affects the National Achievement Test. Its specific objectives are to extract predictive features of subjects that

affect the National Achievement Test (NAT), determine the effects of various academic subjects on the

National Achievement Test, identify the subjects that have a strong effect on the National Achievement Test,

and finally determine whether academic performance per quarter affects the National Achievement Test.

1 INTRODUCTION

One of the factors used to evaluate a student's ability

is academic achievement. The evaluation of periodic

grades, which is the criteria for measuring academic

performance, serves as the foundation for grading all

scholastic activities. The National Achievement Test

(NAT) is given to Filipino students every year by the

Department of Education (DepEd) to guarantee that

the aim of achieving quality education is realized

(Ebio, Jr., 2016). The test, according to the same

source, is designed to evaluate pupils' ability in a

number of topics, including Mathematics, English,

Science, Filipino, and others.

According to Hand (2012), data mining is a

branch of computer science in which you are bound

to identify some structures in non-random samples of

the population if you have large enough datasets. The

art of detecting patterns in massive amounts of data is

known as data mining. The most important element

of data mining is that; it can be used to produce new

ideas, hypotheses, and predictions from datasets in a

more scientific way. Data mining, also known as

Knowledge Discovery in Databases (KDD), is the

field of uncovering fresh and possibly usable

information from enormous amounts of data,

according to Baker(2007). The major goal of

educational institutions is to offer students with a

https://orcid.org/0000-0001-6935-5262

high-quality educat

ion in order to improve their

academic performance (Dabhade et al., 2021.Finding

patterns in datasets of national achievement tests and

student academic performance may be the ideal

application of Data Mining. Educational Data Mining

(EDM) is a field that uses statistical, machine

learning, and data-mining (DM) algorithms to

analyze various forms of educational data (Romero &

Ventura, 2010).

EDM is also defined as the

application of data mining techniques to data sets

produced from educational settings that could lead to

the discovery of new information (Amazona &

Hernandez, 2019)

Predicting a student's performance is crucial for

assisting at-risk students and ensuring their retention,

as well as offering good learning materials and

experience and boosting the university's ranking and

reputation (Mahmoud, 2019). The purpose of this

study is to construct a predictive model to assess the

effect of academic performance on the National

Achievement Test. The hypothesis in this study is the

effect of having greater or lower academic

performance on the NAT scores. Following the

development of the model, it will be used to extract

predictive features of subjects that affect the National

Achievement Test, determine the effects of various

academic subjects on the NAT, identify the subjects

that have a strong effect on the National Achievement

Casildo, N.

Modelling the Effect of Academic Performance on National Achievement Test (NAT).

DOI: 10.5220/0011106300003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 517-522

ISBN: 978-989-758-562-3; ISSN: 2184-5026

517

Test, and finally determine if academic performance

per quarter affects the National Achievement Test.

With 50 publications (42.4 percent), classification

is the most widely employed technique, followed by

regression with 33 papers (28 percent). Clustering

(11%) and association (1.7%) were employed far less

frequently, as were other approaches that were not

specifically mentioned (16.9% highlighted Machine

Learning/ Data Mining broadly) (López-Zambrano et

al., 2021). (Regio, et al., 2021). Alyahyan and

Düştegör's study, Predicting academic performance

in higher education: literature review and best

practices, revealed an accuracy of 89.15 percent when

applying the Logistic regression model.

2 RELATED WORKS

Abu Saa (2016,) conducted research on educational

data mining and student performance prediction. This

study used a variety of data mining tasks to create

high-performing qualitative prediction models that

can reliably predict students' grades based on a

training dataset. A survey was sent out to students to

collect personal, social, and academic information; it

was then preprocessed for data mining operations; the

data mining work was started; and finally, a pattern

was detected. It was discovered in this study that a

student's accomplishment is not solely based on their

academic efforts; there are various other factors that

are equally or more important. To summarize, this

study can motivate and aid universities in conducting

regular data mining jobs on their students' data in

order to reveal interesting results and patterns that can

benefit both the institution and the students in a

variety of ways.

