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         American Journal of Agricultural Science, Engineering and Technology 
 

 
 

 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 
http://journals.e-palli.org 

 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
 

 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 
http://journals.e-palli.org 

The American Journal of Agricultural Science, Engineering and Technology (AJASET) is 
blind peer reviewed international journal publishing articles that emphasize research, 
development and application within the fields of agricultural science, engineering and 
technology. The AJASET covers all areas of Agricultural Science, Engineering and 
Technology, publishing original research articles. The AJASET reviews article within 
approximately two weeks of submission and publishes accepted articles online immediately 
upon receiving the final versions.  

Published Media: ISSN: 2158-8104 (Online), 2164-0920 (Print).  

Frequency: 2 issues per year (January, July)  

Area of publication: Agricultural Science, Any Engineering and Technology related original 
and innovative works.  

EDITORIAL BOARD 

Chief Editor 

Dr Mamun-Or-Rashid 
Professor, Dhaka University, Bangladesh 

Board Members  

Dr. Sumit Garg, IL, USA 
Professor Dr. James J. Riley, The University of Arizona, USA 
Dr. Ekkehard KÜRSCHNER, Agriculture Development Consultant, Germany 
Professor Dr. Rodriguez Hilda, USA 
Professor Dr. Michael D. Whitt, USA 
Professor Dr. Wael Al-aghbari, Yemen 
Professor Dr. Muhammad Farhad Howladar, Bangladesh 
Dr. Clement Kiprotich Kiptum, University of Eldoret, Kenya 
Professor Dr M Shamim Kaiser, Professor, Jahangirnagar University, Bangladesh 
Professor Dr Mohammad Shahadat Hossain, Chittagong University, Bangladesh 
Professor Dr. Nirmal Chandra Roy, Sylhet Agricultural University, Bangladesh 

Managing Editor 

Md. Roshidul Hasan 
Professor, Department of Computer Science and Information Technology,  
Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh  

 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
207 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

http://journals.e-palli.org 

STRUGGLING LEARNERS' ACHIEVEMENT LEVEL USING ELECTRONIC 

STRATEGIC INTERVENTION MATERIAL IN MATHEMATICS (ESIMATH) 

Glory Fe Oraller Balazo1 

DOI: https://doi.org/10.54536/ajaset.v5i2.96 

 

ABSTRACT 

This study focused on the achievement levels of Grade 11 struggling learners using an 
electronic Strategic Intervention Material in Mathematics (eSIMath) in teaching least 
mastered competencies in the subject, Statistics and Probability for Senior High School. 
A Solomon four-group quasi-experimental research design was employed to two 
classes using eSIMath-based instruction, and two classes via conventional instruction, 
who were identified through a local diagnostic examination given by the teacher-
researcher before the instruction for the second semester of the school year 2019-2020. 
The data gathered were analyzed using independent and dependent t-test, Dunnett's T3 
test for post-hoc analysis of posttest scores, and assessment of mean gained scores. 
Findings of the study showed that the use of eSIMath-based instruction is an effective 
approach in the increase of struggling learners' achievement level in Statistics & 
Probability.  
 
 
Keywords: Achievement Level, Conventional Teaching, Electronic Strategic 
Intervention, Material, Pre-test, Posttest 

1 Graduate Student, Eastern Samar State University, Special Science Teacher 1, Dolores National 
High School 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
208 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