Shahiri et al., (2015) published a review on

applying data mining approaches to predict student

performance. The most essential features were

discovered using a prediction algorithm, and

educational data mining methodologies revealed that

student achievement and success might be improved

more effectively and efficiently. (Sorour et al., 2015)

An experiment reported a model of student academic

performance predictor by evaluating student

comments data as predictor factors. The current study

is essentially about developing a predictive model. As

a result, the research is limited to text mining

approaches rather than data mining techniques.

The studies of Hamsa et al., (2016) and this one

are similar in terms of predicting student academic

performance, however they differ in terms of

algorithm because Decision Tree and Fuzzy Genetic

Algorithm were utilized. It seeks to construct a model

for predicting student academic performance for

Bachelor and Master Degree students in Computer

Science and Electronics and Communication utilizing

two categorization methods: Decision Tree and Fuzzy

Genetic Algorithm.

Regio et al., (2021) provide a prediction model for

participant performance, allowing them to discover

the elements that best explain exam results. They used

accessible data from the Ministry of Education as

well as the Logistic Regression approach for this

project. They can infer the student's performance with

a 74 percent accuracy using the predictive model.

Furthermore, because they employed a statistical

model that is simple to read and implement rather

than a complicated Machine Learning technique,

school administrators could use the results without

having a comprehensive understanding of the mining

technique.

3 METHODOLOGY

The Periodic Grades dataset and the National

Achievement Test dataset are the two data sources

used in this investigation. All of the information was

obtained in hard copy from Central Mindanao

University's College of Education.

This study implements the KDD (Knowledge

Discovery in Database) methodology. Figure 1 shows

the specific KDD (Knowledge Discovery in

Database) methodology for this study using the

Logistic Regression Algorithm and Feature Selection

Technique.

Figure 1: Logistic Regression with Feature Selection and

Student-level Cross-Validation.

Periodic grades data was obtained from a total of 723

Senior High School students of Central Mindanao

University when they were in grade 11 (the school

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year 2017-2018) and grade 12 (the school year 2018-

2019) related to beginning the National Achievement

Test.

The first step in preparing for the National

Achievement Test dataset was to convert the hard

copy to digital. The Mean Percentage Score was then

categorized, and the NAT score, according to Albano

(2019), follows the criteria listed in Table 1.

Table 1: National Achievement Test criteria.

NAT

Scores

Criteria

0 - 35 Very Low Mastery

36 – 65 Low Mastery

66 – 85 Average Mastery

86 – 100 Moving Towards Mastery

The criteria are based on the National

Achievement Test's Mean Percentage Score. Table 1

shows that the range 0–35 represents Very Low

Mastery, 36–65 represents Low Mastery, 66–85

represents Average Mastery, and 86–100 represents

Moving Towards Mastery. VLM is for Very Low

Mastery, LM stands for Low Mastery, AM stands for

Average Mastery, and MTM stands for Moving

Toward Mastery.

The National Achievement Test and Periodic

Grades datasets were pooled and then divided into

strands. There are four strands in total: Humss, Stem,

ABM, and TVL. To eliminate repetition in the

attributes and data while integrating the datasets, the

four files were joined using a union operator, as

illustrated in Figure 2. The union operator joins

multiple example sets together to form a superset. If

both examplesets have a common attribute, the union

operator creates a single attribute that holds the data

for both sets.

4 RESULTS AND DISCUSSION

The main purpose of this study is to develop a

predictive model for the National Achievement Test.

In order to estimate the impact of academic success

on NAT, a prediction model must be developed. The

logistic regression approach was used to generate the

model.

Table 2 shows the values of the model which

corresponds coefficients, standard coefficients,

standard error values, Wald values, P-Value which

we can derive predictive value. This tables shows the

variables or extracted topics represents as Var, grade

level (Grade), Quarter(Quar), and predictive features

Coefficient(Coeff.) , Standard Coefficient (Std.

Coeff.), Standard Error (Std Err.), Wald, and

subsequently the p-value).

Table 2. The Values of the Model.

Var. Grade Quar.Coeff.

Std.

Coeff.

Std

Err. Wald

value

Phy.Scie 11 4 -17.11 -9.11 7.03 -2.43 0.01

PerDev 11 2 -4.1 -15.61 1.2 -3.42 0

UCSP 11 1 -4.89 -8.07 1.91 -2.56 0.01

CLPW 12 1 -1.93 -6.71 0.9 -2.14 0.03

Philosophy 12 2 -1.36 -13.67 0.51 -2.63 0.01

EAPP 11 2 -30.97 -10.6 11.79 -2.63 0.01

Filipino 11 3 -0.65 -4.32 0.75 -0.87 0.38

MIL 12 1 -1.04 -6.06 0.58 -1.78 0.08

ELS 11 2 2.75 3.76 3.3 0.84 0.4

Filipino 11 2 0.46 3.01 0.39 1.18 0.24

EAPP 11 1 18.32 7.88 9.45 1.94 0.05

Filipino 11 4 0.15 1.24 0.69 0.22 0.82

Arts 12 2 2.7 12.95 0.57 4.73 0

Extract Predictive Features Affecting NAT. The

subjects listed in Table 3, were among the 14

variables extracted by the feature selection technique

used in this study; among them, there are repeated

subjects that vary in quarter, so the technique selected

a total of 14 variables. The assumptions of the study

require these features.

Effects of Various Academic Subject in National

Achievement Test. When the value of the

independent variable rises, so does the mean of the

dependent variable. According to the data, when the

grade of the following subjects improves, the mean of

the National Achievement Test rises. The feature

selection algorithm clearly chose two identical

subjects with positive coefficients, as shown in Table

3. Students' performance in Filipino at the same grade

level and from various quarters had an unexpected

impact on the National Achievement Test. It can be

argued that as a student's Filipino grade rises, so does

the mean of the National Achievement Test. The

larger the positive correlation coefficient is in terms

of its value and how it influences factors, the more

likely children will score higher on the National

Achievement Test. In Quarter 2, Filipino had a value

Modelling the Effect of Academic Performance on National Achievement Test (NAT)

519

of 0.46, and in Quarter 4, it had a value of.15. Quarter

2 was found to have a higher influence than Quarter

4, which only had a.15 effect.

Table 3: Positive Coefficient.

Subjects Grade Quarter Coeff.

Philosophy 12 1 1.13

Filipino 11 2 0.46

Filipino 11 4 0.15

English for Academic and

Professional Purposes

11 1 18.32

Earth and Life Science 11 2 2.75

Arts 12 2 2.70

The negative coefficient in Table 4 indicates that

as the grades in the following topics rise, the mean of

the National Achievement Test Scores falls. If the

estimated coefficient is close to zero, the predictor's

effect is small. When the value of a student's

performance grade grows, the mean of the National

Achievement Test Scores tends to fall, as seen in the

table below. Among the disciplines provided are

Physical Sciences, Personal Development,

Understanding Culture, Society, and Politics, 21st

Century Literature in the Philippines and the World,

Philosophy, English for Academic Purposes, Filipino,

and Media and Information Literacy.

Table 4: Negative Coefficient.

Subjects Grade Quarter Coeff.

Physical Science 11 4 -17.11

Personal Development 11 2 -4.10

Understanding Culture

Society and Politics

11 1 -4.89

21st Century Literature in the

Philippines and the World

12 1 -1.93

Philosophy 12 2 -1.36

English for Academic and

Professional Purposes

11 2 -30.97

Filipino 11 3 -0.65

Media and Information

Literacy

12 1 -1.04

Strong Effect of Student Grades to the National

Achievement Test. The standardized beta coefficient

measures the strength of each independent and

dependent variable's effect; the larger the beta

coefficient's absolute value, the stronger the effect.

Physical Sciences in Grade 11 from Quarters 1 and

English for Academic and Professional Purposes in

Grade 11 from Quarters 1 had the greatest beta

coefficients, indicating a stronger linear association.

The table below shows that Filipino subjects in

quarters 3, 2, and 4 have the closest beta coefficient

to zero, and Filipinos in the third quarter had the

highest absolute beta coefficient value. This could

indicate that in the National Achievement Test, this

subject has a greater impact.