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INTRODUCTION 
Mathematics is challenging, rewarding, and fun. It is both logical and creative. It needs 
critical, analytical, problem - solving, and quantitative reasoning. Disciplines such as 
mathematics and science, unfortunately, create negative feedback for most high school 
students(Salviejo et al., 2014). Works of Lee, Grigg and Dion (2007)  showed that only a few 
students achieved the proficient level in Mathematics, and at least 33% did not reach even the 
basic level. In the year 2018, Filipino students participated in the Programme for 
International Students Assessment (PISA) and among the 79 participating countries, the 
Philippines ranked second last in the bottom in both mathematics and science (San Juan, 
2019). These results are associated with the performance of students in Dolores National 
High School; the mathematics mean percentage score, specifically in the course Statistics and 
Probability of Grade 11 students are still below 75%. These poor math results are indicators 
that learners of today struggles in understanding mathematical concepts and skills. With the 
pressing issues in the country's present educational system, Supovitz and  Klein (2003) 
opined the need for teachers to be more creative, flexible, and to provide aid to students' 
performance. According to Zakaria and Iksan (2007), the use of the latest approaches, 
methodologies, and strategies in teaching will encourage the learners to gain more knowledge 
and to apply the best practices in mathematics education. The use of technology inside the 
classroom helps the students become independent (Baylor & Ritchie, 2002; Windschitl & 
Sahl, 2002). The said approach   paves the way for an interactive and collaborative learning 
inside the four corners of the classroom. As presented by Himmelsbach (2019), technology 
integration enables students' engagement, maximization of self-paced learning, and promotes 
innovative teaching techniques. With this, the researcher adopted the idea of integrating 
technology in teaching least mastered competencies in the subject Statistics and Probability 
for Senior High School. The researcher's desire to help students develop their mathematical 
competence is aided through an Electronic Strategic Intervention Material (eSIM). Electronic 
Strategic Intervention Material is the newest remediation material prescribed by the 
Department of Education (DepEd). According to Dumigsi and Cabrella (2019), SIM is a 
useful remediation tool in solving problems, while Dahar (2011) found the use of SIM 
effective in improving learners' mathematics performance. This eSIMath based instruction 
utilized a researcher-developed learning tool embedded in a computer software that contains 
a title card, guide card, activity card, enrichment card, and reference card. The said 
instructional material focused on regression analysis as a consistent least learned competency 
based on the item analyses conducted for the school year 2017 – 2018 and 2018 – 2019 in 
Dolores National High School.  
Given all the aforementioned reasons involving a considerable number of learners who are 
experiencing difficulty in mastering learning and the critical role of technology in 
understanding complex mathematical concept, this study finds it significant to develop an 
electronic-based Strategic Intervention Material in Matheamatics (eSIMath) focusing on 
regression analysis which is included in the topic: Inferential Statistics, among 11th-grade 
students in Dolores National High School of the school year 2019-2020 via Four Group 
Solomon approach.  
 
 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
209 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

http://journals.e-palli.org 

METHODS 
Research design 
This is a quasi-experimental study via Solomon four group design that determined the 
achievement levels of struggling students using Electronic Strategic Intervention Material in 
Mathematics (eSIMath) among Grade 11 Statistics and Probability classes, specifically 
focusing on the least mastered competencies on Regression Analysis.  
 
Locale of the Study  
The experimentation was administered at Dolores National High School located at Reynaldo 
St. Barangay 09 Dolores, Eastern Samar, Philippines. The school was chosen because it is 
one of the pilot schools which implemented the Senior High School, has a big enrolment and 
due to the proximity of its location to the researcher. Results of the tests in Grade 11 Statistics 
and Probability were limited among four classes of 11th Grade learners enrolled in the 
Humanities and Social Sciences track for the school year 2019-2020. 
 
Respondents of the Study 
The Grade 11 Humanities and Social Sciences (HUMSS) struggling students were the 
subjects of this study. It is the teacher-researchers' observation that in Dolores National High 
School, at least 50% among HUMSS students are having difficulty in understanding the 
concepts in mathematics. So, with this, the researcher decided to have them as the subjects of 
this study. Four classes, with 30 subjects each, were involved in the experiment, two classes 
for the experimental group and two classes for the control group.  
 
Sampling Procedure 
The researcher employed non-random purposive sampling in selecting the subjects for the 
experimental and control group using a local diagnostic before the instruction for the 2nd 
semester as the basis in selecting its subjects.  
 
Research Instrument 
A 25-item pretest/posttest instrument was drafted by the teacher-researcher, and was content 
validated among Master Teacher, Senior High Coordinator, and expert teachers who have 
finished or have completed academic requirements for Master of Arts in Teaching 
Mathematics. The said instrument was then pilot tested to non-participating class, and the 
result yielded a 0.731 reliability coefficient through Cronbach's' alpha. An Electronic 
Strategic Intervention Material in Mathematics (eSIMath) made by the teacher-researcher 
was also used which undergone content validation, among mathematics teachers in order to 
determine the strong and weak points of the developed eSIMath. Comments and 
recommendations were considered in modifying the material.  
Data Analysis 
The data collected in this study were tabulated, analyzed and statistically treated using 
percentage achievement scores, dependent and dependent t-test, one-way analysis of 
variance, and dunnetts’ T3 test for multiple comparisons.  
 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
210 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

http://journals.e-palli.org 

RESULTS AND DISCUSSION 

Pretest achievement levels of the experimental group  and control group   
Table 1 presents the pretest achievement levels of the experimental group (EG1) and control 
group (CG1). The experimental group (EG1) got a percentage score of 24.52, while the 
control group (CG1) obtained 22.12. Their scores deviations' showed minimal difference, 
before their exposure to the instructions. These results were both interpreted as with "Low 
Achievement".  
This pattern of observation on pretest assessment result has been shown in previous works, 
specifically with Abuda (2019), in which participants assigned in control and experimental 
groups got low scores in the pretest assessment, and Jitendra and Xin (1997) who suggested 
that at-risk students first master basic numerical skills before engaging in more complex tasks 
such as problem-solving. The data signify that the subjects in both groups belong to 
struggling learners and poorly performing in class.  
 

Table 1. Pretest achievement levels of the experimental group  and control group 

Groups Percentage Score SD Achievement Level 
EG1 24.52 1.655 Low Achievement 
CG1 22.12 1.332 Low Achievement 

SD - Standard Deviation  
 
Posttest achievement level of the four groups 
Table 2 showcases the posttest achievement levels of the four groups namely: experimental 
group/EG1 (with pretest), experimental group/EG2 (without pretest), control group/CG1 (with 
pretest), and control group/CG2 (without pretest). The table shows that both the experimental 
group with and without pretest gained percentage scores of 60.40 and 55.08, respectively of 
which are interpreted as "Average achievement". On the other hand, the control group with 
pretest gained a mean score of 43.88 interpreted as "Average achievement", and the control 
group without pretest got the lowest achievement score of 36.28 interpreted as with "Average 
Achievement".  It can be observed that groups exposed to pretest assessment (EG1 and CG1) 
performed better than their counterparts, although all of the four groups performed in the 
same achievement level.  These findings are consistent with the available data of De Jesus 
(2019), that the use of SIM significantly increase the learners' performance on the least 
mastered topics, and Moore (2007) who pinpointed that learners who are into technology 
became more, engage and concentrated in learning the topics.  
 

Table 2. Posttest achievement levels of the experimental and control groups 
Groups Percentage Score SD Achievement Level 

EG1 60.40 2.264 Average Achievement 
EG2 55.08 4.376 Average Achievement 
CG1 43.88 3.624 Average Achievement 
CG2 36.28 3.352 Average Achievement 

SD - Standard Deviation  
 
Difference between the pretest achievement levels of the experimental and control 
groups 
Prior to analyzing the test on the significant difference of the pretest scores of the two group, 
normality test via Shapiro Wilk approach was conducted, and the results showed that the 
scores of the two groups followed normal distribution since p values from two groups were 
greater than 0.05. 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
211 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

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Results in Table 3 reveal that since the p-value is higher than the level of significance set at 
0.05, the researcher fails to reject the null hypothesis. Therefore, the pretest achievement 
scores of the control and experimental group showed a non-significant difference. This 
amplifies that the learning achievement of the subjects were homogenous as opined by 
Fajardo (2004). A similar achievement level resulted due to the pairing made by the 
researcher to assure that the two groups were made equal. 
 

Table 3. Test significant difference on pretest achievement levels of the experimental 
and control groups 

Groups t-value p-value Decision Interpretation 
EG1 1.547 .127 Fail to reject H0 Not Significant 
CG1 

df = 58, α = 0.5 
 
Difference between the pretest and posttest achievement levels of the experimental and 
control groups 
Table 4 presents the mean difference of scores between the achievement levels in the pretest 
and posttest mean scores of the experimental group (EG1) and control group (CG1), and the 
pretest sensitization of the experimental groups and control groups, respectively. 
It can be observed from the table that highest mean difference can be seen on the 
experimental group with pretest (EG1 = -8.97), while the lowest mean difference reflects on 
the control group without pretest (CG2 = -3.53). Since the control group without pretest 
describes pretest sensitization, it is safe to declare that the exposure to pretest has little impact 
on the posttest performance of the students.  Since all the computed p-values were less than 
the level of significance set at 0.05. The results suggest that there is a significant difference 
between the pretest and posttest mean scores of the experimental group and control group, 
and in both pretest mean score of control and experimental groups and posttest mean score of 
both experimental and control groups without pretest. This clearly suggests that using 
electronic Strategic Intervention Material in Mathematics (eSIMath) enhances the 
achievement level of struggling math learners in the 11th grade. It was also observed that 
there is a significant difference of achievement levels of students who were exposed to pretest 
and those who were not pretested. This general observation is also supported by findings 
from Zhang et al. (2015) on the improvement of student learning after exposing them to math 
apps.  
 