Academic Performance Per Quarter May Affect

the National Achievement Test. Table 5 shows the

results. The feature extraction algorithm chose

Filipino, English for Academic and Professional

Purposes, and Philosophy as subjects. Each quarter,

we can only compare three subjects; the same

subjects are from the same grade level and differ each

quarter. Based on the coefficient value, the algorithm

chose Filipino in grade 11 from quarters 3,2, and 4,

English for Academic and Professional Purposes in

grade 11 from quarters 2 and 1, and Philosophy in

grade 12 from quarters 2 and 1.

Table 5: The same subjects extracted.

Variables Grade Quarter Coeff.

Filipino 11 3 -0.65

Filipino 11 2 0.46

Filipino 11 4 0.15

English for

Academic and

Professional

Purposes

11 2 -30.97

English for

Academic and

Professional

Purposes

11 1 18.32

Philosophy 12 2 -1.36

Philosophy 12 1 1.13

In Quarter 3, the value of the Filipino indicates

that as the value of the Filipino grows, the mean of

the National Achievement Test tends to fall. On the

other hand, Filipino in quarters 2 and 3 may indicate

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that as the grade Filipino in these quarters rises, the

mean of the National Achievement Test will climb.

When the grade in English for Academic and

Professional Purposes in Quarter 2 of grade 11

improves, the mean of the National Achievement Test

decreases. As the grade of English for Academic and

Professional Purposes in Quarter 1 of grade 11

improves, the mean of the National Achievement Test

will increase. When the grade of Philosophy in

Quarter 2 rises, the mean of the National

Achievement Test normally falls. The mean of the

National Achievement Test will improve if the grade

in Philosophy in Quarter 1 improves.

Validation. The VLM (Very Low Mastery) and LM

(Low Mastery) anticipated values are shown in the

Confusion Matrix (Low Mastery). As indicated in

Table 6, 42.85% of the population received a National

Achievement Test rating of Very Low Mastery, with

a yield sensitivity of 96.33 percent and a fair kappa

rating of.852.

Table 6: Confusion Matrix.

Observed Y Predicted Y Accuracy

VLM True LM True

VLM True

42.85 0.26% 96.33%

LM False

7.15 49.74 87.12

Overall Accuracy

92.59%

5 CONCLUSION AND

RECOMMENDATION TO

FUTURE WORK

We discovered ten subjects that had a significant

finding on the National Achievement Test when

creating the model. Physical Science, Personal

Development, Culture, Society, and Politics, 21st

Century Literature in the Philippines and the World,

Philosophy, English for Academic and Professional

Purposes, Filipino, Media and Information Literacy,

Earth and Life Sciences, and Arts are among the

subjects covered.

We can deduce that not only the subjects differ,

but also the quarters in which the subject was taken

may have an impact on the National Achievement

Test score. Students' performance in Filipino at the

same grade level and from various quarters had an

unexpected impact on the National Achievement

Test. It can be argued that as a student's Filipino grade

rises, so does the mean of the National Achievement

Test. In comparison to Filipinos in Quarter 4,

Filipinos in Quarter 2 have a bigger impact.

The mean of the National Achievement Test

Scores tends to decrease as the value of the students'

performance grade in Physical Sciences, Personal

Development, Understanding Culture, Society, and

Politics, 21st Century Literature in the Philippines

and the World, Philosophy, English for Academic

Purposes, Filipino, and Media and Information

Literacy rises. Filipino subjects from quarters 3,2, and

4 have a stronger effect in the National Achievement

Test.

According to the algorithm, the only three

disciplines with the same quarter may have a

significant impact on the National Achievement Test

are Filipino, English for Academic Purposes, and

Philosophy. We can presume that quarterly grades or

academic achievement in the listed areas may have an

impact on the National Achievement Test outcome

because the coefficients for these topics vary each

quarter.

The researcher may suggest studying broader

datasets and including scores from other schools,

such as one division or region. Another study

including the use of several algorithms and

comparing the results of various algorithms could

yield positive results.

ACKNOWLEDGEMENTS

I'd like to thank Central Mindanao University for

providing funds for this research. I'd also like to thank

my colleagues from the College of Information

Sciences and Computing for all of their assistance. As

motivation to continue my studies, I dedicate this

letter to my family and friends. For guidance and

clear thinking for this research, I thank to the

Almighty God.

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