Table 4. Test significant difference of the pretest and posttest achievement levels of the 
experimental and control groups 
 

df =  29, α = 0.5 
 
Posttest achievement levels of the four groups 
Prior to the conduct of multiple post-hoc analysis, Shapiro Wilk-based normality test was 
conducted. The result revealed that all the four groups are normally distributed since their p-
values are greater than 0.05. Furthermore, in choosing what multiple comparison based-

Groups MD t-value p-value Decision Interpretation 
EG1 (Pretest-Posttest) -8.97 -19.139 .000 Reject H0 Significant 
EG1(Pretest)/ 
EG2(Posttest) 

-7.64 -7.785 .000 Reject H0 Significant 

CG1(Pretest-Posttest) -5.43 -8.649 .000 Reject H0 Significant 
CG1(Pretest)/ 
CG2(Posttest) 

-3.53 -5.041 .000 Reject H0 Significant 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
212 ISSN: 2158-8104 (Online), 2164-0920 (Print), 2021, Vol. 5, Issue.2 

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parametric test to be used, a Levene's analysis was generated which guided the researcher to 
use Dunnette's T3 test since homogeneity of variances cannot be assumed. Prior to post hoc 
analysis, a one-way ANOVA was calculated to determine whether the differences of scores 
of the four groups are statistically significant. Since the computed p-value is less than 0.05, 
there exists a significant difference in the posttest scores of the four groups. Navarro and 
Siegel (2018) stated that the effectiveness of the treatment can be evaluated by comparisons 
between experimental and control groups with pretest, and experimental and control groups 
without pretest. Table 5 shows that there is a significant difference between the experimental 
and control group with pretest and also, on the comparison between the experimental and 
control group without pretest. Hence, it can be assumed that the treatment used in the study 
was effective. Electronic Strategic Intervention Material in Mathematics (eSIMath) based 
instruction is way better compared to conventional teaching method. This is consistent with 
the findings reported by Abuda (2019) on the significant difference found on the posttest 
mastery levels of the comparison groups, and Ferguson (2010) who reported that inquiry-
based learning instruction showed a significant improvement in the posttest result rather than 
in conventional instruction in the Eighth Grade Pre-Algebra Classroom. 
 
Table 5. Dunnette T3 test significant difference of the posttest achievement levels of the 

four groups 

*df = 3, a = 0.5 
 
Mean gain scores of the experimental and the control group 
Prior to determining the significant difference of the mean gain scores of the experimental 
and control groups with pretest, a test of homogeneity of variances was used to determine the 
normality of distribution of the two groups. A Levene's statistic of 4.12 with a corresponding 
p-value of .047  were calculated, which means that equal variances of the two groups are not 
assumed, which is one requirement for t-test analysis. Furthermore, since both groups show 
normality in their distributions as depicted in the Shapiro Wilk Test of Normality, a 
parametric analysis such as an independent (unmatched) t-test can be used. 
As shown on Table 6 the mean gain score of the experimental group is higher compared to 
the control group. It has a mean difference of 3.54. Thus, technology really helps in engaging 
young minds and enhancing learners understanding (Carlson (2005). Furthermore, a t-value 
of 4.509 was computed with a corresponding p-value of approximately equal to 0. This 
means that, there is a statistically significant difference between the mean gain scores of the 
two groups.  
 

Table 6. Test significant difference of the mean gain scores of the experimental and 
control groups with pretest 

Groups MGS MD t-value p-value Decision Interpretation 
EG1 8.97 

3.54 4.509 .000 Reject H0 Significant 
CG1 5.43 

df = 53.637, MGS – Mean Gain Scores, MD - Mean Difference  
 
CONCLUSIONS 

Groups 
*F-value/Mean 

Difference 
p-value Decision Interpretation 

4 groups *18.253 0.000 Reject H0 Significant 
EG1 – CG1 4.13 .000 Reject H0 Significant 
EG2 – CG2 4.70 .000 Reject H0 Significant 



         American Journal of Agricultural Science, Engineering and Technology 
 

 
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Based on the findings of the present study, it is concluded that both the experimental and 
control groups got a low achievement mean score in the pretest assessment. After exposing 
these groups to the instructions, the experimental and control groups with pretest performed 
better in the posttest assessment, while the control group without pretest shown the least 
achievement. Further analysis revealed no significant difference observed between the pretest 
achievement level of the students in both experimental and control groups, while the pretest 
and posttest achievement levels of the experimental and control groups showed a significant 
difference. Finally, the achievement levels among the groups significantly improved in the 
posttest, and based on the ANOVA result all of the four groups significantly differ on their 
posttest mean scores. Having a significant result on the experimental and control groups with 
pretest, and experimental and control group without pretest we can say that Electronic 
Strategic intervention Material (eSIMath) based teaching is better compared to conventional 
teaching. There is a significant difference between the mean gain score of the experimental 
and control groups. Experimental group show a higher mean gain score compared to the 
control group.  
 
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